id,slug,type,content,published,createdAt,publishedAt,updatedAt,publicationContext,revisionId,breadcrumbs,markdown,title 1zzTqdQC1XVVfDbq9iZKDk09Re0y8nRgL7zMpZnul-zM,rotavirus-vaccine,article,"{""toc"": [{""slug"": ""rotavirus-is-the-leading-cause-of-childhood-diarrhea"", ""text"": ""Rotavirus is the leading cause of childhood diarrhea"", ""title"": ""Rotavirus is the leading cause of childhood diarrhea"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""rotavirus-vaccines"", ""text"": ""Rotavirus vaccines"", ""title"": ""Rotavirus vaccines"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""rotavirus-vaccine-could-save-the-lives-of-even-more-children"", ""text"": ""Rotavirus vaccine could save the lives of even more children"", ""title"": ""Rotavirus vaccine could save the lives of even more children"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""immunisation-rates-are-still-too-low"", ""text"": ""Immunisation rates are still too low"", ""title"": ""Immunisation rates are still too low"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""vaccine-efficacy-differs-by-region"", ""text"": ""Vaccine efficacy differs by region"", ""title"": ""Vaccine efficacy differs by region"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""other-high-impact-interventions"", ""text"": ""Other high-impact interventions"", ""title"": ""Other high-impact interventions"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Diarrhea is the third leading "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/what-are-children-dying-from-and-what-can-we-do-about-it"", ""children"": [{""text"": ""cause of child mortality"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – it claimed "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/what-are-children-dying-from-and-what-can-we-do-about-it"", ""children"": [{""text"": ""more than half a million"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" children in 2017. Rotavirus is the leading cause of diarrhea in children – it is estimated that between one-quarter and one-third of all child deaths from diarrhea are the result of rotavirus infection."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Countries which have introduced "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/vaccination"", ""children"": [{""text"": ""vaccines"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" against rotavirus have seen significant reductions in rotavirus-related cases of diarrhea. Improved coverage and better vaccine efficacy in low-income countries could save even more lives."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Rotavirus is the leading cause of childhood diarrhea"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Rotavirus is the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/childhood-diarrheal-diseases#what-causes-diarrheal-diseases"", ""children"": [{""text"": ""leading cause of diarrheal disease"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in children globally. It's estimated that between one-quarter and one-third of all child deaths from diarrhea are the result of rotavirus infection."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Global Burden of Disease"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" study estimated that rotavirus was the cause of 128,500 deaths and was responsible for an estimated 258 million cases of infectious diarrhea among children under-5 in 2016."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The map shows the global distribution of rotavirus-related child deaths. As we can see, the highest mortality rates occur in the Sub-Saharan region. In terms of total numbers, Nigeria, the Democratic Republic of Congo and India have the highest "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/child-deaths-from-rotavirus?tab=map"", ""children"": [{""text"": ""total number"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of rotavirus-related deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/grapher/share-of-children-with-a-weight-too-low-for-their-height-wasting"", ""children"": [{""text"": ""Childhood wasting"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", access to "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/population-using-at-least-basic-drinking-water"", ""children"": [{""text"": ""clean water"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-population-with-improved-sanitation-faciltities"", ""children"": [{""text"": ""unsafe sanitation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" are "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/number-of-deaths-from-diarrheal-diseases-by-risk-factor-for-under-5s"", ""children"": [{""text"": ""the major risk factors"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", associated with 84% of all deaths from diarrheal disease in children."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/child-mortality-rate-from-rotavirus"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While reducing these risk factors can help to avoid a large number of rotavirus cases, it is not sufficient to prevent all infections. However, we do have an effective intervention against the virus: the rotavirus vaccine."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Rotavirus vaccines"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The first widely-used rotavirus vaccine was approved in the United States in 2006. Today, there are four oral rotavirus vaccines recommended for use by the World Health Organisation (WHO): Rotarix, RotaTeq, RotaSiil, and Rotavac."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Rotarix and RotaTeq are the most widely used and both have shown good efficacy against rotavirus infections in clinical trials."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since the use of rotavirus vaccines have been approved, they have had a notable impact on the reduction of rotavirus-related deaths. According to a study published in 2018, the use of rotavirus vaccines prevented approximately 28,900 child deaths globally in 2016. However, as the chart shows, full vaccine use – that is a 100% coverage globally – could have prevented an additional 83,200 deaths."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This means that, even at the current rates of efficacy, 53% of all deaths in children under-5 from rotavirus in 2016 could have been avoided by full vaccine coverage."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In addition to saving lives, the rotavirus vaccine also reduces the burden on healthcare systems. Between 2008 and 2016 the introduction of the rotavirus vaccine has reduced the number of diarrhea-related hospital admissions on average by 40%."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/avertable-deaths-from-rotavirus-with-full-vaccine-coverage"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Rotavirus vaccine could save the lives of even more children"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If there is so much scope for saving more children’s lives, what is the reason that these children are still dying?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are two key barriers to achieving the full potential of the rotavirus vaccine: immunization rates, and the efficacy of the vaccine in specific regions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Immunisation rates are still too low"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""According to the WHO, by the end of 2018, 101 countries were using the rotavirus vaccine. The major drivers for the introduction of the vaccine are the burden of diarrheal diseases, the availability of funding, and a favourable political climate for vaccines."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The vaccine is only given to children – it’s recommended that the vaccination should be initiated 15 weeks after birth and finished by the 32nd week. However, the global coverage is still very low: it is estimated that just 35% of under one-year-olds were vaccinated in 2018."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The map shows the WHO estimates on the share of one-year olds who received the full recommended dosage of the vaccine (two immunizations for Rotarix vaccine or three immunizations for RotaTeq vaccine). For many countries where data coverage is low, it’s expected that the share of infants receiving the vaccine is very low. Some countries however did see rapid increases in rates of immunization. In a period of only a few years countries including "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-one-year-olds-who-received-the-rotavirus-vaccine?tab=chart&time=2011..2018&country=SDN+GMB+MWI"", ""children"": [{""text"": ""Sudan, Malawi and Gambia"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" have increased immunisation rates from below 10% to 80-95% – click on the country to see the change over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-one-year-olds-who-received-the-rotavirus-vaccine"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Vaccine efficacy differs by region"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since most "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/child-deaths-from-rotavirus"", ""children"": [{""text"": ""rotavirus cases"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" occur in Sub-Saharan Africa where "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/child-mortality-rate-from-rotavirus?tab=chart"", ""children"": [{""text"": ""mortality from rotavirus infection is also the highest"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", it is essential to increase and maintain high immunisation coverage in this region. However, in addition to delivering the vaccine for those who need it, we also need to work on improving its efficacy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Vaccine efficacy for the rotavirus vaccine is defined as the percentage reduction of the rate of diarrhea incidences in vaccinated versus unvaccinated groups of children. It is well established that the efficacy of the rotavirus vaccine is not the same across all countries — in countries with high child mortality rates the vaccine shows much lower efficacy."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""left"": [{""type"": ""text"", ""value"": [{""text"": ""The chart is from a recent study by Clark et "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""al."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", which looked at how the efficacy of live oral rotavirus vaccines changes in different countries following vaccination. The chart shows that in countries with high child mortality rates, not only is the immediate vaccine efficacy lower – 98% in low child mortality countries versus 66% in high child mortality countries – but also the vaccine efficacy decreases faster in high child mortality countries over time."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Five years after vaccination, the rotavirus vaccine reduces the chances of getting diarrhea by 90% in low child mortality countries and only by 30% in high mortality countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The table shows how good the rotavirus vaccine is at preventing severe diarrhea and reducing hospitalization due to diarrhea in children under-5 in different regions."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In high-income countries, rotavirus vaccination has been shown to reduce the cases of severe rotavirus diarrhea by 91% and hospitalization by 94%. In Eastern Asia and Latin America, the effectiveness rates are lower but still high – preventing 88% and 80% of severe diarrhea cases, respectively. However, effectiveness in South Asia and Sub-Saharan Africa is significantly lower, only reducing severe diarrhea in around half of the cases."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The reasons for different responses to the vaccine are not entirely clear."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" "", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is likely that the gut responses to the oral rotavirus vaccines in children in lower-income countries are different. This may be due to a variety of causes, including "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/micronutrient-deficiency"", ""children"": [{""text"": ""micronutrient deficiencies"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", pre-vaccination exposure to certain pathogens, and the presence of chronic conditions such as "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/malaria"", ""children"": [{""text"": ""malaria"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" or "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/hiv-aids"", ""children"": [{""text"": ""HIV"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Overall, the poor gut response to the live vaccine means the efficacy of the vaccine is reduced. Taking all of the above mentioned points into account, there are several interventions that could increase the benefits of the rotavirus vaccine even further. In addition to increasing the vaccine coverage, improving nutritional health (of both infants and mothers) and improving hygiene and sanitation conditions (to lower the prevalence of damaging pathogens) could have positive effects on the vaccine’s efficacy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""side-by-side"", ""right"": [{""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Rotavirus-vaccine-efficacy-by-child-mortality-group-1.png"", ""parseErrors"": []}, {""rows"": [{""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Outcome"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Region"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}, {""type"": 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[]}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": []}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Sub-Saharan Africa"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""46%"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Hospitalization due to rotavirus infection"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Developed"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""94%"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": []}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Eastern Asia and Southeast Asia"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""94%"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": []}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Latin America and Caribbean"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""84%"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": []}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Sub-Saharan Africa"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""58%"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}], ""size"": ""narrow"", ""type"": ""table"", ""template"": ""header-row"", ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We are still at quite an early stage of the rotavirus vaccine use. Although the vaccine has brought huge benefits already, it could go even further. Improving vaccination coverage, particularly across Sub-Saharan Africa and South Asia is key to continued reduction of childhood deaths from diarrhea. Even at moderate levels of vaccine efficacy, a significant number of additional additional child deaths could be prevented every year. The bar chart above that shows the number of preventable deaths illustrates the potential for extended vaccine coverage to save many more lives. And this is already taking into account the regional differences in the vaccine’s effectiveness."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In addition to increased coverage, improving the effectiveness of the vaccine would go even further in tackling one of the leading causes of death."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Other high-impact interventions"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While rotavirus is the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/childhood-diarrheal-diseases#what-causes-diarrheal-diseases"", ""children"": [{""text"": ""leading cause of diarrhea in children"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and therefore, rotavirus vaccine can save the lives of many children, it is not the only cause of diarrhea. 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"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""The Journal of Infectious Diseases"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""200"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", S39-S48."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Rotavirus vaccine - an effective tool that prevents children dying from diarrhea"", ""authors"": [""Bernadeta Dadonaite"", ""Hannah Ritchie""], ""excerpt"": ""Rotavirus is the leading cause of diarrheal deaths in children. There is, however, an effective tool against it: the rotavirus vaccine."", ""dateline"": ""August 22, 2019"", ""subtitle"": ""Rotavirus is the leading cause of diarrheal deaths in children. There is, however, an effective tool against it: the rotavirus vaccine."", ""sidebar-toc"": false, ""featured-image"": ""avertable-deaths-from-rotavirus-with-full-vaccine-coverage-2.png""}",1,2023-12-28 10:54:36,2019-08-22 04:58:27,2024-03-18 15:41:59,listed,ALBJ4LsMaUchbvSkQHfdKgkh8AhGIZnidxjI26dTPD72vTvq36XLy_o-wHvxc1sYLzPJvvJqZ6zCrjwm4y94AA,,"Diarrhea is the third leading [cause of child mortality](https://ourworldindata.org/what-are-children-dying-from-and-what-can-we-do-about-it) – it claimed [more than half a million](https://ourworldindata.org/what-are-children-dying-from-and-what-can-we-do-about-it) children in 2017. Rotavirus is the leading cause of diarrhea in children – it is estimated that between one-quarter and one-third of all child deaths from diarrhea are the result of rotavirus infection. Countries which have introduced [vaccines](https://ourworldindata.org/vaccination) against rotavirus have seen significant reductions in rotavirus-related cases of diarrhea. Improved coverage and better vaccine efficacy in low-income countries could save even more lives. ## Rotavirus is the leading cause of childhood diarrhea Rotavirus is the [leading cause of diarrheal disease](https://ourworldindata.org/childhood-diarrheal-diseases#what-causes-diarrheal-diseases) in children globally. It's estimated that between one-quarter and one-third of all child deaths from diarrhea are the result of rotavirus infection.1 The _Global Burden of Disease_ study estimated that rotavirus was the cause of 128,500 deaths and was responsible for an estimated 258 million cases of infectious diarrhea among children under-5 in 2016.2 The map shows the global distribution of rotavirus-related child deaths. As we can see, the highest mortality rates occur in the Sub-Saharan region. In terms of total numbers, Nigeria, the Democratic Republic of Congo and India have the highest [total number](https://ourworldindata.org/grapher/child-deaths-from-rotavirus?tab=map) of rotavirus-related deaths. [Childhood wasting](https://ourworldindata.org/grapher/share-of-children-with-a-weight-too-low-for-their-height-wasting), access to [clean water](https://ourworldindata.org/grapher/population-using-at-least-basic-drinking-water) and [unsafe sanitation](https://ourworldindata.org/grapher/share-of-population-with-improved-sanitation-faciltities) are [the major risk factors](https://ourworldindata.org/grapher/number-of-deaths-from-diarrheal-diseases-by-risk-factor-for-under-5s), associated with 84% of all deaths from diarrheal disease in children.2 While reducing these risk factors can help to avoid a large number of rotavirus cases, it is not sufficient to prevent all infections. However, we do have an effective intervention against the virus: the rotavirus vaccine. ## Rotavirus vaccines The first widely-used rotavirus vaccine was approved in the United States in 2006. Today, there are four oral rotavirus vaccines recommended for use by the World Health Organisation (WHO): Rotarix, RotaTeq, RotaSiil, and Rotavac.3 Rotarix and RotaTeq are the most widely used and both have shown good efficacy against rotavirus infections in clinical trials.45 Since the use of rotavirus vaccines have been approved, they have had a notable impact on the reduction of rotavirus-related deaths. According to a study published in 2018, the use of rotavirus vaccines prevented approximately 28,900 child deaths globally in 2016. However, as the chart shows, full vaccine use – that is a 100% coverage globally – could have prevented an additional 83,200 deaths.6 This means that, even at the current rates of efficacy, 53% of all deaths in children under-5 from rotavirus in 2016 could have been avoided by full vaccine coverage. In addition to saving lives, the rotavirus vaccine also reduces the burden on healthcare systems. Between 2008 and 2016 the introduction of the rotavirus vaccine has reduced the number of diarrhea-related hospital admissions on average by 40%.7 ### Rotavirus vaccine could save the lives of even more children If there is so much scope for saving more children’s lives, what is the reason that these children are still dying? There are two key barriers to achieving the full potential of the rotavirus vaccine: immunization rates, and the efficacy of the vaccine in specific regions. ### Immunisation rates are still too low According to the WHO, by the end of 2018, 101 countries were using the rotavirus vaccine. The major drivers for the introduction of the vaccine are the burden of diarrheal diseases, the availability of funding, and a favourable political climate for vaccines.8 The vaccine is only given to children – it’s recommended that the vaccination should be initiated 15 weeks after birth and finished by the 32nd week. However, the global coverage is still very low: it is estimated that just 35% of under one-year-olds were vaccinated in 2018.9 The map shows the WHO estimates on the share of one-year olds who received the full recommended dosage of the vaccine (two immunizations for Rotarix vaccine or three immunizations for RotaTeq vaccine). For many countries where data coverage is low, it’s expected that the share of infants receiving the vaccine is very low. Some countries however did see rapid increases in rates of immunization. In a period of only a few years countries including [Sudan, Malawi and Gambia](https://ourworldindata.org/grapher/share-of-one-year-olds-who-received-the-rotavirus-vaccine?tab=chart&time=2011..2018&country=SDN+GMB+MWI) have increased immunisation rates from below 10% to 80-95% – click on the country to see the change over time. ### Vaccine efficacy differs by region Since most [rotavirus cases](https://ourworldindata.org/grapher/child-deaths-from-rotavirus) occur in Sub-Saharan Africa where [mortality from rotavirus infection is also the highest](https://ourworldindata.org/grapher/child-mortality-rate-from-rotavirus?tab=chart), it is essential to increase and maintain high immunisation coverage in this region. However, in addition to delivering the vaccine for those who need it, we also need to work on improving its efficacy. Vaccine efficacy for the rotavirus vaccine is defined as the percentage reduction of the rate of diarrhea incidences in vaccinated versus unvaccinated groups of children. It is well established that the efficacy of the rotavirus vaccine is not the same across all countries — in countries with high child mortality rates the vaccine shows much lower efficacy.10 The chart is from a recent study by Clark et _al._, which looked at how the efficacy of live oral rotavirus vaccines changes in different countries following vaccination. The chart shows that in countries with high child mortality rates, not only is the immediate vaccine efficacy lower – 98% in low child mortality countries versus 66% in high child mortality countries – but also the vaccine efficacy decreases faster in high child mortality countries over time.11 Five years after vaccination, the rotavirus vaccine reduces the chances of getting diarrhea by 90% in low child mortality countries and only by 30% in high mortality countries. The table shows how good the rotavirus vaccine is at preventing severe diarrhea and reducing hospitalization due to diarrhea in children under-5 in different regions.12 In high-income countries, rotavirus vaccination has been shown to reduce the cases of severe rotavirus diarrhea by 91% and hospitalization by 94%. In Eastern Asia and Latin America, the effectiveness rates are lower but still high – preventing 88% and 80% of severe diarrhea cases, respectively. However, effectiveness in South Asia and Sub-Saharan Africa is significantly lower, only reducing severe diarrhea in around half of the cases. The reasons for different responses to the vaccine are not entirely clear.13 14 It is likely that the gut responses to the oral rotavirus vaccines in children in lower-income countries are different. This may be due to a variety of causes, including [micronutrient deficiencies](https://ourworldindata.org/micronutrient-deficiency), pre-vaccination exposure to certain pathogens, and the presence of chronic conditions such as [malaria](https://ourworldindata.org/malaria) or [HIV](https://ourworldindata.org/hiv-aids). Overall, the poor gut response to the live vaccine means the efficacy of the vaccine is reduced. Taking all of the above mentioned points into account, there are several interventions that could increase the benefits of the rotavirus vaccine even further. In addition to increasing the vaccine coverage, improving nutritional health (of both infants and mothers) and improving hygiene and sanitation conditions (to lower the prevalence of damaging pathogens) could have positive effects on the vaccine’s efficacy. |**Outcome**|**Region**|**Vaccine effectivness**| |Severe rotavirus diarrhea|Developed|91%| ||Eastern Asia and Southeast Asia|88%| ||Latin America and Caribbean|80%| ||Southern Asia|50%| ||Sub-Saharan Africa|46%| |Hospitalization due to rotavirus infection|Developed|94%| ||Eastern Asia and Southeast Asia|94%| ||Latin America and Caribbean|84%| ||Sub-Saharan Africa|58%| We are still at quite an early stage of the rotavirus vaccine use. Although the vaccine has brought huge benefits already, it could go even further. Improving vaccination coverage, particularly across Sub-Saharan Africa and South Asia is key to continued reduction of childhood deaths from diarrhea. Even at moderate levels of vaccine efficacy, a significant number of additional additional child deaths could be prevented every year. The bar chart above that shows the number of preventable deaths illustrates the potential for extended vaccine coverage to save many more lives. And this is already taking into account the regional differences in the vaccine’s effectiveness. In addition to increased coverage, improving the effectiveness of the vaccine would go even further in tackling one of the leading causes of death. ## Other high-impact interventions While rotavirus is the [leading cause of diarrhea in children](https://ourworldindata.org/childhood-diarrheal-diseases#what-causes-diarrheal-diseases) and therefore, rotavirus vaccine can save the lives of many children, it is not the only cause of diarrhea. To prevent deaths from other diarrheal diarrheal pathogens we need additional interventions and treatments. Deaths from other diarrheal pathogens, such as cholera or shigella, could be avoided by [antibiotics or vaccines](https://ourworldindata.org/childhood-diarrheal-diseases#how-can-we-stop-child-deaths-from-diarrhea), where they are available. But preventions that limit the risks of exposure to diarrheal pathogens in the first place are the key to saving the most lives. [Unsafe water and unsafe sanitation](https://ourworldindata.org/water-use-sanitation) contributed to 72% and 56% of the under-5 deaths from diarrheal diseases in 2016. Children who are [undernourished](https://ourworldindata.org/hunger-and-undernourishment), are less likely to survive a diarrheal episode. Consequently, [childhood wasting](https://ourworldindata.org/hunger-and-undernourishment#too-little-weight-for-height-wasting) (having a weight too low for their height) is the leading risk factor for diarrheal mortality in children, contributing to an estimated 80% of under-5 deaths from diarrheal diseases in 2016.15 Clearly, eliminating [risk factors](https://ourworldindata.org/childhood-diarrheal-diseases#why-are-children-still-dying-from-diarrhea) associated with diarrheal diseases should be on our priority list. One of the most cost-effective ways to treat most cases of diarrhea, including those due to rotavirus infection, is not a highly sophisticated drug or vaccine but a simple water, salt and sugar solution: oral rehydration therapy (ORT). ORT can be used to prevent deaths from diarrhea originating from any cause, and has already saved the lives of tens of millions of children since its introduction in the 1970s. A study by [Clark et al.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5593200/) (2017) investigated the comparative estimates from the World Health Organization (WHO/CDC), Institute for Health Metrics and Evaluation (IHME), and the Child Health Epidemiology Reference Group (CHERG) of the contribution of rotavirus. The IHME estimates 24% of diarrheal deaths resulted from the rotavirus in 2013; 27% by the CHERG; and 37% from the WHO. Clark, A., Black, R., Tate, J., Roose, A., Kotloff, K., Lam, D., … & Troeger, C. (2017). [Estimating global, regional and national rotavirus deaths in children aged< 5 years: current approaches, new analyses and proposed improvements](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5593200/). _PloS one_, _12_(9), e0183392. Troeger, C., Blacker, B. F., Khalil, I. A., Rao, P. C., Cao, S., Zimsen, S. R., … & Alvis-Guzman, N. (2018). [Estimates of the global, regional, and national morbidity, mortality, and aetiologies of diarrhoea in 195 countries: a systematic analysis for the Global Burden of Disease Study 2016](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(18)30362-1/fulltext#figures). _The Lancet Infectious Diseases_, _18_(11), 1211-1228. World Health Organization. (2019). _Rotavirus_. [online] Available at: [https://www.who.int/immunization/diseases/rotavirus/en/](https://www.who.int/immunization/diseases/rotavirus/en/) [Accessed 14 Aug. 2019]. Ruiz-Palacios, G. M., Pérez-Schael, I., Velázquez, F. R., Abate, H., Breuer, T., Clemens, S. C., … & Cervantes, Y. (2006). [Safety and efficacy of an attenuated vaccine against severe rotavirus gastroenteritis](https://www.nejm.org/doi/full/10.1056/nejmoa052434). _New England Journal of Medicine_, _354_(1), 11-22. Vesikari, T., Matson, D. O., Dennehy, P., Van Damme, P., Santosham, M., Rodriguez, Z., … & Shinefield, H. R. (2006). [Safety and efficacy of a pentavalent human–bovine (WC3) reassortant rotavirus vaccine](https://www.nejm.org/doi/full/10.1056/nejmoa052664). _New England Journal of Medicine_, _354_(1), 23-33. Troeger, C., Khalil, I. A., Rao, P. C., Cao, S., Blacker, B. F., Ahmed, T., … & Kang, G. (2018). [Rotavirus vaccination and the global burden of rotavirus diarrhea among children younger than 5 years](https://jamanetwork.com/journals/jamapediatrics/fullarticle/2696431). _JAMA Pediatrics_, _172_(10), 958-965. Aliabadi, Negar, et al. [""Global impact of rotavirus vaccine introduction on rotavirus hospitalisations among children under 5 years of age, 2008–16: findings from the Global Rotavirus Surveillance Network.""](https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(19)30207-4/fulltext)_The Lancet Global Health_7.7 (2019): e893-e903. The cost of rotavirus vaccines varies by country. For example, countries with a gross national income per capita of less than US$1,000 receive subsidies from the Global Alliance for Vaccines and Immunisation, and there the cost per dose is [set between US$2.10 - 3.20](https://www.unicef.org/supply/files/Rotavaccine.pdf). With additional co-financing options can reduce the costs [down to US$0.13](https://www.sabin.org/sites/sabin.org/files/frederic_debellut.pdf). In high-income countries, such as the US, the cost per dose is between [US$70 and 95](https://www.cdc.gov/vaccines/programs/vfc/awardees/vaccine-management/price-list/index.html) Countries are more likely to introduce the vaccine if the political environment is favourably disposed towards them. For example, if a country has set a high priority on achieving the Millennium Development Goal targets or the introduction of vaccines is seen as a positive news story, especially during election years. Burchett, H. E. D., Mounier-Jack, S., Griffiths, U. K., Biellik, R., Ongolo-Zogo, P., Chavez, E., … & Molla, M. (2012). [New vaccine adoption: qualitative study of national decision-making processes in seven low-and middle-income countries](https://academic.oup.com/heapol/article/27/suppl_2/ii5/594662). _Health policy and planning_, _27_(suppl_2), ii5-ii16. World Health Organization (2019). _Immunization coverage_. [online] Available at: [https://www.who.int/news-room/fact-sheets/detail/immunization-coverage](https://www.who.int/news-room/fact-sheets/detail/immunization-coverage) [Accessed 14 Aug. 2019]. Clark, Andrew, et al. [""Efficacy of live oral rotavirus vaccines by duration of follow-up: a meta-regression of randomised controlled trials.""](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(19)30126-4/fulltext)_The Lancet Infectious Diseases_ (2019). The [Clark et al.](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(19)30126-4/fulltext) (2019) study defined low child mortality rate as less than 1.3% of newborns; medium mortality rate as between 1.35% and 2.81%; and high mortality rate as more than 2.81% Lamberti, L. M., Ashraf, S., Walker, C. L. F., & Black, R. E. (2016). [A systematic review of the effect of rotavirus vaccination on diarrhea outcomes among children younger than 5 years](https://www.ingentaconnect.com/content/wk/inf/2016/00000035/00000009/art00016). _The Pediatric Infectious Disease Journal_, _35_(9), 992-998. Patel, M., Shane, A. L., Parashar, U. D., Jiang, B., Gentsch, J. R., & Glass, R. I. (2009). [Oral rotavirus vaccines: how well will they work where they are needed most?](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3673012/#R3). _The Journal of Infectious Diseases_, _200_, S39-S48. Parker, E. P., Ramani, S., Lopman, B. A., Church, J. A., Iturriza-Gomara, M., Prendergast, A. J., & Grassly, N. C. (2018). [Causes of impaired oral vaccine efficacy in developing countries](https://www.futuremedicine.com/doi/10.2217/fmb-2017-0128?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dwww.ncbi.nlm.nih.gov&). _Future microbiology_, _13_(1), 97-118. Because the cause of death may be influenced by multiple risk factors, the attributable fraction of deaths caused by individual risk factors may overlap and therefore, add up to more than 100%. Troeger, C., Blacker, B. F., Khalil, I. A., Rao, P. C., Cao, S., Zimsen, S. R., … & Alvis-Guzman, N. (2018). [Estimates of the global, regional, and national morbidity, mortality, and aetiologies of diarrhoea in 195 countries: a systematic analysis for the Global Burden of Disease Study 2016](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(18)30362-1/fulltext). _The Lancet Infectious Diseases_, _18_(11), 1211-1228.",Rotavirus vaccine - an effective tool that prevents children dying from diarrhea 1ztTAx024yw9uhm-grj8I46PaUCwjO0jdiW-UjS1JSLk,environmental-impact-milks,article,"{""toc"": [{""slug"": ""how-does-the-nutritional-profile-of-dairy-compare-with-plant-based-milks"", ""text"": ""How does the nutritional profile of dairy compare with plant-based milks?"", ""title"": ""How does the nutritional profile of dairy compare with plant-based milks?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""i-ve-heard-that-soy-is-linked-to-deforestation-in-the-amazon-is-this-a-concern-for-soy-milk"", ""text"": ""I’ve heard that soy is linked to deforestation in the Amazon. Is this a concern for soy milk?"", ""title"": ""I’ve heard that soy is linked to deforestation in the Amazon. Is this a concern for soy milk?"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""left"": [{""type"": ""text"", ""value"": [{""text"": ""Milk is a dietary staple across many countries in the world. But dairy can contribute a lot to the greenhouse gas emissions of our food. In typical EU diets, it accounts for just over one-quarter of the carbon footprint, sometimes as much as one-third."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Growing awareness of this means many are looking to plant-based alternatives. In the UK, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.mintel.com/press-centre/food-and-drink/milking-the-vegan-trend-a-quarter-23-of-brits-use-plant-based-milk"", ""children"": [{""text"": ""surveys suggest"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" one-quarter of adults now drink some non-dairy milks (although not always exclusively). It’s even more popular in younger demographics with one-third of 16 to 23-year-olds opting for them."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There is now a range of ‘plant-based’ milk alternatives available, including soy, oat, almond, rice, and coconut. This raises two common questions: are plant-based milks really better for the environment, and which is best?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart here we compare milks across a number of environmental metrics: land use, greenhouse gas emissions, water use, and eutrophication – the pollution of ecosystems with excess nutrients. These are compared per liter of milk."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" At the end of this article I address some of the differences in the nutritional quality of these milks, which is important to consider in certain populations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Cow’s milk has significantly higher impacts than the plant-based alternatives across all metrics. It causes around three times as much greenhouse gas emissions; uses around ten times as much land; two to twenty times as much freshwater; and creates much higher levels of eutrophication."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you want to reduce the environmental footprint of your diet, switching to plant-based alternatives is a good option."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Which of the vegan milks is best? It really depends on the impact we care most about. Almond milk has lower greenhouse gas emissions and uses less land than soy, for example, but requires more water and results in higher eutrophication."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All of the alternatives have a lower impact than dairy, but there is no clear winner on all metrics."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/environmental-footprint-milks"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Common FAQs on this topic:"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""How does the nutritional profile of dairy compare with plant-based milks?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the figures above we look at the comparison of milks "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""per liter"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". A liter of dairy milk is not comparable to a liter of plant-based milk in terms of its nutritional profile."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Dairy milk tends to be higher in calories, and importantly, contains more protein. 100ml of cow’s milk "", ""spanType"": ""span-simple-text""}, {""url"": ""https://fdc.nal.usda.gov/fdc-app.html#/food-details/1097517/nutrients"", ""children"": [{""text"": ""will contain"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" around 3.4 grams of protein, versus 0.5 grams "", ""spanType"": ""span-simple-text""}, {""url"": ""https://fdc.nal.usda.gov/fdc-app.html#/food-details/1999631/nutrients"", ""children"": [{""text"": ""in almond milk"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The protein in dairy is also a more ‘complete’ protein source, which means it has the full profile of essential amino acids."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most plant-based milks have a similar calcium content to cow’s milk – almond and cow’s milk both have around 120 milligrams per 100ml, for example."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One of the benefits of plant-based milks is that they are often fortified with vitamins and minerals. Vitamin D, for example, is often added. Cow’s milk naturally contains very little vitamin D, although it is possible to buy fortified varieties. Vitamin B"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" is one micronutrient that only occurs in animal products; vegans are therefore at risk of deficiency without supplementation. However, most plant-based milks are now fortified with vitamin B"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""From a nutritional perspective, the replacement of dairy with plant-based milks is unlikely to be a concern for those with a diverse diet, and for those who do not rely on milk as an important source of protein. It is possible to meet these requirements from other foods – such as a combination of legumes, meat substitutes, and grains. However, for certain demographics – especially young children, and those on lower incomes with poor dietary diversity – this might be an inappropriate switch."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The average person in many lower-income countries gets most of their calories from cheap, energy-dense crops like cereals and tubers (like cassava). This can be "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-energy-from-cereals-roots-and-tubers-vs-gdp-per-capita"", ""children"": [{""text"": ""more than three-quarters"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of an individual’s calorie intake. These diets do not provide the diversity of nutrients needed for good health – they are likely to be deficient in a number of "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/micronutrient-deficiency"", ""children"": [{""text"": ""micronutrients"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and protein (once we adjust for quality). Without access to foods that are fortified with vitamins and minerals, often small amounts of animal protein – such as milk – provide one of the few sources of complete protein and micronutrients in their diet."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In such cases, removing dairy from a person’s diet without sufficient replacements could have a negative impact on health and nutrition. For most people in middle-to–high income countries, however, this is unlikely to be an issue."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""I’ve heard that soy is linked to deforestation in the Amazon. Is this a concern for soy milk?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One of the largest concerns about alternatives such as soy milk is that they drive deforestation in the Amazon region. It’s true that the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/soybean-production?tab=chart&country=~OWID_WRL"", ""children"": [{""text"": ""growing demand for soy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" has been "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/soy#is-soy-production-driving-deforestation"", ""children"": [{""text"": ""one of the drivers"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of Brazilian land-use change. Although, by far, the largest driver has been pasture for beef production."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But, it’s important to note what this soy is used for. 95% of Brazilian soy is used for animal feed."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Globally, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/soy#more-than-three-quarters-of-global-soy-is-fed-to-animals"", ""children"": [{""text"": ""more than three-quarters"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of soy, by mass, is used for animal feed. The other main co-product is soybean oil. This means that very little of Amazonian land-use pressures from soy have been driven by crops for direct human consumption; most is for animal feed."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another factor to consider here, especially for European consumers, is that "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.reuters.com/article/us-brazil-gmo/brazil-boasts-worlds-second-largest-genetically-modified-crop-area-isaaa-idUSKBN1JN1KW"", ""children"": [{""text"": ""most of Brazil’s soy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" crop is genetically modified (some estimates put this figure at 94%). There are strict regulations on the use of GM soy for direct human food in the European Union. Most of the soy consumed in Europe is produced in Europe."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/grapher/soybean-yields"", ""children"": [{""text"": ""Soy yields"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" are similar in the EU as they are in Brazil and the rest of South America – 3 tonnes per hectare in France versus 2.9 tonnes in Brazil. In fact, some EU countries have higher yields, such as Spain (3.3 tonnes) and Italy (4 tonnes). 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"", ""spanType"": ""span-simple-text""}, {""url"": ""https://pubmed.ncbi.nlm.nih.gov/8172124/"", ""children"": [{""text"": ""Plant proteins in relation to human protein and amino acid nutrition"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The American Journal of Clinical Nutrition, 59(5), 1203S-1212S."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""5063a00979ad86000ba4459609101485e159cc93"": {""id"": ""5063a00979ad86000ba4459609101485e159cc93"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This data comes from the largest meta-analysis of food impacts to date, published by Joseph Poore and Thomas Nemecek (2018) in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Science. "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""In this study, the authors looked at data across more than 38,000 commercial farms in 119 countries and quantified their environmental impacts taking into account the entire production chain – from land-use change through to retail and packaging."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Poore, J., & Nemecek, T. (2018). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://science.sciencemag.org/content/360/6392/987"", ""children"": [{""text"": ""Reducing food’s environmental impacts through producers and consumers"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Science, 360(6392), 987-992."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""81af7797328df0fa080cc39d8fa026627c7bbbcc"": {""id"": ""81af7797328df0fa080cc39d8fa026627c7bbbcc"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Despite a large share of the population saying they now drink plant-based alternatives, dairy milk still dominates the UK market in terms of sales volume (with 96% for white milk). Market surveys suggest people favor cow’s milk versus vegan milks for particular uses e.g. hot versus cold drinks."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""8ad8aa42264beb5aaf4ab4d9e82eb4eafcd7aa2b"": {""id"": ""8ad8aa42264beb5aaf4ab4d9e82eb4eafcd7aa2b"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Poore, J., & Nemecek, T. (2018). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://science.sciencemag.org/content/360/6392/987"", ""children"": [{""text"": ""Reducing food’s environmental impacts through producers and consumers"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Science, 360(6392), 987-992."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""FAOstat: UN Food and Agriculture Organization (FAO) Statistics. Available at: http://www.fao.org/faostat/en/#data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Dairy vs. plant-based milk: what are the environmental impacts?"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""A growing number of people are interested in switching to plant-based alternatives to dairy. But are they better for the environment, and which is best?"", ""dateline"": ""January 19, 2022"", ""subtitle"": ""A growing number of people are interested in switching to plant-based alternatives to dairy. But are they better for the environment, and which is best?"", ""sidebar-toc"": false, ""featured-image"": ""environment-milks-thumbnail.png""}",1,2024-03-01 20:22:39,2022-01-19 08:10:35,2024-03-01 20:28:30,listed,ALBJ4LuHveOoIOzoBnZvQqphJbsrq337u3WDcrAnLD2lGF3bo8-z8Dd6Z_TatIycKiNQVVkegU07OZSADOCJ-g,,"Milk is a dietary staple across many countries in the world. But dairy can contribute a lot to the greenhouse gas emissions of our food. In typical EU diets, it accounts for just over one-quarter of the carbon footprint, sometimes as much as one-third.1 Growing awareness of this means many are looking to plant-based alternatives. In the UK, [surveys suggest](https://www.mintel.com/press-centre/food-and-drink/milking-the-vegan-trend-a-quarter-23-of-brits-use-plant-based-milk) one-quarter of adults now drink some non-dairy milks (although not always exclusively). It’s even more popular in younger demographics with one-third of 16 to 23-year-olds opting for them.2 There is now a range of ‘plant-based’ milk alternatives available, including soy, oat, almond, rice, and coconut. This raises two common questions: are plant-based milks really better for the environment, and which is best? In the chart here we compare milks across a number of environmental metrics: land use, greenhouse gas emissions, water use, and eutrophication – the pollution of ecosystems with excess nutrients. These are compared per liter of milk.3 At the end of this article I address some of the differences in the nutritional quality of these milks, which is important to consider in certain populations. Cow’s milk has significantly higher impacts than the plant-based alternatives across all metrics. It causes around three times as much greenhouse gas emissions; uses around ten times as much land; two to twenty times as much freshwater; and creates much higher levels of eutrophication. If you want to reduce the environmental footprint of your diet, switching to plant-based alternatives is a good option. Which of the vegan milks is best? It really depends on the impact we care most about. Almond milk has lower greenhouse gas emissions and uses less land than soy, for example, but requires more water and results in higher eutrophication. All of the alternatives have a lower impact than dairy, but there is no clear winner on all metrics. # Common FAQs on this topic: ## How does the nutritional profile of dairy compare with plant-based milks? In the figures above we look at the comparison of milks _per liter_. A liter of dairy milk is not comparable to a liter of plant-based milk in terms of its nutritional profile. Dairy milk tends to be higher in calories, and importantly, contains more protein. 100ml of cow’s milk [will contain](https://fdc.nal.usda.gov/fdc-app.html#/food-details/1097517/nutrients) around 3.4 grams of protein, versus 0.5 grams [in almond milk](https://fdc.nal.usda.gov/fdc-app.html#/food-details/1999631/nutrients). The protein in dairy is also a more ‘complete’ protein source, which means it has the full profile of essential amino acids.4 Most plant-based milks have a similar calcium content to cow’s milk – almond and cow’s milk both have around 120 milligrams per 100ml, for example. One of the benefits of plant-based milks is that they are often fortified with vitamins and minerals. Vitamin D, for example, is often added. Cow’s milk naturally contains very little vitamin D, although it is possible to buy fortified varieties. Vitamin B12 is one micronutrient that only occurs in animal products; vegans are therefore at risk of deficiency without supplementation. However, most plant-based milks are now fortified with vitamin B12. From a nutritional perspective, the replacement of dairy with plant-based milks is unlikely to be a concern for those with a diverse diet, and for those who do not rely on milk as an important source of protein. It is possible to meet these requirements from other foods – such as a combination of legumes, meat substitutes, and grains. However, for certain demographics – especially young children, and those on lower incomes with poor dietary diversity – this might be an inappropriate switch. The average person in many lower-income countries gets most of their calories from cheap, energy-dense crops like cereals and tubers (like cassava). This can be [more than three-quarters](https://ourworldindata.org/grapher/share-of-energy-from-cereals-roots-and-tubers-vs-gdp-per-capita) of an individual’s calorie intake. These diets do not provide the diversity of nutrients needed for good health – they are likely to be deficient in a number of [micronutrients](http://ourworldindata.org/micronutrient-deficiency), and protein (once we adjust for quality). Without access to foods that are fortified with vitamins and minerals, often small amounts of animal protein – such as milk – provide one of the few sources of complete protein and micronutrients in their diet. In such cases, removing dairy from a person’s diet without sufficient replacements could have a negative impact on health and nutrition. For most people in middle-to–high income countries, however, this is unlikely to be an issue. ## I’ve heard that soy is linked to deforestation in the Amazon. Is this a concern for soy milk? One of the largest concerns about alternatives such as soy milk is that they drive deforestation in the Amazon region. It’s true that the [growing demand for soy](https://ourworldindata.org/grapher/soybean-production?tab=chart&country=~OWID_WRL) has been [one of the drivers](https://ourworldindata.org/soy#is-soy-production-driving-deforestation) of Brazilian land-use change. Although, by far, the largest driver has been pasture for beef production. But, it’s important to note what this soy is used for. 95% of Brazilian soy is used for animal feed.5 Globally, [more than three-quarters](https://ourworldindata.org/soy#more-than-three-quarters-of-global-soy-is-fed-to-animals) of soy, by mass, is used for animal feed. The other main co-product is soybean oil. This means that very little of Amazonian land-use pressures from soy have been driven by crops for direct human consumption; most is for animal feed. Another factor to consider here, especially for European consumers, is that [most of Brazil’s soy](https://www.reuters.com/article/us-brazil-gmo/brazil-boasts-worlds-second-largest-genetically-modified-crop-area-isaaa-idUSKBN1JN1KW) crop is genetically modified (some estimates put this figure at 94%). There are strict regulations on the use of GM soy for direct human food in the European Union. Most of the soy consumed in Europe is produced in Europe. [Soy yields](https://ourworldindata.org/grapher/soybean-yields) are similar in the EU as they are in Brazil and the rest of South America – 3 tonnes per hectare in France versus 2.9 tonnes in Brazil. In fact, some EU countries have higher yields, such as Spain (3.3 tonnes) and Italy (4 tonnes). So the environmental impact of EU soy will be lower than in South America. --- # Keep reading at Our World in Data ### Is our appetite for soy driving deforestation in the Amazon? https://ourworldindata.org/soy ### Explore our page on the environmental impacts of food production https://ourworldindata.org/environmental-impacts-of-food Sandström, V., Valin, H., Krisztin, T., Havlík, P., Herrero, M., & Kastner, T. (2018). [The role of trade in the greenhouse gas footprints of EU diets](https://www.sciencedirect.com/science/article/pii/S2211912418300361). Global Food Security, 19, 48-55. Despite a large share of the population saying they now drink plant-based alternatives, dairy milk still dominates the UK market in terms of sales volume (with 96% for white milk). Market surveys suggest people favor cow’s milk versus vegan milks for particular uses e.g. hot versus cold drinks. This data comes from the largest meta-analysis of food impacts to date, published by Joseph Poore and Thomas Nemecek (2018) in _Science. _In this study, the authors looked at data across more than 38,000 commercial farms in 119 countries and quantified their environmental impacts taking into account the entire production chain – from land-use change through to retail and packaging. Poore, J., & Nemecek, T. (2018). [Reducing food’s environmental impacts through producers and consumers](https://science.sciencemag.org/content/360/6392/987). Science, 360(6392), 987-992. One way of comparing the quality of different protein sources is using their Protein Digestibility-Corrected Amino Acid Score (PDCAAS). This score looks not only at the total protein they provide but also digestibility, and whether there are particular deficiencies of specific amino acids. Most animal proteins tend to score very highly on PDCAAS. Plant-based foods such as soy also score very highly. But achieving a complete animo acid profile on a vegan diet requires a mix of grains, legumes and meat-free substitute proteins. Schaafsma, G. (2000). [The protein digestibility–corrected amino acid score](https://academic.oup.com/jn/article/130/7/1865S/4686203). The Journal of Nutrition, 130(7), 1865S-1867S. Young, V. R., & Pellett, P. L. (1994). [Plant proteins in relation to human protein and amino acid nutrition](https://pubmed.ncbi.nlm.nih.gov/8172124/). The American Journal of Clinical Nutrition, 59(5), 1203S-1212S. Poore, J., & Nemecek, T. (2018). [Reducing food’s environmental impacts through producers and consumers](https://science.sciencemag.org/content/360/6392/987). Science, 360(6392), 987-992. FAOstat: UN Food and Agriculture Organization (FAO) Statistics. 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""seir-model-with-machine-learning-layer-details-as-of-23-august-2020"", ""text"": ""SEIR model with machine learning layer (details as of 23 August 2020)"", ""title"": ""SEIR model with machine learning layer (details as of 23 August 2020)"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""update-youyang-gu-announced-that-5-october-2020-is-the-final-model-update"", ""text"": ""Update: Youyang Gu announced that 5 October 2020 is the final model update"", ""title"": ""Update: Youyang Gu announced that 5 October 2020 is the final model update"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""website"", ""text"": ""Website"", ""title"": ""Website"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""regions-covered"", ""text"": ""Regions covered"", ""title"": ""Regions covered"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""time-covered"", ""text"": ""Time covered"", ""title"": ""Time covered"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""update-frequency"", ""text"": ""Update frequency"", ""title"": ""Update frequency"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-is-the-model"", ""text"": ""What is the model?"", ""title"": ""What is the model?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-is-the-model-used-for"", ""text"": ""What is the model used for?"", ""title"": ""What is the model used for?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-data-is-the-model-based-on"", ""text"": ""What data is the model based on?"", ""title"": ""What data is the model based on?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-are-key-assumptions-and-potential-limitations"", ""text"": ""What are key assumptions and potential limitations?"", ""title"": ""What are key assumptions and potential limitations?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""statistical-model-estimating-underreporting-of-infections-details-as-of-23-august-2020"", ""text"": ""Statistical model estimating underreporting of infections (details as of 23 August 2020)"", ""title"": ""Statistical model estimating underreporting of infections (details as of 23 August 2020)"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""website"", ""text"": ""Website"", ""title"": ""Website"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""regions-covered"", ""text"": ""Regions covered"", ""title"": ""Regions covered"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""time-covered"", ""text"": ""Time covered"", ""title"": ""Time covered"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""update-frequency"", ""text"": ""Update frequency"", ""title"": ""Update frequency"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-is-the-model"", ""text"": ""What is the model?"", ""title"": ""What is the model?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-is-the-model-used-for"", ""text"": ""What is the model used for?"", ""title"": ""What is the model used for?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-data-is-the-model-based-on"", ""text"": ""What data is the model based on?"", ""title"": ""What data is the model based on?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-are-key-assumptions-and-potential-limitations"", ""text"": ""What are key assumptions and potential limitations?"", ""title"": ""What are key assumptions and potential limitations?"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""A key limitation in our understanding of the COVID-19 pandemic is that we do not know the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""true"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" number of infections. Instead, we only know of infections that have been confirmed by a test – the confirmed cases. But because many infected people never get tested,"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" we know that confirmed cases are only a fraction of true infections. How small a fraction though?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To answer this question, several research groups have developed epidemiological models of COVID-19. These models use the data we have – confirmed cases and deaths, testing rates, and more – plus a range of assumptions and epidemiological knowledge to estimate true infections and other important metrics."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart here shows the mean estimates of the true number of daily new infections in the United States from four of the most prominent models."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" For comparison, the number of confirmed cases is also shown."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""url"": ""http://ourworldindata.org/covid-models#imperial-college-london-icl"", ""children"": [{""text"": ""Imperial College London (ICL)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""http://ourworldindata.org/covid-models#institute-for-health-metrics-and-evaluation-ihme"", ""children"": [{""text"": ""The Institute for Health Metrics and Evaluation (IHME)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""http://ourworldindata.org/covid-models#youyang-gu-yyg"", ""children"": [{""text"": ""Youyang Gu (YYG)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""http://ourworldindata.org/covid-models#london-school-of-hygiene-tropical-medicine-lshtm"", ""children"": [{""text"": ""The London School of Hygiene & Tropical Medicine (LSHTM)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/daily-new-estimated-infections-of-covid-19"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Two things are clear from this chart: All four models agree that true infections "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""far outnumber"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" confirmed cases. But the models disagree by how much, and how infections have changed over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When the number of confirmed cases in the US reached a peak in late July 2020, the IHME and LSHTM models estimated that the true number of infections was about twice as high as confirmed cases, the ICL model estimated it was nearly three times as high, and Youyang Gu's model estimated it was more than "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""six times"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" as high. Back in March the estimated discrepancy between confirmed cases and true infections was even many times higher."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this post we examine these four models and how they differ by unpacking their essential elements: what they are used for, how they work, the data they are based on, and the assumptions they make."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We also aim to make the model estimates easily accessible in our interactive charts, allowing you to quickly explore different models of the pandemic for most countries in the world. To do this simply click \""Change country\"" on each chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Three of the four models we look at are “SEIR”"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" models,"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" which simulate how individuals in a population move through four states of a COVID-19 infection: being "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""S"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""usceptible, "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""E"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""xposed, "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""I"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""nfectious, and "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""R"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""ecovered (or deceased). How individuals move through these states is determined by different model “parameters,” of which there are many. Two key ones are the effective reproduction number (Rt)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" – how many other people a person with COVID-19 infects at a given time – and the infection fatality rate (IFR) – the percent of people infected with a disease who die from it."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can learn more about how SEIR models work by exploring these resources:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""url"": ""https://covid19-projections.com/model-details/"", ""children"": [{""text"": ""Youyang Gu’s Model Details"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (for a brief read)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://youtu.be/Lcx2a1jXISc"", ""children"": [{""text"": ""COVID Act Now’s COVID Data 101: What is an SEIR model?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (for a brief video)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://medium.com/data-for-science/epidemic-modeling-102-all-covid-19-models-are-wrong-but-some-are-useful-c81202cc6ee9"", ""children"": [{""text"": ""Bruno Gonçalves’s Epidemic Modeling 102: All CoVID-19 models are wrong, but some are useful"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (for a more in-depth read)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Imperial College London (ICL)"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Age-structured SEIR model focused on low- and middle-income countries (details as of 23 August 2020)"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart shows the ICL model’s estimates of the true number of daily new infections in the United States. To see the estimates for other countries click \""Change country.\"" The lines labeled “upper” and “lower” show the bounds of a 95% uncertainty interval. For comparison, the number of confirmed cases is also shown."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/daily-new-estimated-covid-19-infections-icl-model"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Website"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://mrc-ide.github.io/global-lmic-reports/"", ""children"": [{""text"": ""https://mrc-ide.github.io/global-lmic-reports/"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""text"": [{""text"": ""Regions covered"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""164 countries and territories across the world"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Time covered"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The first date covered is the estimated start of the pandemic for each country. The model makes projections that extend 90 days past the latest date of update."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""Update frequency"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""About 2–3 times per week"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What is the model?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model is a stochastic SEIR variant with multiple infectious states to reflect different COVID-19 severities, such as mild or asymptomatic versus severe."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What is the model used for?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""ICL describes its model as a tool to help countries understand at what stage the country is in its epidemic (e.g., before or after a peak) and how healthcare demand might change in the future under three policy scenarios. These scenarios are designed to provide a counterfactual of what could happen if current interventions were maintained, increased, or relaxed and are therefore not intended to forecast future mortality."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""ICL uses the model estimates to write reports for individual low- and middle-income countries (LMICs) that are relatively early in their epidemics; these reports are focused on the next 28 days. The downloadable model estimates additionally include data for some high-income countries later in their epidemics (e.g., the US and EU countries) and projections 90 days into the future."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Based on the model ICL publishes estimates of the following metrics:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""True infections (to-date and projected)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Confirmed deaths (projected)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Hospital and ICU demand (to-date and projected)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Effective reproduction number, Rt (to-date and projected)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""What data is the model based on?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model is “fit” to data on confirmed deaths"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" by using an estimated IFR to “back-calculate” how many infections would have been likely over the previous weeks to produce that number of deaths. It uses mobility data – from "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/covid-mobility-trends"", ""children"": [{""text"": ""Google"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" or, if unavailable, inferred from "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.acaps.org/covid19-government-measures-dataset"", ""children"": [{""text"": ""ACAPS government measures data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – to modulate the Rt, the key parameter on how transmission is changing."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Additionally, the model uses age- and country-specific data on demographics, patterns of social contact, hospital availability, and the risk of hospitalization and death, though the availability of this data varies by country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What are key assumptions and potential limitations?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model uses an estimated IFR for each country calculated by applying age-specific IFRs observed in China and Europe (of about 0.6–1%) to that country’s age distribution. In countries like many LMICs with younger populations than in China and Europe, this results in IFR estimates of typically 0.2–0.3% because younger populations have lower associated mortality rates. These lower mortality rates, however, assume access to sufficient healthcare, which might not always be the case in LMICs. Differences between the estimated and true IFRs could impact the accuracy of model estimates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model assumes that the number of confirmed deaths is equal to the true number of deaths. But "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/excess-mortality-covid"", ""children"": [{""text"": ""research on excess mortality"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and known limitations to testing and reporting capacity suggest that confirmed deaths are often fewer than true deaths. Where this is the case the model likely underestimates the true health burden."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model assumes that the change in transmission over time is a function of average mobility trends for places like stores and workplaces but not parks and residential areas."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" If these assumptions about mobility and transmission do not hold, the model might not accurately track the pandemic."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Like all models, this one makes many assumptions, and we cover only a few key ones here. For a full list see "", ""spanType"": ""span-simple-text""}, {""url"": ""https://mrc-ide.github.io/global-lmic-reports/parameters.html"", ""children"": [{""text"": ""the model methods description"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Institute for Health Metrics and Evaluation (IHME)"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Hybrid statistical/SEIR model (details as of 23 August 2020)"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Update: IHME "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.healthdata.org/covid/data-downloads"", ""children"": [{""text"": ""announced"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that \""after December 16, 2022, IHME will pause its COVID-19 modeling for the foreseeable future.\"""", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart shows the IHME model’s estimates of the true number of daily new infections in the United States. To see the estimates for other countries click \""Change country.\"" The lines labeled “upper” and “lower” show the bounds of a 95% uncertainty interval. For comparison, the number of confirmed cases is also shown."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/daily-new-estimated-covid-19-infections-ihme-model"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Website"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://covid19.healthdata.org/"", ""children"": [{""text"": ""https://covid19.healthdata.org/"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""text"": [{""text"": ""Regions covered"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""159 countries and territories across the world including subnational data for the US and several other countries"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Time covered"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The first date covered varies by country"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" The model makes projections that extend approximately 90–120 days past the latest date of update."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Update frequency"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""About once a week (though not all countries are updated each time)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What is the model?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model is a hybrid with two main components: a statistical “death model” component produces death estimates that are used to fit an SEIR model component."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Note that the model has had two significant updates since its initial publication:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""url"": ""http://www.healthdata.org/sites/default/files/files/Projects/COVID/Estimation_update_050420.pdf"", ""children"": [{""text"": ""The SEIR component was added on 4 May 2020"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""http://www.healthdata.org/sites/default/files/files/Projects/COVID/Estimation_update_05.30.2020.pdf"", ""children"": [{""text"": ""The death model component was updated on 29 May 2020"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""What is the model used for?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""IHME describes its model as a tool to help government officials understand how different policy decisions could impact the course of the pandemic and to plan for changing healthcare demand."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model makes deaths projections that have been highly publicized and sometimes criticized."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Though much of the criticism was leveled at a previous version of the model, known as “CurveFit,” that was used before the SEIR component was added on 4 May. The projections are made under currently three scenarios."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Based on the model IHME publishes estimates of the following metrics:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""True infections (to-date and projected)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Confirmed deaths (projected)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Hospital, ICU, and ventilator demand (to-date and projected)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Effective reproduction number, Rt (to-date and projected)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Testing levels (projected)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Mobility, as a proxy for social distancing (projected)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""What data is the model based on?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The death model uses data on confirmed cases, confirmed deaths,"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" and testing."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The SEIR model is fit to the output of the death model by using an estimated IFR to back-calculate the true number of infections."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model uses several other types of data to simulate transmission and disease progression: mobility, social distancing policies, population density, pneumonia seasonality and death rate, air pollution, altitude, smoking rates, and self-reported contacts and mask use. Details on the sources of these data can be found on the "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.healthdata.org/covid/faqs"", ""children"": [{""text"": ""model FAQs"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.healthdata.org/covid/updates"", ""children"": [{""text"": ""estimation updates"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" pages."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What are key assumptions and potential limitations?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model uses an estimated IFR based on data from the Diamond Princess cruise ship and New Zealand. Though IHME does not give numbers for these, the Diamond Princess IFR has been estimated at 0.6% (95% uncertainty interval of 0.2–1.3%)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Differences between the estimated and true IFRs could impact the accuracy of model estimates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The death model makes several assumptions about the relationship between confirmed deaths, confirmed cases, and testing levels. For example, that a decreasing "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""case"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" fatality rate (CFR) – the ratio of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""confirmed"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" deaths to "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""confirmed"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" cases"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" – is reflective of increasing testing and a shift toward testing mild or asymptomatic cases. But the CFR could also decrease for other reasons, such as improved treatment or a decline in the average age of infected people."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model assumes that the change in transmission over time is a function of several data inputs (listed above), like mobility and population density. If these assumptions do not hold – for example, because the data is less relevant or its relationship with transmission is misspecified – the model might not accurately track the pandemic."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""More details are discussed in the "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.healthdata.org/covid/faqs"", ""children"": [{""text"": ""model FAQs"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and in different "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.healthdata.org/covid/updates"", ""children"": [{""text"": ""estimation update reports"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Youyang Gu (YYG)"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""SEIR model with machine learning layer (details as of 23 August 2020)"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Update: Youyang Gu "", ""spanType"": ""span-simple-text""}, {""url"": ""https://youyanggu.com/blog/six-months-later"", ""children"": [{""text"": ""announced"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that 5 October 2020 is the final model update"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart shows the YYG model’s estimates of the true number of daily new infections in the United States. To see the estimates for other countries click \""Change country.\"" The lines labeled “upper” and “lower” show the bounds of a 95% uncertainty interval. For comparison, the number of confirmed cases is also shown."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/daily-new-estimated-covid-19-infections-yyg-model"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Website"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://covid19-projections.com/"", ""children"": [{""text"": ""https://covid19-projections.com/"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""text"": [{""text"": ""Regions covered"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""71 countries across the world including subnational data for the US and Canada"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Time covered"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The first date covered varies by country"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" The model makes projections that extend approximately 90 days past the latest date of update."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Update frequency"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Daily"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What is the model?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model consists of an SEIR base with a machine learning layer on top to search for the parameters that minimize the error between the model estimates and the observed data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What is the model used for?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Youyang describes his model as making projections of true infections and deaths that optimize for forecast accuracy. Though he also stresses that his projections cover a range of possible outcomes, and that projections are not “wrong” if they help shape a different outcome in the future."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Based on the model Youyang publishes estimates of the following metrics:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""True infections (to-date and projected)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Confirmed deaths (projected)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Effective reproduction number, Rt (to-date and projected)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Tests per day targets (projected)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model does not focus on projections under different scenarios, but has explored what would have happened if the US had mandated social distancing "", ""spanType"": ""span-simple-text""}, {""url"": ""https://covid19-projections.com/us-1weekearlier"", ""children"": [{""text"": ""one week earlier"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" or "", ""spanType"": ""span-simple-text""}, {""url"": ""https://covid19-projections.com/us-1weeklater"", ""children"": [{""text"": ""one week later"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", or "", ""spanType"": ""span-simple-text""}, {""url"": ""https://covid19-projections.com/us-self-quarantine"", ""children"": [{""text"": ""if 20% of infected people immediately self-quarantined"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What data is the model based on?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model is fit to data on confirmed deaths"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-15"", ""children"": [{""children"": [{""text"": ""15"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" by using an estimated IFR to back-calculate the true number of infections. Confirmed cases and hospitalization data are sometimes used to help set bounds for the machine learning parameter search."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What are key assumptions and potential limitations?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model uses an estimated IFR for each region based initially on that region’s observed CFR. The IFR is then decreased"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-16"", ""children"": [{""children"": [{""text"": ""16"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" linearly over the span of three months until it is 30% of its initial value to reflect the lower average age of infections and improving treatments. Currently, the IFR is estimated to be 0.2–0.4% in most of the US and Europe. Differences between the estimated and true IFRs could impact the accuracy of model estimates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model assumes there will be unreported deaths for the \""first few weeks” of a region’s pandemic, and that this underreporting will decrease until the number of confirmed deaths equals true deaths. As noted before, this is often not the case, and thus the model might underestimate the true health burden."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model makes assumptions about how reopening will affect social distancing and ultimately transmission. For example, if reopening causes a resurgence of infections, the model assumes regions will take action to reduce transmission, which is modeled by limiting the Rt. It also assumes a reopening date for regions (especially outside the US and Europe) where the true date is unknown."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model was created and optimized for the US. Thus for other countries the model estimates might be less accurate."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For a full list of assumptions and limitations see "", ""spanType"": ""span-simple-text""}, {""url"": ""https://covid19-projections.com/about/#assumptions"", ""children"": [{""text"": ""the model \""About\"" page"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""London School of Hygiene & Tropical Medicine (LSHTM)"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Statistical model estimating underreporting of infections (details as of 23 August 2020)"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart shows the LSHTM model’s estimates of the true number of daily new infections in the United States. To see the estimates for other countries click \""Change country.\"" The lines labeled “upper” and “lower” show the bounds of a 95% uncertainty interval. For comparison, the number of confirmed cases is also shown."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/daily-new-estimated-covid-19-infections-lshtm-model"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Website"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://cmmid.github.io/topics/covid19/global_cfr_estimates.html"", ""children"": [{""text"": ""https://cmmid.github.io/topics/covid19/global_cfr_estimates.html"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""text"": [{""text"": ""Regions covered"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""159 countries and territories across the world (those with at least 10 confirmed deaths out of a total of 210)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Time covered"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The first date covered varies by country"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "". "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""The model does not make projections."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Update frequency"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""About once a week"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What is the model?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model starts with a country’s CFR and adjusts it for the fact that there is a delay of roughly 2–3 weeks between case confirmation and death (or recovery)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-17"", ""children"": [{""children"": [{""text"": ""17"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This delay-adjusted CFR is then compared to a baseline, delay-adjusted CFR to estimate the \""ascertainment rate\"" – the proportion of all "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""symptomatic"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" infections that have actually been confirmed."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-18"", ""children"": [{""children"": [{""text"": ""18"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This estimated ascertainment rate is then used to adjust the number of confirmed cases"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-19"", ""children"": [{""children"": [{""text"": ""19"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" to estimate the true number of symptomatic infections. To finally estimate "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""total"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" infections, the symptomatic infections estimate is adjusted to include "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""asymptomatic"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" infections, which are estimated to compose between 10–70% (median 50%) of total infections."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-20"", ""children"": [{""children"": [{""text"": ""20"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""What is the model used for?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""LSHTM describes its model as a tool to help understand the level of undetected epidemic progression and to aid response planning, such as when to introduce and relax control measures."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Based on the model LSHTM publishes estimates of the ascertainment rate."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What data is the model based on?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model is based on data on confirmed deaths and confirmed cases."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-21"", ""children"": [{""children"": [{""text"": ""21"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""What are key assumptions and potential limitations?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model assumes a baseline, delay-adjusted CFR of 1.4% and that any difference between that and a country’s delay-adjusted CFR is entirely due to under-ascertainment. But many other factors likely play a role, such as the burden on the healthcare system, COVID-19 risk factors in the population, the ages of those infected, and more."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The assumed baseline CFR is based on data from China and does not account for different age distributions outside China. This causes the ascertainment rate to be overestimated in countries with younger populations and underestimated in countries with older populations."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-22"", ""children"": [{""children"": [{""text"": ""22"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model assumes that the number of confirmed deaths is equal to the true number of deaths. As noted before, this is often not the case, and thus the model might underestimate the true health burden."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Reported deaths data is sometimes changed retroactively, which can be challenging for the model and might affect its estimates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""More assumptions and limitations are discussed in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://cmmid.github.io/topics/covid19/reports/UnderReporting.pdf"", ""children"": [{""text"": ""the full report"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How should we think about these models and their estimates?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All four models we looked at agree that true infections far outnumber confirmed cases, but they disagree by how much. We now have some insight into these differences: The models all differ to some degree in what they are used for, how they work, the data they are based on, and the assumptions they make."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Making these differences transparent helps us understand how we should think about these models and their estimates. For example, understanding that some models are used for scenario planning and not forecasting (like ICL’s) while others are optimized for forecast accuracy (like Youyang’s) puts their estimates in context. And the models all make different assumptions that each have limitations; we can decide if those limitations are relevant to a given situation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the end, though, we still want to have confidence that models can track the pandemic accurately. We can calibrate our confidence in different models by giving their estimates a reality check."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One way to do this is to compare model estimates against some observed “ground truth” data. For example, if a model is forecasting the number of deaths four weeks from now, we can wait four weeks and compare the forecast to the deaths that actually occur."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-23"", ""children"": [{""children"": [{""text"": ""23"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But sometimes the ground truth is not easily observed, as is the case with the true number of infections. Here we have to look for "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""converging evidence"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" from other research, such as from seroprevalence studies that test for COVID-19 antibodies in the blood serum to estimate how many people have ever been infected."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-24"", ""children"": [{""children"": [{""text"": ""24"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""By gaining a deeper, more nuanced understanding of these models and their strengths and weaknesses, we can use them as valuable tools to help make progress against the pandemic."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""We are grateful to the researchers whose work we cover in this article for giving helpful feedback and suggestions. Thank you."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Acknowledgments"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""0c27775cc86a32d9205fbf6aa330412888d49c03"": {""id"": ""0c27775cc86a32d9205fbf6aa330412888d49c03"", ""index"": 17, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""All but a trivial number of confirmed cases are assumed to be symptomatic."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""0c5dcae44c19f01124721292e6c8cddbc06075e6"": {""id"": ""0c5dcae44c19f01124721292e6c8cddbc06075e6"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Also called \""time-varying\"" reproduction number."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""166b028e61b7dd5aa40b69b6e05df086b9a0e9d9"": {""id"": ""166b028e61b7dd5aa40b69b6e05df086b9a0e9d9"", ""index"": 14, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""As reported by Johns Hopkins University. 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Source: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html"", ""children"": [{""text"": ""CDC COVID-19 Pandemic Planning Scenarios"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""84dc6087af02b9222643166e09bda5ea3a796161"": {""id"": ""84dc6087af02b9222643166e09bda5ea3a796161"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For example: Sharon Begley (2020, 17 Apr.) “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.statnews.com/2020/04/17/influential-covid-19-model-uses-flawed-methods-shouldnt-guide-policies-critics-say/"", ""children"": [{""text"": ""Influential Covid-19 model uses flawed methods and shouldn’t guide U.S. policies, critics say."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” STAT News."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""8c5c5c0e5bc1442977474b4e5da591666c40a1f6"": {""id"": ""8c5c5c0e5bc1442977474b4e5da591666c40a1f6"", ""index"": 21, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""In a secondary analysis the LSHTM researchers do adjust the baseline CFR for different age distributions. But this has its own assumptions and limitations and is thus not clearly a better approach. More details can be found in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://cmmid.github.io/topics/covid19/reports/UnderReporting.pdf"", ""children"": [{""text"": ""the full report"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""8fc6577e22d0a0c1ba50c29861f1f180dc3b7083"": {""id"": ""8fc6577e22d0a0c1ba50c29861f1f180dc3b7083"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""There are many models in use besides these four, including other ones by the research groups we cover here. We chose these four models because they are prominent, have been used by policymakers, and have been updated regularly. 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You can read more about this in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://cmmid.github.io/topics/covid19/Under-Reporting.html"", ""children"": [{""text"": ""their full report."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b4f7d268760d4cbd74f065198f86752861bdc7d1"": {""id"": ""b4f7d268760d4cbd74f065198f86752861bdc7d1"", ""index"": 22, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Though we still need to consider that such forecasts might not track what actually occurs if they help shape a different outcome in the future."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Some current efforts to score forecasts for accuracy are by "", ""spanType"": ""span-simple-text""}, {""url"": ""https://github.com/youyanggu/covid19-forecast-hub-evaluation"", ""children"": [{""text"": ""Youyang Gu"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.healthdata.org/research-article/predictive-performance-international-covid-19-mortality-forecasting-models"", ""children"": [{""text"": ""IHME"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://zoltardata.com/about"", ""children"": [{""text"": ""The Zoltar Project"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://covidcompare.io/"", ""children"": [{""text"": ""Covid Compare"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""be13bc3577800db99e036516bcebf2b2788dcd91"": {""id"": ""be13bc3577800db99e036516bcebf2b2788dcd91"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The London School model is not an SEIR model."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""dbf0eb676b12e912e7984c662d85b507add596b6"": {""id"": ""dbf0eb676b12e912e7984c662d85b507add596b6"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The model assumes that in parks “significant contact events are negligible” and that an “increase in residential movement will not change household contacts.”"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e26f6b211c10c36d54a45fbccdcda2190107b2e5"": {""id"": ""e26f6b211c10c36d54a45fbccdcda2190107b2e5"", ""index"": 10, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Confirmed cases and deaths data as reported by Johns Hopkins University and several official sources."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ec044ccf2580e9dd5bf3d90fce751c1947297538"": {""id"": ""ec044ccf2580e9dd5bf3d90fce751c1947297538"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Pronounced by saying each letter, “S-E-I-R.”"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ed968eeefbf17ad9c3c2a3d76767bb04aacd4ddb"": {""id"": ""ed968eeefbf17ad9c3c2a3d76767bb04aacd4ddb"", ""index"": 9, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For more details about the scenarios see the "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.healthdata.org/covid/faqs"", ""children"": [{""text"": ""model FAQs"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""eeb28e1a3aa51506a06dfb30ec98f58688cdea03"": {""id"": ""eeb28e1a3aa51506a06dfb30ec98f58688cdea03"", ""index"": 12, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Russell et al (2020). Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship. Eurosurveillance, 25(12). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.2807/1560-7917.ES.2020.25.12.2000256"", ""children"": [{""text"": ""https://doi.org/10.2807/1560-7917.ES.2020.25.12.2000256"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""How epidemiological models of COVID-19 help us estimate the true number of infections"", ""authors"": [""Charlie Giattino""], ""excerpt"": ""We know that confirmed COVID-19 cases are only a fraction of true infections. How small a fraction though?"", ""dateline"": ""August 24, 2020"", ""subtitle"": ""We know that confirmed COVID-19 cases are only a fraction of true infections. How small a fraction though?"", ""sidebar-toc"": false, ""featured-image"": ""covid-models.png""}",1,2024-01-26 14:25:43,2020-08-24 11:00:25,2024-01-26 17:22:53,listed,ALBJ4LvV5LgH-navhick4_5S1qKhC-mTIUkbi08CKn3r1tmX_JREFNvRPCOb-FHIedlGjMItrs95lh2ZPf_HEQ,,"A key limitation in our understanding of the COVID-19 pandemic is that we do not know the _true_ number of infections. Instead, we only know of infections that have been confirmed by a test – the confirmed cases. But because many infected people never get tested,1 we know that confirmed cases are only a fraction of true infections. How small a fraction though? To answer this question, several research groups have developed epidemiological models of COVID-19. These models use the data we have – confirmed cases and deaths, testing rates, and more – plus a range of assumptions and epidemiological knowledge to estimate true infections and other important metrics. The chart here shows the mean estimates of the true number of daily new infections in the United States from four of the most prominent models.2 For comparison, the number of confirmed cases is also shown. * [Imperial College London (ICL)](http://ourworldindata.org/covid-models#imperial-college-london-icl) * [The Institute for Health Metrics and Evaluation (IHME)](http://ourworldindata.org/covid-models#institute-for-health-metrics-and-evaluation-ihme) * [Youyang Gu (YYG)](http://ourworldindata.org/covid-models#youyang-gu-yyg) * [The London School of Hygiene & Tropical Medicine (LSHTM)](http://ourworldindata.org/covid-models#london-school-of-hygiene-tropical-medicine-lshtm) Two things are clear from this chart: All four models agree that true infections _far outnumber_ confirmed cases. But the models disagree by how much, and how infections have changed over time. When the number of confirmed cases in the US reached a peak in late July 2020, the IHME and LSHTM models estimated that the true number of infections was about twice as high as confirmed cases, the ICL model estimated it was nearly three times as high, and Youyang Gu's model estimated it was more than _six times_ as high. Back in March the estimated discrepancy between confirmed cases and true infections was even many times higher. In this post we examine these four models and how they differ by unpacking their essential elements: what they are used for, how they work, the data they are based on, and the assumptions they make. We also aim to make the model estimates easily accessible in our interactive charts, allowing you to quickly explore different models of the pandemic for most countries in the world. To do this simply click ""Change country"" on each chart. Three of the four models we look at are “SEIR”3 models,4 which simulate how individuals in a population move through four states of a COVID-19 infection: being **S**usceptible, **E**xposed, **I**nfectious, and **R**ecovered (or deceased). How individuals move through these states is determined by different model “parameters,” of which there are many. Two key ones are the effective reproduction number (Rt)5 – how many other people a person with COVID-19 infects at a given time – and the infection fatality rate (IFR) – the percent of people infected with a disease who die from it. You can learn more about how SEIR models work by exploring these resources: * [Youyang Gu’s Model Details](https://covid19-projections.com/model-details/) (for a brief read) * [COVID Act Now’s COVID Data 101: What is an SEIR model?](https://youtu.be/Lcx2a1jXISc) (for a brief video) * [Bruno Gonçalves’s Epidemic Modeling 102: All CoVID-19 models are wrong, but some are useful](https://medium.com/data-for-science/epidemic-modeling-102-all-covid-19-models-are-wrong-but-some-are-useful-c81202cc6ee9) (for a more in-depth read) # Imperial College London (ICL) ## Age-structured SEIR model focused on low- and middle-income countries (details as of 23 August 2020) This chart shows the ICL model’s estimates of the true number of daily new infections in the United States. To see the estimates for other countries click ""Change country."" The lines labeled “upper” and “lower” show the bounds of a 95% uncertainty interval. For comparison, the number of confirmed cases is also shown. ## Website [https://mrc-ide.github.io/global-lmic-reports/](https://mrc-ide.github.io/global-lmic-reports/) ## Regions covered 164 countries and territories across the world ## Time covered The first date covered is the estimated start of the pandemic for each country. The model makes projections that extend 90 days past the latest date of update.6 ## Update frequency About 2–3 times per week ## What is the model? The model is a stochastic SEIR variant with multiple infectious states to reflect different COVID-19 severities, such as mild or asymptomatic versus severe. ## What is the model used for? ICL describes its model as a tool to help countries understand at what stage the country is in its epidemic (e.g., before or after a peak) and how healthcare demand might change in the future under three policy scenarios. These scenarios are designed to provide a counterfactual of what could happen if current interventions were maintained, increased, or relaxed and are therefore not intended to forecast future mortality. ICL uses the model estimates to write reports for individual low- and middle-income countries (LMICs) that are relatively early in their epidemics; these reports are focused on the next 28 days. The downloadable model estimates additionally include data for some high-income countries later in their epidemics (e.g., the US and EU countries) and projections 90 days into the future. Based on the model ICL publishes estimates of the following metrics: * True infections (to-date and projected) * Confirmed deaths (projected) * Hospital and ICU demand (to-date and projected) * Effective reproduction number, Rt (to-date and projected) ## What data is the model based on? The model is “fit” to data on confirmed deaths7 by using an estimated IFR to “back-calculate” how many infections would have been likely over the previous weeks to produce that number of deaths. It uses mobility data – from [Google](https://ourworldindata.org/covid-mobility-trends) or, if unavailable, inferred from [ACAPS government measures data](https://www.acaps.org/covid19-government-measures-dataset) – to modulate the Rt, the key parameter on how transmission is changing. Additionally, the model uses age- and country-specific data on demographics, patterns of social contact, hospital availability, and the risk of hospitalization and death, though the availability of this data varies by country. ## What are key assumptions and potential limitations? The model uses an estimated IFR for each country calculated by applying age-specific IFRs observed in China and Europe (of about 0.6–1%) to that country’s age distribution. In countries like many LMICs with younger populations than in China and Europe, this results in IFR estimates of typically 0.2–0.3% because younger populations have lower associated mortality rates. These lower mortality rates, however, assume access to sufficient healthcare, which might not always be the case in LMICs. Differences between the estimated and true IFRs could impact the accuracy of model estimates. The model assumes that the number of confirmed deaths is equal to the true number of deaths. But [research on excess mortality](https://ourworldindata.org/excess-mortality-covid) and known limitations to testing and reporting capacity suggest that confirmed deaths are often fewer than true deaths. Where this is the case the model likely underestimates the true health burden. The model assumes that the change in transmission over time is a function of average mobility trends for places like stores and workplaces but not parks and residential areas.8 If these assumptions about mobility and transmission do not hold, the model might not accurately track the pandemic. Like all models, this one makes many assumptions, and we cover only a few key ones here. For a full list see [the model methods description](https://mrc-ide.github.io/global-lmic-reports/parameters.html). # Institute for Health Metrics and Evaluation (IHME) ## Hybrid statistical/SEIR model (details as of 23 August 2020) ## Update: IHME [announced](https://www.healthdata.org/covid/data-downloads) that ""after December 16, 2022, IHME will pause its COVID-19 modeling for the foreseeable future."" This chart shows the IHME model’s estimates of the true number of daily new infections in the United States. To see the estimates for other countries click ""Change country."" The lines labeled “upper” and “lower” show the bounds of a 95% uncertainty interval. For comparison, the number of confirmed cases is also shown. ## Website [https://covid19.healthdata.org/](https://covid19.healthdata.org/) ## Regions covered 159 countries and territories across the world including subnational data for the US and several other countries ## Time covered The first date covered varies by country**.** The model makes projections that extend approximately 90–120 days past the latest date of update. ## Update frequency About once a week (though not all countries are updated each time) ## What is the model? The model is a hybrid with two main components: a statistical “death model” component produces death estimates that are used to fit an SEIR model component. Note that the model has had two significant updates since its initial publication: * [The SEIR component was added on 4 May 2020](http://www.healthdata.org/sites/default/files/files/Projects/COVID/Estimation_update_050420.pdf) * [The death model component was updated on 29 May 2020](http://www.healthdata.org/sites/default/files/files/Projects/COVID/Estimation_update_05.30.2020.pdf) ## What is the model used for? IHME describes its model as a tool to help government officials understand how different policy decisions could impact the course of the pandemic and to plan for changing healthcare demand. The model makes deaths projections that have been highly publicized and sometimes criticized.9 Though much of the criticism was leveled at a previous version of the model, known as “CurveFit,” that was used before the SEIR component was added on 4 May. The projections are made under currently three scenarios.10 Based on the model IHME publishes estimates of the following metrics: * True infections (to-date and projected) * Confirmed deaths (projected) * Hospital, ICU, and ventilator demand (to-date and projected) * Effective reproduction number, Rt (to-date and projected) * Testing levels (projected) * Mobility, as a proxy for social distancing (projected) ## What data is the model based on? The death model uses data on confirmed cases, confirmed deaths,11 and testing.12 The SEIR model is fit to the output of the death model by using an estimated IFR to back-calculate the true number of infections. The model uses several other types of data to simulate transmission and disease progression: mobility, social distancing policies, population density, pneumonia seasonality and death rate, air pollution, altitude, smoking rates, and self-reported contacts and mask use. Details on the sources of these data can be found on the [model FAQs](http://www.healthdata.org/covid/faqs) and [estimation updates](http://www.healthdata.org/covid/updates) pages. ## What are key assumptions and potential limitations? The model uses an estimated IFR based on data from the Diamond Princess cruise ship and New Zealand. Though IHME does not give numbers for these, the Diamond Princess IFR has been estimated at 0.6% (95% uncertainty interval of 0.2–1.3%).13 Differences between the estimated and true IFRs could impact the accuracy of model estimates. The death model makes several assumptions about the relationship between confirmed deaths, confirmed cases, and testing levels. For example, that a decreasing _case_ fatality rate (CFR) – the ratio of _confirmed_ deaths to _confirmed_ cases14 – is reflective of increasing testing and a shift toward testing mild or asymptomatic cases. But the CFR could also decrease for other reasons, such as improved treatment or a decline in the average age of infected people. The model assumes that the change in transmission over time is a function of several data inputs (listed above), like mobility and population density. If these assumptions do not hold – for example, because the data is less relevant or its relationship with transmission is misspecified – the model might not accurately track the pandemic. More details are discussed in the [model FAQs](http://www.healthdata.org/covid/faqs) and in different [estimation update reports](http://www.healthdata.org/covid/updates). # Youyang Gu (YYG) ## SEIR model with machine learning layer (details as of 23 August 2020) ## Update: Youyang Gu [announced](https://youyanggu.com/blog/six-months-later) that 5 October 2020 is the final model update This chart shows the YYG model’s estimates of the true number of daily new infections in the United States. To see the estimates for other countries click ""Change country."" The lines labeled “upper” and “lower” show the bounds of a 95% uncertainty interval. For comparison, the number of confirmed cases is also shown. ## Website [https://covid19-projections.com/](https://covid19-projections.com/) ## Regions covered 71 countries across the world including subnational data for the US and Canada ## Time covered The first date covered varies by country**.** The model makes projections that extend approximately 90 days past the latest date of update. ## Update frequency Daily ## What is the model? The model consists of an SEIR base with a machine learning layer on top to search for the parameters that minimize the error between the model estimates and the observed data. ## What is the model used for? Youyang describes his model as making projections of true infections and deaths that optimize for forecast accuracy. Though he also stresses that his projections cover a range of possible outcomes, and that projections are not “wrong” if they help shape a different outcome in the future. Based on the model Youyang publishes estimates of the following metrics: * True infections (to-date and projected) * Confirmed deaths (projected) * Effective reproduction number, Rt (to-date and projected) * Tests per day targets (projected) The model does not focus on projections under different scenarios, but has explored what would have happened if the US had mandated social distancing [one week earlier](https://covid19-projections.com/us-1weekearlier) or [one week later](https://covid19-projections.com/us-1weeklater), or [if 20% of infected people immediately self-quarantined](https://covid19-projections.com/us-self-quarantine). ## What data is the model based on? The model is fit to data on confirmed deaths15 by using an estimated IFR to back-calculate the true number of infections. Confirmed cases and hospitalization data are sometimes used to help set bounds for the machine learning parameter search. ## What are key assumptions and potential limitations? The model uses an estimated IFR for each region based initially on that region’s observed CFR. The IFR is then decreased16 linearly over the span of three months until it is 30% of its initial value to reflect the lower average age of infections and improving treatments. Currently, the IFR is estimated to be 0.2–0.4% in most of the US and Europe. Differences between the estimated and true IFRs could impact the accuracy of model estimates. The model assumes there will be unreported deaths for the ""first few weeks” of a region’s pandemic, and that this underreporting will decrease until the number of confirmed deaths equals true deaths. As noted before, this is often not the case, and thus the model might underestimate the true health burden. The model makes assumptions about how reopening will affect social distancing and ultimately transmission. For example, if reopening causes a resurgence of infections, the model assumes regions will take action to reduce transmission, which is modeled by limiting the Rt. It also assumes a reopening date for regions (especially outside the US and Europe) where the true date is unknown. The model was created and optimized for the US. Thus for other countries the model estimates might be less accurate. For a full list of assumptions and limitations see [the model ""About"" page](https://covid19-projections.com/about/#assumptions). # London School of Hygiene & Tropical Medicine (LSHTM) ## Statistical model estimating underreporting of infections (details as of 23 August 2020) This chart shows the LSHTM model’s estimates of the true number of daily new infections in the United States. To see the estimates for other countries click ""Change country."" The lines labeled “upper” and “lower” show the bounds of a 95% uncertainty interval. For comparison, the number of confirmed cases is also shown. ## Website [https://cmmid.github.io/topics/covid19/global_cfr_estimates.html](https://cmmid.github.io/topics/covid19/global_cfr_estimates.html) ## Regions covered 159 countries and territories across the world (those with at least 10 confirmed deaths out of a total of 210) ## Time covered The first date covered varies by country**. **The model does not make projections. ## Update frequency About once a week ## What is the model? The model starts with a country’s CFR and adjusts it for the fact that there is a delay of roughly 2–3 weeks between case confirmation and death (or recovery).17 This delay-adjusted CFR is then compared to a baseline, delay-adjusted CFR to estimate the ""ascertainment rate"" – the proportion of all _symptomatic_ infections that have actually been confirmed.18 This estimated ascertainment rate is then used to adjust the number of confirmed cases19 to estimate the true number of symptomatic infections. To finally estimate _total_ infections, the symptomatic infections estimate is adjusted to include _asymptomatic_ infections, which are estimated to compose between 10–70% (median 50%) of total infections.20 ## What is the model used for? LSHTM describes its model as a tool to help understand the level of undetected epidemic progression and to aid response planning, such as when to introduce and relax control measures. Based on the model LSHTM publishes estimates of the ascertainment rate. ## What data is the model based on? The model is based on data on confirmed deaths and confirmed cases.21 ## What are key assumptions and potential limitations? The model assumes a baseline, delay-adjusted CFR of 1.4% and that any difference between that and a country’s delay-adjusted CFR is entirely due to under-ascertainment. But many other factors likely play a role, such as the burden on the healthcare system, COVID-19 risk factors in the population, the ages of those infected, and more. The assumed baseline CFR is based on data from China and does not account for different age distributions outside China. This causes the ascertainment rate to be overestimated in countries with younger populations and underestimated in countries with older populations.22 The model assumes that the number of confirmed deaths is equal to the true number of deaths. As noted before, this is often not the case, and thus the model might underestimate the true health burden. Reported deaths data is sometimes changed retroactively, which can be challenging for the model and might affect its estimates. More assumptions and limitations are discussed in [the full report](https://cmmid.github.io/topics/covid19/reports/UnderReporting.pdf). # How should we think about these models and their estimates? All four models we looked at agree that true infections far outnumber confirmed cases, but they disagree by how much. We now have some insight into these differences: The models all differ to some degree in what they are used for, how they work, the data they are based on, and the assumptions they make. Making these differences transparent helps us understand how we should think about these models and their estimates. For example, understanding that some models are used for scenario planning and not forecasting (like ICL’s) while others are optimized for forecast accuracy (like Youyang’s) puts their estimates in context. And the models all make different assumptions that each have limitations; we can decide if those limitations are relevant to a given situation. In the end, though, we still want to have confidence that models can track the pandemic accurately. We can calibrate our confidence in different models by giving their estimates a reality check. One way to do this is to compare model estimates against some observed “ground truth” data. For example, if a model is forecasting the number of deaths four weeks from now, we can wait four weeks and compare the forecast to the deaths that actually occur.23 But sometimes the ground truth is not easily observed, as is the case with the true number of infections. Here we have to look for _converging evidence_ from other research, such as from seroprevalence studies that test for COVID-19 antibodies in the blood serum to estimate how many people have ever been infected.24 By gaining a deeper, more nuanced understanding of these models and their strengths and weaknesses, we can use them as valuable tools to help make progress against the pandemic. --- Infected people might not get tested for several reasons, such as not having easy access to testing or not even knowing they are infected because they have no symptoms (though they are still able to transmit the virus). Such asymptomatic infections are estimated to be 10–70% of total infections. Source: [CDC COVID-19 Pandemic Planning Scenarios](https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html). There are many models in use besides these four, including other ones by the research groups we cover here. We chose these four models because they are prominent, have been used by policymakers, and have been updated regularly. We use them more for illustration than completeness. Pronounced by saying each letter, “S-E-I-R.” The London School model is not an SEIR model. Also called ""time-varying"" reproduction number. While projections are an important aspect of what this and some other models are used for, we do not cover them in this article. As reported by the European Centre for Disease Prevention and Control (ECDC). The model assumes that in parks “significant contact events are negligible” and that an “increase in residential movement will not change household contacts.” For example: Sharon Begley (2020, 17 Apr.) “[Influential Covid-19 model uses flawed methods and shouldn’t guide U.S. policies, critics say.](https://www.statnews.com/2020/04/17/influential-covid-19-model-uses-flawed-methods-shouldnt-guide-policies-critics-say/)” STAT News. For more details about the scenarios see the [model FAQs](http://www.healthdata.org/covid/faqs). Confirmed cases and deaths data as reported by Johns Hopkins University and several official sources. As reported by the COVID Tracking Project (for US), official sources (Brazil and Dominican Republic), and Our World in Data (all other countries). Russell et al (2020). Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship. Eurosurveillance, 25(12). [https://doi.org/10.2807/1560-7917.ES.2020.25.12.2000256](https://doi.org/10.2807/1560-7917.ES.2020.25.12.2000256) The CFR is similar to the IFR but uses the _confirmed_ deaths and cases reported by countries. In contrast, the IFR uses true deaths and infections, which are generally not known and have to be estimated. As reported by Johns Hopkins University. The data is smoothed before fitting. Except in “later-impacted regions like Latin America, we wait an additional 3 months before beginning to decrease the IFR.” The typical CFR calculation divides confirmed deaths by confirmed cases _reported on the same day_, but those deaths were actually caused by cases confirmed roughly 2–3 weeks before. All but a trivial number of confirmed cases are assumed to be symptomatic. This data is first smoothed. In accordance with this methodology and in consultation with the LSHTM researchers, we perform these calculations to produce the estimates of total infections presented here. Both as reported by the ECDC. In a secondary analysis the LSHTM researchers do adjust the baseline CFR for different age distributions. But this has its own assumptions and limitations and is thus not clearly a better approach. More details can be found in [the full report](https://cmmid.github.io/topics/covid19/reports/UnderReporting.pdf). Though we still need to consider that such forecasts might not track what actually occurs if they help shape a different outcome in the future. Some current efforts to score forecasts for accuracy are by [Youyang Gu](https://github.com/youyanggu/covid19-forecast-hub-evaluation), [IHME](http://www.healthdata.org/research-article/predictive-performance-international-covid-19-mortality-forecasting-models), [The Zoltar Project](https://zoltardata.com/about), and [Covid Compare](https://covidcompare.io/). The LSHTM researchers, for example, compared their model estimates to seroprevalence estimates and found good agreement. You can read more about this in [their full report.](https://cmmid.github.io/topics/covid19/Under-Reporting.html)",How epidemiological models of COVID-19 help us estimate the true number of infections 1zi0ZsRO1gP-IzBpCs0D6mTVcvo65Vohwyi7zZd8ZAOA,the-world-has-become-more-resilient-to-disasters-but-investment-is-needed-to-save-more-lives,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""In 1970, more than 300,000 people died when "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.thebritishacademy.ac.uk/documents/3537/JBA-9s9-04-Sammonds-etal.pdf"", ""children"": [{""text"": ""a strong cyclone"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" hit the coast of Bangladesh."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In 1985, another storm caused 15,000 deaths. Just six years later, another killed 140,000."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Fast-forward to 2020. Bangladesh was hit by cyclone Amphan, one of the strongest storms on record in the Bay of Bengal. The death toll was 26 — barely visible on the chart below, compared to these very deadly disasters."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""That’s 26 too many deaths, and the cyclone also caused huge amounts of damage: millions of people were displaced, and there were large economic losses. But tens — possibly hundreds — of thousands of lives were saved through early warnings, evacuations, and increased resilience. People in Bangladesh are much better protected from disasters than they were a few decades ago."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This development is part of a longer-term and widespread success in reducing humanity’s vulnerability to storms, floods, earthquakes, and other hazards."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/explorers/natural-disasters?time=1968..latest&facet=none&hideControls=true&Disaster+Type=Storms&Impact=Deaths&Timespan=Annual&Per+capita=false&country=~BGD&tab=chart"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The world has become more resilient to disasters"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Bangladesh is not an isolated example. We can observe long-term improvements in the world's resilience."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Here, I will look at data published by the International Disaster Database, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.emdat.be/"", ""children"": [{""text"": ""EM-DAT"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", which stretches back to 1900. In the chart below, I’ve shown the number of deaths from disasters, given as the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""decadal"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" average. This is helpful as there is a lot of volatility in disasters from year to year."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" You can also explore this data "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/natural-disasters?time=1900..latest&facet=none&Disaster+Type=All+disasters&Impact=Deaths&Timespan=Annual&Per+capita=false&country=~OWID_WRL"", ""children"": [{""text"": ""annually"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The number of people killed in disasters has fallen a lot over the last century. That’s despite there being "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/population?time=1920..latest&country=~OWID_WRL"", ""children"": [{""text"": ""four times as many"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" people. That means the decline in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/natural-disasters?time=1900..latest&facet=none&Disaster+Type=All+disasters&Impact=Deaths&Timespan=Decadal+average&Per+capita=true&country=~OWID_WRL"", ""children"": [{""text"": ""death "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""children"": [{""url"": ""https://ourworldindata.org/explorers/natural-disasters?time=1900..latest&facet=none&Disaster+Type=All+disasters&Impact=Deaths&Timespan=Decadal+average&Per+capita=true&country=~OWID_WRL"", ""children"": [{""text"": ""rates"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""text"": "" has been even "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/natural-disasters?time=1900..latest&facet=none&Disaster+Type=All+disasters&Impact=Deaths&Timespan=Decadal+average&Per+capita=true&country=~OWID_WRL"", ""children"": [{""text"": ""more dramatic"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/explorers/natural-disasters?time=1900..latest&facet=none&Disaster+Type=All+disasters&Impact=Deaths&Timespan=Decadal+average&Per+capita=false&country=~OWID_WRL&hideControls=true&tab=chart"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Single events that caused over a million deaths were not rare in the first half of the 20th century. These events were mostly floods or droughts and were often linked to agricultural shocks that caused hunger and starvation. There are many similar examples in humanity’s history before the year 1900. A strong El-Niño weather event in the 1870s led to "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/Great_Famine_of_1876%E2%80%931878"", ""children"": [{""text"": ""severe famines"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" across China, India, and Brazil, killing tens of millions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To be clear, deaths haven’t declined so steeply because disasters are becoming less frequent or intense. This data also doesn’t mean climate change isn’t happening or isn’t worsening weather events. The main reason that fewer people are dying is that we’ve gotten better at protecting ourselves and each other."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we’ll see later, the toll of disasters depends on our physical environment, economic resources, political systems, technological advances, and cooperation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Massive "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/crop-yields"", ""children"": [{""text"": ""productivity improvements"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" have made agricultural systems far more resilient to shocks. Changes in political systems have reduced the risk of famine. As my colleagues Joe Hasell and Max Roser have shown, famines "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/famines#democracy-and-oppression"", ""children"": [{""text"": ""are rare"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in well-functioning democracies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Weather forecasts "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/weather-forecasts"", ""children"": [{""text"": ""have improved dramatically"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" over the past 50 years, and populations can better prepare for storms, floods, droughts, and wildfires. Many more countries have early warning systems."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And overall, people worldwide "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/economic-growth"", ""children"": [{""text"": ""are richer"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" than they were a century ago. It’s often the poorest who are most vulnerable to disasters. Having an earthquake-proof home, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-the-population-with-access-to-electricity?tab=chart&country=~OWID_WRL"", ""children"": [{""text"": ""access to electricity"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and clean water, air conditioning or heating, enough money to absorb shocks in energy or food prices, and resources to recover "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""after"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" a disaster means you can protect yourself. Billions of people have gained access to these basic resources over the last century."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Many researchers and organizations have noted this reduction in vulnerability to various types of disasters. The World Meteorological Organization "", ""spanType"": ""span-simple-text""}, {""url"": ""https://wmo.int/media/news/weather-related-disasters-increase-over-past-50-years-causing-more-damage-fewer-deaths"", ""children"": [{""text"": ""found that"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" deaths from climate and weather-related disasters decreased almost 3-fold from 1970 to 2019. Researchers Giuseppe Formetta and Luc Feyen "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.sciencedirect.com/science/article/pii/S0959378019300378"", ""children"": [{""text"": ""studied"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" a different disaster database — Munich RE’s NatCatSERVICE — and found a large reduction in global vulnerability to disasters from the 1980s to 2010s."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""“No such thing as a natural disaster”"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Three factors determine the risk of damage when a hazard hits."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""First, the characteristics of the"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" hazard"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" itself. Is it a flood, drought, hurricane, or heatwave? What’s its magnitude, speed, or power? And how long does it last? Is it a 30-minute downpour or a 5-day deluge of heavy rainfall?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Second, the number of people or the amount of infrastructure "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""exposed"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" to the hazard. Does the earthquake strike a densely populated city or a rural area? How many people live on a coastline inundated by storm surges or sea level rise? How much stuff — buildings, bridges, roads, and other infrastructure — is in harm’s way?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Third and finally, the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""vulnerability"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" of those who are exposed. A heatwave in Dubai will be less harmful than one in New Delhi because most people in Dubai have air conditioning. A strong earthquake in a country with quake-resistant infrastructure will be less damaging than the one that struck Haiti in 2010. Vulnerability is often strongly linked to income: poorer countries and communities tend to have fewer resources to protect themselves and respond afterward."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""narrow"", ""type"": ""image"", ""filename"": ""hazard-disaster-risk.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Disaster risk, then, sits at the center of all three. An increase in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""any"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of these dimensions increases the risk, while a reduction lowers it."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is why you might "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.undrr.org/news/sendai-framework-6th-anniversary-time-recognize-there-no-such-thing-natural-disaster-were"", ""children"": [{""text"": ""hear the phrase"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", “There’s no such thing as a natural disaster”. Hazards only become disasters when they impact societies and people. A hurricane, for example, is not a disaster until it hurts or kills people or destroys homes in its path."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How does this framework explain the dramatic decline in deaths from disasters over the last century?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""hazard"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" component has "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""not"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" declined. It has probably increased — on several dimensions — because of climate change."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Exposure"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" has not declined either. There are more than "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/population-growth?insight=the-world-population-has-increased-rapidly-over-the-last-few-centuries#key-insights"", ""children"": [{""text"": ""four times as many"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" people on the planet than a century ago. People have "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/urbanization"", ""children"": [{""text"": ""migrated to cities"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", often along coastlines, where events such as storm surges, cyclones, and flooding are more likely. That means more people in harm’s way. What has reduced, though, is "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""acute exposure"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" to some events. Better "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/weather-forecasts"", ""children"": [{""text"": ""weather prediction"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and early warning systems mean people can evacuate before a hazard hits."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What has declined is "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""vulnerability"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": a long list of improvements have made communities less vulnerable. Our agricultural systems are more productive and recover from damaging events. Political systems allow for national and international support before or after a hazard strikes. People live in better buildings. Some have heating or air conditioning to protect them from extreme temperatures."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How to reduce disaster risk going forward"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How should we think about these three factors going forward?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""First, we will never be able to stop "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""hazards"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" completely. There will never be an end to earthquakes, cyclones, or extreme rainfall. What we "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""can"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" determine is how much worse they get in a changing climate. Hazards at 3°C of warming will be worse than at 2°C, which will be worse than at 1.5°C. We need to reduce our "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/co2-and-greenhouse-gas-emissions"", ""children"": [{""text"": ""carbon emissions"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and avoid the worst impacts of climate change. The better we do here, the less exposed and vulnerable we will be."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But that alone won’t be enough. Regardless of how successful we are in reducing our emissions, climate change is already here, and the world will get warmer even on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.carbonbrief.org/analysis-what-the-new-ipcc-report-says-about-how-to-limit-warming-to-1-5c-or-2c/"", ""children"": [{""text"": ""our most ambitious"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" pathways. Societies will need to be more resilient to these changes, "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""and"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" they should be protected from events unrelated to climate change — the types of events that our ancestors were exposed to 50, 100, or 1,000 years ago."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To reduce "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""exposure"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "", we must understand when and where hazards are most likely to hit. We must understand which regions are most vulnerable to sea-level rise or wildfires. People are still moving into areas at serious risk in the future, exposing many of them to disruption and damage."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Increasing the coverage of early warning systems will help. When a hazard is imminent, people can evacuate and their exposure can be temporarily reduced. According to the World Meteorological Organization, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://documents1.worldbank.org/curated/en/099050123155016375/pdf/P1765160197f400b80947e0af8c48049151.pdf"", ""children"": [{""text"": ""around one-third of the world"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" — mostly in the poorest countries — does not have these systems. I’ve recently "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/weather-forecasts"", ""children"": [{""text"": ""written"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" about this."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Finally, there is a lot of room for reducing "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""vulnerabilities"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "". Poverty makes people most vulnerable; that’s why I argue that lifting people out of "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/poverty"", ""children"": [{""text"": ""poverty"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" is an essential solution to reducing climate risks. Investing in infrastructure, making agriculture more productive, and building strong political governance are all vital. Poorer countries "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""tend"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" to have higher death rates from disasters, as the chart below shows. (Note that this is on a logarithmic scale.)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/death-rates-disaster-gdp?time=2021"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This relationship is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/affected-by-disasters-vs-gdp"", ""children"": [{""text"": ""even stronger"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" for the total number of people "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""affected"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" by disasters, which includes those left homeless, injured, or requiring assistance. People in lower-income countries are much more vulnerable to disasters such as drought, which affects many more people. Poorer infrastructure also means they’re slower to respond and recover from disasters, leaving more people affected."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Richer countries can help and have committed to doing so with adaptation funds within the Paris Agreement on climate change."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One way to build more resilient communities is to learn from other countries, like Bangladesh, that have saved many lives with the right preparation and response. The problem is that we tend to see the large and fatal events where things have gone wrong and miss the small events where people were protected. Disasters hit the news; averted ones don’t."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Thankfully, some researchers are trying to change this. The project “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://disastersavoided.com/"", ""children"": [{""text"": ""Disasters Avoided"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""”, led by a team of disaster risk experts, tries to highlight case studies of events where disasters were prevented. As Ilan Kelman, one of the authors, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://theconversation.com/we-rarely-hear-about-the-disasters-that-were-avoided-but-theres-a-lot-we-can-learn-from-them-217850"", ""children"": [{""text"": ""writes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "": “We frequently see headlines about disasters. But where are the headlines covering the good news of lives saved and damage averted when disasters do not happen?”"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""To keep up with escalating climate change, we will need to move faster"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Over the last century, we have outpaced the impacts of climate change on natural disasters. Deaths have fallen "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""despite"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" climate change because we’ve built more resilient societies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Think of it like being in a race. Climate change has been jogging while we’ve been running. We’ve mostly stayed ahead, but there is no guarantee that things will stay that way. Slow down, and we’ll be overtaken. Stay at the same pace, and we’ll probably still be overtaken as the impacts of climate change accelerate."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we fail to invest in protection measures and development trends slow down, then the progress we’ve made over the last century could easily reverse, and disaster deaths could start to rise again."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What’s key, though, is that the direction of that trend — a continued fall or a reversal — is up to us."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""None of this will happen on its own. Bangladesh’s success was driven by local communities and investment in early warning systems. Chile and Japan’s "", ""spanType"": ""span-simple-text""}, {""url"": ""https://discovery.ucl.ac.uk/id/eprint/10093419/"", ""children"": [{""text"": ""resilience to earthquakes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" came from architects, engineers, and governments upholding strict building standards. The dramatic decline in famine came from technological revolutions in agriculture and populations pushing for political rights and accountability."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""International cooperation and support will be needed to ensure that the poorest and most vulnerable are not left behind."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""2450e66e10f9042c7b9d3bc52444f40174a51a77"": {""id"": ""2450e66e10f9042c7b9d3bc52444f40174a51a77"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""One caveat to keep in mind about this data. Data quality and completeness are lower the further we go back in time. I’ll cover these issues in more detail in an upcoming article. It’s mostly large events captured in the first half of the 20th century because smaller ones were often not recorded or reported. That means many smaller events — and the deaths they caused — are missing."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""2a80af495d0fabcc248835e80dd77f8d9b2ba3ab"": {""id"": ""2a80af495d0fabcc248835e80dd77f8d9b2ba3ab"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Formetta, G., & Feyen, L. (2019). Empirical evidence of declining global vulnerability to climate-related hazards. Global Environmental Change, 57, 101920."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3ff43deaceecc6a827cce1c20ebf199a3d4ada8a"": {""id"": ""3ff43deaceecc6a827cce1c20ebf199a3d4ada8a"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Note that there are some uncertainties around these estimates, especially when the indirect impacts of the disasters are considered. All estimates are in the range of hundreds of thousands of people."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f41b65fb85df85f5b976627081b856e215c21223"": {""id"": ""f41b65fb85df85f5b976627081b856e215c21223"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The latest report from the Intergovernmental Panel on Climate Change (IPCC) "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/"", ""children"": [{""text"": ""notes that"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" the frequency and intensity of heatwave events have likely increased due to climate change. Heavy precipitation events have likely increased, particularly across Europe, North America, and Asia. Agricultural droughts have likely increased in some regions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""The world has become more resilient to disasters, but investment is needed to save more lives"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""Deaths from disasters have fallen, but we need to build even more resilience to ensure this progress doesn’t reverse."", ""subtitle"": ""Deaths from disasters have fallen, but we need to build even more resilience to ensure this progress doesn’t reverse."", ""featured-image"": ""global-disaster-featured-image.png""}",1,2024-04-16 10:56:51,2024-05-20 08:21:34,2024-05-13 11:12:32,listed,ALBJ4LusJpxW2CuE7tdnFW1JRIuP30gfT99Lpkq5XWFd6Ht4TINNbO-uAcx3EQaXg0Qm5P2-NIzTflPvmX8OfQ,,"In 1970, more than 300,000 people died when [a strong cyclone](https://www.thebritishacademy.ac.uk/documents/3537/JBA-9s9-04-Sammonds-etal.pdf) hit the coast of Bangladesh.1 In 1985, another storm caused 15,000 deaths. Just six years later, another killed 140,000. Fast-forward to 2020. Bangladesh was hit by cyclone Amphan, one of the strongest storms on record in the Bay of Bengal. The death toll was 26 — barely visible on the chart below, compared to these very deadly disasters That’s 26 too many deaths, and the cyclone also caused huge amounts of damage: millions of people were displaced, and there were large economic losses. But tens — possibly hundreds — of thousands of lives were saved through early warnings, evacuations, and increased resilience. People in Bangladesh are much better protected from disasters than they were a few decades ago. This development is part of a longer-term and widespread success in reducing humanity’s vulnerability to storms, floods, earthquakes, and other hazards. # The world has become more resilient to disasters Bangladesh is not an isolated example. We can observe long-term improvements in the resilience of the world as a whole. Here, I will look at data published by the International Disaster Database, [EM-DAT](https://www.emdat.be/), which stretches back to 1900. In the chart below, I’ve shown the number of deaths from disasters, given as the _decadal_ average. This is helpful as there is a lot of volatility in disasters from year to year.2 You can also explore this data [annually](https://ourworldindata.org/explorers/natural-disasters?time=1900..latest&facet=none&Disaster+Type=All+disasters&Impact=Deaths&Timespan=Annual&Per+capita=false&country=~OWID_WRL). The number of people killed in disasters has fallen a lot over the last century. That’s despite there being [four times as many](https://ourworldindata.org/grapher/population?time=1920..latest&country=~OWID_WRL) people. That means the decline in [death ](https://ourworldindata.org/explorers/natural-disasters?time=1900..latest&facet=none&Disaster+Type=All+disasters&Impact=Deaths&Timespan=Decadal+average&Per+capita=true&country=~OWID_WRL)_[rates](https://ourworldindata.org/explorers/natural-disasters?time=1900..latest&facet=none&Disaster+Type=All+disasters&Impact=Deaths&Timespan=Decadal+average&Per+capita=true&country=~OWID_WRL)_ has been even [more dramatic](https://ourworldindata.org/explorers/natural-disasters?time=1900..latest&facet=none&Disaster+Type=All+disasters&Impact=Deaths&Timespan=Decadal+average&Per+capita=true&country=~OWID_WRL). Single events that caused over a million deaths were not rare in the first half of the 20th century. These events were mostly floods or droughts and were often linked to agricultural shocks that caused hunger and starvation. There are many similar examples in humanity’s history before the year 1900. A strong El-Niño weather event in the 1870s led to [severe famines](https://en.wikipedia.org/wiki/Great_Famine_of_1876%E2%80%931878) across China, India, and Brazil, killing tens of millions. To be clear, deaths haven’t declined so steeply because disasters are becoming less frequent or intense. This data also doesn’t mean climate change isn’t happening or isn’t worsening weather events. The main reason that fewer people are dying is that we’ve gotten better at protecting ourselves and each other. As we’ll see later, the toll of disasters depends on our physical environment, economic resources, political systems, technological advances, and cooperation. Agricultural systems are far more resilient to shocks because of [massive improvements](https://ourworldindata.org/crop-yields) in productivity. Changes in political systems have reduced the risk of famine. As my colleagues Joe Hasell and Max Roser have shown, famines [are rare](https://ourworldindata.org/famines#democracy-and-oppression) in well-functioning democracies. Weather forecasts [have improved dramatically](https://ourworldindata.org/weather-forecasts) over the past 50 years, and populations can better prepare for storms, floods, droughts, and wildfires. Many more countries have early warning systems. And overall, people worldwide [are richer](https://ourworldindata.org/economic-growth) than they were a century ago. It’s often the poorest who are most vulnerable to disasters. Having an earthquake-proof home, [access to electricity](https://ourworldindata.org/grapher/share-of-the-population-with-access-to-electricity?tab=chart&country=~OWID_WRL) and clean water, air conditioning or heating, enough money to absorb shocks in energy or food prices, and resources to recover _after_ a disaster means you can protect yourself. Billions of people have gained access to these basic resources over the last century. Many researchers and organizations have noted this reduction in vulnerability to various types of disasters. The World Meteorological Organization [found that](https://wmo.int/media/news/weather-related-disasters-increase-over-past-50-years-causing-more-damage-fewer-deaths) deaths from climate and weather-related disasters decreased almost 3-fold from 1970 to 2019. Researchers Giuseppe Formetta and Luc Feyen [studied](https://www.sciencedirect.com/science/article/pii/S0959378019300378) a different disaster database — Munich RE’s NatCatSERVICE — and also found a large reduction in global vulnerability to disasters from the 1980s to 2010s.3 # “No such thing as a natural disaster” Three factors determine the risk of damage when a hazard hits. First, the characteristics of the** hazard** itself. Is it a flood, drought, hurricane, or heatwave? What’s its magnitude, speed, or power? And how long does it last: is it a 30-minute downpour or a 5-day deluge of heavy rainfall? Second, the number of people or the amount of infrastructure **exposed** to the hazard. Does the earthquake strike a densely-populated city or a rural area? How many people live on a coastline that’s inundated by storm surges or sea level rise? How much stuff — buildings, bridges, roads, and other infrastructure — is in harm’s way? Third and finally, the **vulnerability** of those who are exposed. A heatwave in Dubai will be less harmful than one in New Delhi, because most people in Dubai have air conditioning. A strong earthquake in a country with quake-resistant infrastructure will be less damaging than the one that struck Haiti in 2010. Vulnerability is often strongly linked to income: poorer countries and communities tend to have fewer resources to protect themselves and respond afterward. Disaster risk, then, sits at the center of all three. An increase in _any_ of these dimensions increases the risk, while a reduction lowers it. This is why you might [hear the phrase](https://www.undrr.org/news/sendai-framework-6th-anniversary-time-recognize-there-no-such-thing-natural-disaster-were), “There’s no such thing as a natural disaster”. Hazards only become disasters when they impact societies and people. A hurricane, for example, is not a disaster until it hurts or kills people or destroys homes in its path. How does this framework explain the dramatic decline in deaths from disasters over the last century? The **hazard** component has _not_ declined. It has probably increased — on several dimensions — because of climate change.4 **Exposure** has not declined either. There are more than [four times as many](https://ourworldindata.org/population-growth?insight=the-world-population-has-increased-rapidly-over-the-last-few-centuries#key-insights) people on the planet than a century ago. People have [migrated to cities](https://ourworldindata.org/urbanization), often along coastlines, where events such as storm surges, cyclones, and flooding are more likely. That means more people in harm’s way. What has reduced, though, is _acute exposure_ to some events. Better [weather prediction](https://ourworldindata.org/weather-forecasts) and early warning systems mean people can evacuate before a hazard hits. What has declined is **vulnerability**: a long list of improvements have made communities less vulnerable. Our agricultural systems are more productive and recover from damaging events. Political systems allow for national and international support before or after a hazard strikes. People live in better buildings. Some have heating or air conditioning to protect them from extreme temperatures. # How to reduce disaster risk going forward How should we think about these three factors going forward? First, we will never be able to stop **hazards** completely. There will never be an end to earthquakes, cyclones, or extreme rainfall. What we _can_ determine is how much worse they get in a changing climate. Hazards at 3°C of warming will be worse than at 2°C, which will be worse than at 1.5°C. We need to reduce our [carbon emissions](https://ourworldindata.org/co2-and-greenhouse-gas-emissions) and avoid the worst impacts of climate change. The better we do here, the less exposed and vulnerable we will be. But that alone won’t be enough. Regardless of how successful we are in reducing our emissions, climate change is already here, and the world will get warmer even on [our most ambitious](https://www.carbonbrief.org/analysis-what-the-new-ipcc-report-says-about-how-to-limit-warming-to-1-5c-or-2c/) pathways. Societies will need to be more resilient to these changes, _and_ they should be protected from events unrelated to climate change — the types of events that our ancestors were exposed to 50, 100, or 1,000 years ago. To reduce **exposure**, we must understand when and where hazards are most likely to hit. We need to understand which regions are most vulnerable to sea-level rise or wildfires. People are still moving into areas at serious risk in the future, exposing many of them to disruption and damage. Increasing the coverage of early warning systems will help. When a hazard is imminent, people can evacuate, and their exposure can be temporarily reduced. According to the World Meteorological Organization, [around one-third of the world](https://documents1.worldbank.org/curated/en/099050123155016375/pdf/P1765160197f400b80947e0af8c48049151.pdf) — mostly in the poorest countries — does not have these systems. I’ve recently [written](https://ourworldindata.org/weather-forecasts) about this. Finally, there is a lot of room for reducing **vulnerabilities**. Poverty makes people most vulnerable; that’s why I argue that lifting people out of [poverty](http://ourworldindata.org/poverty) is an essential solution to reducing climate risks. Investing in infrastructure, making agriculture more productive, and building strong political governance are all vital. Poorer countries _tend_ to have higher death rates from disasters, as the chart below shows. (Note that this is on a logarithmic scale.) This relationship is [even stronger](https://ourworldindata.org/grapher/affected-by-disasters-vs-gdp) for the total number of people _affected_ by disasters, which includes those left homeless, injured, or requiring assistance. People in lower-income countries are much more vulnerable to disasters such as drought, which affects many more people. Poorer infrastructure also means they’re slower to respond and recover from disasters, leaving more people affected. Richer countries can help and have committed to doing so with adaptation funds within the Paris Agreement on climate change. One way to build more resilient communities is to learn from other countries, like Bangladesh, that have saved many lives with the right preparation and response. The problem is that we tend to see the large and fatal events where things have gone wrong and miss the small events where people were protected. Disasters hit the news; averted ones don’t. Thankfully, some researchers are trying to change this. The project “[Disasters Avoided](https://disastersavoided.com/)”, led by a team of disaster risk experts, tries to highlight case studies of events where disasters were prevented. As Ilan Kelman, one of the authors, [writes](https://theconversation.com/we-rarely-hear-about-the-disasters-that-were-avoided-but-theres-a-lot-we-can-learn-from-them-217850): “We frequently see headlines about disasters. But where are the headlines covering the good news of lives saved and damage averted when disasters do not happen?” # To keep up with escalating climate change, we will need to move faster Over the last century, we have outpaced the impacts of climate change on natural disasters. Deaths have fallen _despite_ climate change because we’ve built more resilient societies. Think of it like being in a race. Climate change has been jogging while we’ve been running. We’ve mostly stayed ahead. But there is no guarantee that things stay that way. Slow down, and we’ll be overtaken. Stay at the same pace, and we’ll probably still be overtaken as the impacts of climate change accelerate. If we fail to invest in protection measures and development trends slow down, then the progress we’ve made over the last century could easily reverse, and disaster deaths could start to rise again. What’s key, though, is that the direction of that trend — a continued fall or a reversal — is up to us. None of this will happen on its own. Bangladesh’s success was driven by local communities and investment in early warning systems. Chile and Japan’s [resilience to earthquakes](https://discovery.ucl.ac.uk/id/eprint/10093419/) came from architects, engineers, and governments upholding strict building standards. The dramatic decline in famine came from technological revolutions in agriculture and populations pushing for political rights and accountability. International cooperation and support will be needed to ensure that the poorest and most vulnerable are not left behind. Note that there are some uncertainties around these estimates, especially when the indirect impacts of the disasters are considered. All estimates are in the range of hundreds of thousands of people. One caveat to keep in mind about this data. Data quality and completeness are lower the further we go back in time. I’ll cover these issues in more detail in an upcoming article. It’s mostly large events captured in the first half of the 20th century because smaller ones were often not recorded or reported. That means many smaller events — and the deaths they caused — are missing. Formetta, G., & Feyen, L. (2019). Empirical evidence of declining global vulnerability to climate-related hazards. Global Environmental Change, 57, 101920. The latest report from the Intergovernmental Panel on Climate Change (IPCC) [notes that](https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/) the frequency and intensity of heatwave events have likely increased due to climate change. Heavy precipitation events have likely increased, particularly across Europe, North America, and Asia. Agricultural droughts have likely increased in some regions.","The world has become more resilient to disasters, but investment is needed to save more lives" 1zgY4yOurKwC1NSioTP6kFDEJP39Cc6ndWUqk4x-fIug,human-rights,topic-page,"{""toc"": [], ""body"": [{""type"": ""topic-page-intro"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Human rights are rights that all people have, regardless of their country, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://docs.google.com/document/d/195wtinOB2zsiGevmvUfAGPZv4NrG4jY_vpib49e5gt4/edit"", ""children"": [{""text"": ""gender"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://docs.google.com/document/d/1RvMWl3OpP6zrygM49jcvI6SotoWWq5er14gLBnPLxUg/edit"", ""children"": [{""text"": ""sexuality"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", ethnicity, or any other trait."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Among others, this includes: physical integrity rights, such as not being killed or tortured; civil rights, such as practicing their religion and moving freely in their country; and political rights, such as freedoms of speech and association."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The protection of these rights allows people to live the lives they want and to thrive in them."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Human rights have become much more protected, but this varies a lot between countries. 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The chart shows — based on data from researcher Kristopher Velasco — that there were very few countries that protected core LGBT+ rights beyond the right to engage in same-sex sexual acts. There were no countries where same-sex partners could marry or adopt. None where a third gender was recognized, and only two where the gender marker could be legally changed with ease."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Over the last few decades, LGBT+ rights have become more protected in many countries. Same-sex sexual acts are now legal in most countries, and same-sex marriage and adoption, third-gender recognition, and gender marker changes are slowly expanding across countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Yet, most countries still fail to acknowledge these core rights. Some have even recently implemented policies actively restricting rights, such as "", ""spanType"": ""span-simple-text""}, {""url"": ""http://marriage-for-same-sex-partners-velasco"", ""children"": [{""text"": ""explicitly banning same-sex marriages"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Even in countries that legally protect these rights, LGBT+ people are still discriminated against in their daily lives, such as by "", ""spanType"": ""span-simple-text""}, {""url"": ""https://journals.sagepub.com/doi/abs/10.1177/1524838018757749?journalCode=tvaa"", ""children"": [{""text"": ""disproportionately being subject to violence"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" or "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.sciencedirect.com/science/article/abs/pii/S0140673616006838"", ""children"": [{""text"": ""struggling to access adequate healthcare"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The trends and differences between countries look similar if we look at the more recent data from "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.equaldex.com/"", ""children"": [{""text"": ""Equaldex"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" on same-sex "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/same-sex-sexual-acts-equaldex"", ""children"": [{""text"": ""sexual acts"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/marriage-same-sex-partners-equaldex"", ""children"": [{""text"": ""marriage"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/right-to-change-legal-gender-equaldex"", ""children"": [{""text"": ""gender marker changes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We have an article that discusses the trends in more detail:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/14o4qiXQqQHuJiQNTjrmxP28VRjL9EVqDYRxXJjbRMkY/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""text"": [{""text"": [{""text"": ""What you should know about this data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 5, ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""We rely on data from "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.krisvelasco.com/data-projects"", ""children"": [{""text"": ""Kristopher Velasco"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "", an Assistant Professor in the Department of Sociology at Princeton University, to measure LGBT+ rights."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We do not include countries if they only partially protected these rights, such as when laws differ across the country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}]}], ""parseErrors"": []}, {""rows"": [], ""type"": ""research-and-writing"", ""primary"": [{""value"": {""url"": ""https://docs.google.com/document/d/1GUDeJCKx4xDIxo-BNrCf1m1wiuAaqIv7RA1EXwsWSDQ/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/14o4qiXQqQHuJiQNTjrmxP28VRjL9EVqDYRxXJjbRMkY/edit""}}], ""secondary"": [], ""parseErrors"": [], ""hide-authors"": false}, {""top"": [{""url"": ""https://ourworldindata.org/grapher/human-rights-index-vdem""}], ""type"": ""all-charts"", ""heading"": ""Interactive Charts on Human Rights"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""05f8f22e35041d62bf5d974e8c8b490b2e4c55ca"": {""id"": ""05f8f22e35041d62bf5d974e8c8b490b2e4c55ca"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The score jumps in 1900 because V-Dem covers many more countries since then (which often were colonies at first)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""11ffc9f252979da66e4d9c1103720dc50ec7f022"": {""id"": ""11ffc9f252979da66e4d9c1103720dc50ec7f022"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Oskar Rydén, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson and Daniel Ziblatt. 2023."", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Coppedge%2C+Michael%2C+John+Gerring%2C+Carl+Henrik+Knutsen%2C+Staffan+I.+Lindberg%2C+Jan+Teorell%2C+David+Altman%2C+Michael+Bernhard%2C+Agnes+Cornell%2C+M.+Steven+Fish%2C+Lisa+Gastaldi%2C+Haakon+Gjerl%C3%B8w%2C+Adam+Glynn%2C+Ana+Good+God%2C+Sandra+Grahn%2C+Allen+Hicken%2C+Katrin+Kinzelbach%2C+Joshua+Krusell%2C+Kyle+L.+Marquardt%2C+Kelly+McMann%2C+Valeriya+Mechkova%2C+Juraj+Medzihorsky%2C+Natalia+Natsika%2C+Anja+Neundorf%2C+Pamela+Paxton%2C+Daniel+Pemstein%2C+Josefine+Pernes%2C+Oskar+Ryd%C3%A9n%2C+Johannes+von+R%C3%B6mer%2C+Brigitte+Seim%2C+Rachel+Sigman%2C+Svend-Erik+Skaaning%2C+Jeffrey+Staton%2C+Aksel+Sundstr%C3%B6m%2C+Eitan+Tzelgov%2C+Yi-ting+Wang%2C+Tore+Wig%2C+Steven+Wilson+and+Daniel+Ziblatt.+2023.+V-Dem+%5BCountry-Year%2FCountry-Date%5D+Dataset+v13.+Varieties+of+Democracy+%28V-Dem%29+Project.&btnG="", ""children"": [{""text"": "" V-Dem [Country-Year/Country-Date] Dataset v13."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" Varieties of Democracy (V-Dem) Project."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""2e8e29a7bb686c481ff36406438f355545fbc0e4"": {""id"": ""2e8e29a7bb686c481ff36406438f355545fbc0e4"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Velasco, Kristopher. 2020."", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Transnational+Backlash+and+the+Deinstitutionalization+of+Liberal+Norms%3A+LGBT%2B+Rights+in+a+Contested+World&btnG="", ""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""url"": ""https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Transnational+Backlash+and+the+Deinstitutionalization+of+Liberal+Norms%3A+LGBT%2B+Rights+in+a+Contested+World&btnG="", ""children"": [{""text"": ""Transnational Backlash and the Deinstitutionalization of Liberal Norms: LGBT+ Rights in a Contested World"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""fc391394dd2120e41601ac7af6e5f668a29481fc"": {""id"": ""fc391394dd2120e41601ac7af6e5f668a29481fc"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This means that populous countries matter more when calculating the average than countries with small populations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""topic-page"", ""title"": ""Human Rights"", ""authors"": [""Bastian Herre"", ""Pablo Arriagada"", ""Max Roser""], ""excerpt"": ""How has the protection of human rights changed over time? How does it differ across countries, and between social groups? Explore global data on human rights."", ""sticky-nav"": [{""text"": ""Introduction"", ""target"": ""#introduction""}, {""text"": ""Key Insights"", ""target"": ""#key-insights""}, {""text"": ""Research & Writing"", ""target"": ""#research-writing""}, {""text"": ""Charts"", ""target"": ""#all-charts""}, {""text"": ""Endnotes"", ""target"": ""#article-endnotes""}, {""text"": ""Cite This Work"", ""target"": ""#article-citation""}, {""text"": ""Reuse This Work"", ""target"": ""#article-licence""}], ""featured-image"": ""human-rights-featured-image.png""}",1,2023-07-18 19:45:54,2016-06-12 21:46:00,2023-12-28 16:31:13,unlisted,ALBJ4LutxW9ONrHfjeBjtyE0guN6UZsznQw4-e91WRXf3Hb89U2Jnb4iDuyh0Wve179DLVNLWiqwgeIL3iqWXQ,,"Human rights are rights that all people have, regardless of their country, gender, sexuality, ethnicity, or any other trait. Among others, this includes: physical integrity rights, such as not being killed or tortured; civil rights, such as practicing their religion and moving freely in their country; and political rights, such as freedoms of speech and association. The protection of these rights allows people to live the lives they want and to thrive in them. Human rights have become much more protected, but this varies a lot between countries. And not everyone enjoys the same protections: people are often marginalized because of their gender, sexuality, or ethnicity. On this page, you can find data, visualizations, and writing on how the protection of human rights has changed over time, how it differs across countries, and how it varies between people of different genders, sexualities, and ethnicities. We have additional topic pages related to people’s economic and social rights, such as on [food](https://ourworldindata.org/hunger-and-undernourishment), [health](https://ourworldindata.org/health-meta), and [education](https://ourworldindata.org/global-education). ## Key Insights on Human Rights ### Human rights have become much more protected around the world Human rights are much better protected than they were one or two hundred years ago. In the late 18th century, human rights were poorly protected. In many countries, people’s [physical integrity](https://ourworldindata.org/grapher/physical-integrity-rights-vdem?tab=chart&country=~OWID_WRL) and [private](https://ourworldindata.org/grapher/private-civil-liberties?tab=chart&country=~OWID_WRL) and [political](https://ourworldindata.org/grapher/political-civil-liberties?tab=chart&country=~OWID_WRL) civil liberties were not respected by their governments. The chart shows — based on data from Varieties of Democracy — that countries received an average score of only 0.3, on a scale from 0 to 1 (most rights). Importantly, this early data does not include many countries that were colonies before 1900. There, human rights were likely protected even less. During the 19th century, the protection of human rights slowly improved. But in the first half of the 20th century, some of this progress was undone, particularly during the World Wars. The protection of human rights then improved massively in the second half of the 20th century. However, this progress has not been linear: there have been setbacks in the 1970s and recent years. Despite these setbacks, human rights remain much more protected than only half a century ago. The changes over time look broadly similar if we instead look at [the extent to which people‘s human rights are protected](https://ourworldindata.org/grapher/human-rights-popw?country=~OWID_WRL) by weighing a country’s human rights score by its population.1 ### Human rights are much more protected in some countries than in others There are large differences across countries in the extent to which human rights are protected. The chart shows the distribution of the latest human rights scores — using data from the Varieties of Democracy (V-Dem) project — across the world. Countries with high scores have well-protected human rights. This especially includes countries in Europe and the Americas. But there are some countries across all regions that protect their inhabitants’ human rights well. In countries where human rights are not protected, political violence lingers, people’s rights to their property can be tenuous, and their ability to move around the country and abroad, practice their religion, and voice their opinions and organize are limited or outright oppressed. These countries are concentrated in Africa and Asia. While large differences between countries remain, many now protect human rights well, and almost all protect human rights much more than they did in the past. ### Women’s rights are now much more protected, but there are big differences between countries Women’s rights are much better protected than they were even 50 years ago. In the late 18th century, women’s rights were poorly protected. In most countries, women did not enjoy [civil liberties](https://ourworldindata.org/grapher/women-civil-liberties?tab=chart&country=~OWID_WRL), could not participate in [civil society](https://ourworldindata.org/grapher/women-civil-society-participation?tab=chart&country=~OWID_WRL), and were not represented in [politics](https://ourworldindata.org/grapher/women-political-participation-index). The chart shows that the global average score for women’s political empowerment — based on data from Varieties of Democracy (V-Dem) — was just 0.12 on the scale from 0 to 1 (with 1 being the highest). The protection of women’s rights did not change much over the 19th century. It became slightly easier for women to access the justice system, and they could discuss political issues more freely, but strong limits on their civil rights and participation in society and politics remained.3 These rights became much better protected in the 20th century, especially in the second half. Women became freer to move around their countries and abroad and to participate in civil society. They gained the [right to vote](https://ourworldindata.org/grapher/countries-with-universal-suffrage) and [seats in parliament](https://ourworldindata.org/grapher/countries-by-share-of-women-in-parliament) in almost all countries, as well as access to senior government offices, up to the [chief executive](https://ourworldindata.org/grapher/countries-with-woman-chief-executive) level. These rights became much better protected in the 20th century, especially in the second half. Women became freer to move around their countries and abroad and to participate in civil society. They gained the [right to vote](https://ourworldindata.org/grapher/countries-with-universal-suffrage) and [seats in parliament](https://ourworldindata.org/grapher/countries-by-share-of-women-in-parliament) in almost all countries, as well as access to senior government offices, up to the [chief executive](https://ourworldindata.org/grapher/countries-with-woman-chief-executive) level. This looks similar if we consider [the extent to which women’s rights are protected](https://ourworldindata.org/grapher/women-political-empowerment-popw) by weighing a country’s score by its population.1 But this progress has been uneven and limited. There are [large differences](https://ourworldindata.org/grapher/distribution-of-women-political-empowerment) in the protection of women’s rights across countries. Even in countries that score highly on this index, women’s rights are not protected to the same extent as the rights of men: women are more frequently subjected to [sexual violence](https://ourworldindata.org/grapher/population-subjected-to-sexual-violence-male-vs-female), receive [less pay for the same work](https://ourworldindata.org/grapher/gender-wage-gap-oecd?country=AUS~FRA~ITA~JPN~SWE~GBR~USA), and remain underrepresented in [parliaments](https://ourworldindata.org/grapher/share-of-women-in-parliament), [ministries](https://ourworldindata.org/grapher/share-of-women-in-ministerial-positions), and among [chief executives](https://ourworldindata.org/grapher/woman-is-chief-executive). ### LGBT+ people’s rights have become more protected in some countries The rights of lesbian, gay, bisexual, transgender, and other people outside traditional sexuality and gender categories have become better protected in some countries. In the early 1990s, few LGBT+ rights were protected. The chart shows — based on data from researcher Kristopher Velasco — that there were very few countries that protected core LGBT+ rights beyond the right to engage in same-sex sexual acts. There were no countries where same-sex partners could marry or adopt. None where a third gender was recognized, and only two where the gender marker could be legally changed with ease. Over the last few decades, LGBT+ rights have become more protected in many countries. Same-sex sexual acts are now legal in most countries, and same-sex marriage and adoption, third-gender recognition, and gender marker changes are slowly expanding across countries. Yet, most countries still fail to acknowledge these core rights. Some have even recently implemented policies actively restricting rights, such as [explicitly banning same-sex marriages](https://ourworldindata.org/grapher/marriage-for-same-sex-partners-banned?time=2019). Even in countries that legally protect these rights, LGBT+ people are still discriminated against in their daily lives, such as by [disproportionately being subject to violence](https://journals.sagepub.com/doi/abs/10.1177/1524838018757749?journalCode=tvaa) or [struggling to access adequate healthcare](https://www.sciencedirect.com/science/article/abs/pii/S0140673616006838). The trends and differences between countries look similar if we look at the most recent data for 2023 from [Equaldex](https://www.equaldex.com/) on same-sex [sexual acts](https://ourworldindata.org/grapher/same-sex-sexual-acts-equaldex), [marriage](https://ourworldindata.org/grapher/marriage-same-sex-partners-equaldex), and [gender marker changes](https://ourworldindata.org/grapher/right-to-change-legal-gender-equaldex). The score jumps in 1900 because V-Dem covers many more countries since then (which often were colonies at first). Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Oskar Rydén, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson and Daniel Ziblatt. 2023.[ V-Dem [Country-Year/Country-Date] Dataset v13.](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Coppedge%2C+Michael%2C+John+Gerring%2C+Carl+Henrik+Knutsen%2C+Staffan+I.+Lindberg%2C+Jan+Teorell%2C+David+Altman%2C+Michael+Bernhard%2C+Agnes+Cornell%2C+M.+Steven+Fish%2C+Lisa+Gastaldi%2C+Haakon+Gjerl%C3%B8w%2C+Adam+Glynn%2C+Ana+Good+God%2C+Sandra+Grahn%2C+Allen+Hicken%2C+Katrin+Kinzelbach%2C+Joshua+Krusell%2C+Kyle+L.+Marquardt%2C+Kelly+McMann%2C+Valeriya+Mechkova%2C+Juraj+Medzihorsky%2C+Natalia+Natsika%2C+Anja+Neundorf%2C+Pamela+Paxton%2C+Daniel+Pemstein%2C+Josefine+Pernes%2C+Oskar+Ryd%C3%A9n%2C+Johannes+von+R%C3%B6mer%2C+Brigitte+Seim%2C+Rachel+Sigman%2C+Svend-Erik+Skaaning%2C+Jeffrey+Staton%2C+Aksel+Sundstr%C3%B6m%2C+Eitan+Tzelgov%2C+Yi-ting+Wang%2C+Tore+Wig%2C+Steven+Wilson+and+Daniel+Ziblatt.+2023.+V-Dem+%5BCountry-Year%2FCountry-Date%5D+Dataset+v13.+Varieties+of+Democracy+%28V-Dem%29+Project.&btnG=) Varieties of Democracy (V-Dem) Project. Velasco, Kristopher. 2020.[ ](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Transnational+Backlash+and+the+Deinstitutionalization+of+Liberal+Norms%3A+LGBT%2B+Rights+in+a+Contested+World&btnG=)[Transnational Backlash and the Deinstitutionalization of Liberal Norms: LGBT+ Rights in a Contested World](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Transnational+Backlash+and+the+Deinstitutionalization+of+Liberal+Norms%3A+LGBT%2B+Rights+in+a+Contested+World&btnG=). This means that populous countries matter more when calculating the average than countries with small populations.",Human Rights 1zcGqgmvg4pEdiPqHMj8eY89PtKHcUlLO3dgL8moT6Bg,research-and-development,topic-page,"{""toc"": [], ""body"": [{""type"": ""topic-page-intro"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Research and development underpin nearly all the transformative changes we see on Our World in Data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Cures for diseases, "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/vaccination"", ""children"": [{""text"": ""vaccines"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and techniques to prevent infection have helped us survive "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/child-mortality"", ""children"": [{""text"": ""beyond childhood"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and live much "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/life-expectancy"", ""children"": [{""text"": ""longer lives"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Understanding hygiene, "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/clean-water-sanitation"", ""children"": [{""text"": ""water, and sanitation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" has saved countless lives from preventable diseases."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Electricity, artificial light, transport, and other "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/energy"", ""children"": [{""text"": ""energy technologies"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" have transformed our lives. Agricultural research has broken deadlocks in "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/crop-yields"", ""children"": [{""text"": ""crop yields"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and allowed us to produce enough food for eight billion people."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Even beyond the long list of technological advances, research into effective political and economic systems, human rights, and social sciences have reshaped societies worldwide."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""More research is needed to address our largest problems — old and new. We will need innovations in clean energy to tackle climate change, agriculture to feed a growing population, and developments in medical research to tackle existing and prevent new diseases. Research is vital to address emerging and ongoing risks such as "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/artificial-intelligence"", ""children"": [{""text"": ""artificial intelligence"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/nuclear-weapons"", ""children"": [{""text"": ""nuclear weapons"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This page contains all of our data, visualizations, and writing on research, development, and innovation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": [], ""relatedTopics"": [{""url"": ""https://ourworldindata.org/technological-change"", ""text"": ""Technological Progress"", ""type"": ""topic-page-intro-related-topic""}, {""url"": ""https://ourworldindata.org/internet"", ""text"": ""Internet"", ""type"": ""topic-page-intro-related-topic""}, {""url"": ""https://ourworldindata.org/global-education"", ""text"": ""Global Education"", ""type"": ""topic-page-intro-related-topic""}, {""url"": ""http://ourworldindata.org/artificial-intelligence"", ""text"": ""Artificial Intelligence"", ""type"": ""topic-page-intro-related-topic""}]}, {""rows"": [], ""type"": ""research-and-writing"", ""heading"": ""Research & Writing"", ""primary"": [{""value"": {""url"": ""https://docs.google.com/document/d/1Vuyi9thca39s45htjDn6QSIgygZVcWuPca1y2pyVReU/edit""}}], ""secondary"": [{""value"": {""url"": ""https://docs.google.com/document/d/1oh0rTB9nZPq00zIJVaHzGC87AiJcjsV8DmYI3eDXFr8/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1yH7P70MBCWsNIx-WQPmZnVqXL4Mrpxrp6J1Jpa6qBko/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1WGJaY95A4hVjybBPzHOA80iGygKUKcgx0kSs-G_b7aA/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/15hEchniGhmgO8mLsSi5mXek2a9na-Mo1VFtrsWcQw1g/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1e0HDWjfdRTrbLlAdUMPGtkqmLJJFw9ookSmgBU6JMPI/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1KeagpzX2OMBS63fnyFRXMbU7QpBIgykKRZBTMdeHFAo/edit""}}], ""parseErrors"": [], ""hide-authors"": false}, {""top"": [], ""type"": ""all-charts"", ""heading"": ""Interactive Charts on Research and Development"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""topic-page"", ""title"": ""Research and Development"", ""authors"": [""Hannah Ritchie"", ""Edouard Mathieu"", ""Max Roser""], ""excerpt"": ""Research and development underpin nearly all of the transformative changes the world has seen."", ""dateline"": ""January 18, 2023"", ""subtitle"": ""Research and development underpin nearly all of the transformative changes the world has seen."", ""sticky-nav"": [{""text"": ""Introduction"", ""target"": ""#introduction""}, {""text"": ""Research & Writing"", ""target"": ""#research-writing""}, {""text"": ""Charts"", ""target"": ""#all-charts""}, {""text"": ""Cite This Work"", ""target"": ""#article-citation""}, {""text"": ""Reuse This Work"", ""target"": ""#article-licence""}], ""sidebar-toc"": false, ""featured-image"": ""research-and-development-thumbnail.png""}",1,2024-06-12 12:36:38,2023-01-18 13:54:15,2024-06-12 12:44:28,unlisted,ALBJ4LtIZSVTtC3bv8RMnnh2eetzlEs8ZprydFBoy-NOFGAXaSDwO7iZLUYiuiT16-9wiuW-6YiMx1toXW596Q,,"Research and development underpin nearly all the transformative changes we see on Our World in Data. Cures for diseases, [vaccines](http://ourworldindata.org/vaccination), and techniques to prevent infection have helped us survive [beyond childhood](http://ourworldindata.org/child-mortality) and live much [longer lives](http://ourworldindata.org/life-expectancy). Understanding hygiene, [water, and sanitation](http://ourworldindata.org/clean-water-sanitation) has saved countless lives from preventable diseases. Electricity, artificial light, transport, and other [energy technologies](http://ourworldindata.org/energy) have transformed our lives. Agricultural research has broken deadlocks in [crop yields](http://ourworldindata.org/crop-yields) and allowed us to produce enough food for eight billion people. Even beyond the long list of technological advances, research into effective political and economic systems, human rights, and social sciences have reshaped societies worldwide. More research is needed to address our largest problems – old and new. We will need innovations in clean energy to tackle climate change, agriculture to feed a growing population, and developments in medical research to tackle existing and prevent new diseases. Research is vital to address emerging and ongoing risks such as [artificial intelligence](http://ourworldindata.org/artificial-intelligence) and [nuclear weapons](http://ourworldindata.org/nuclear-weapons). This page contains all of our data, visualizations, and writing on research, development, and innovation. ## Research & Writing * https://docs.google.com/document/d/1Vuyi9thca39s45htjDn6QSIgygZVcWuPca1y2pyVReU/edit ,* https://docs.google.com/document/d/1oh0rTB9nZPq00zIJVaHzGC87AiJcjsV8DmYI3eDXFr8/edit ,* https://docs.google.com/document/d/1yH7P70MBCWsNIx-WQPmZnVqXL4Mrpxrp6J1Jpa6qBko/edit ,* https://docs.google.com/document/d/1WGJaY95A4hVjybBPzHOA80iGygKUKcgx0kSs-G_b7aA/edit ,* https://docs.google.com/document/d/15hEchniGhmgO8mLsSi5mXek2a9na-Mo1VFtrsWcQw1g/edit ,* https://docs.google.com/document/d/1e0HDWjfdRTrbLlAdUMPGtkqmLJJFw9ookSmgBU6JMPI/edit ,* https://docs.google.com/document/d/1KeagpzX2OMBS63fnyFRXMbU7QpBIgykKRZBTMdeHFAo/edit ",Research and Development 1zVyqc3fuvugPqHnDm2ZQ1mKB_TV3bFtxuC_QINhvNfs,solar-panel-prices-have-fallen-by-around-20-every-time-global-capacity-doubled,data-insight,"{""body"": [{""size"": ""narrow"", ""type"": ""image"", ""filename"": ""solar-learning-curve-desktop.png"", ""hasOutline"": false, ""parseErrors"": [], ""smallFilename"": ""solar-learning-curve-mobile.png""}, {""type"": ""text"", ""value"": [{""text"": ""One of the most transformative changes in technology over the last few decades has been the massive drop in the cost of clean energy. Solar photovoltaic "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/cheap-renewables-growth"", ""children"": [{""text"": ""costs have fallen"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" by 90% in the last decade, onshore wind by 70%, and batteries "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/battery-price-decline"", ""children"": [{""text"": ""by more than"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" 90%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These technologies have followed a “learning curve” called "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/learning-curve"", ""children"": [{""text"": ""Wright’s Law"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". This states that the cost of technology falls consistently as the cumulative production of that technology increases."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows the perfect example of this for solar power. This data comes from the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.irena.org/Publications/2023/Aug/Renewable-Power-Generation-Costs-in-2022"", ""children"": [{""text"": ""International Renewable Agency"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://pcdb.santafe.edu/graph.php?curve=158"", ""children"": [{""text"": ""Greg Nemet"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.sciencedirect.com/science/article/pii/S0048733315001699"", ""children"": [{""text"": ""Doyne Farmer & François Lafond"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On the horizontal axis, we have the cumulative installed capacity of solar panels, and on the vertical axis, the cost. Both are measured on logarithmic scales, and the trend follows a straight line. That means the fall in cost has been "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""exponential"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Costs have fallen by around 20% every time the global cumulative capacity doubles. Over four decades, solar power has transformed from one of the most expensive electricity sources to "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.carbonbrief.org/solar-is-now-cheapest-electricity-in-history-confirms-iea/"", ""children"": [{""text"": ""the cheapest"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in many countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1THmmJVzdsf_Bl2xyf9xlYG4YZBamhvhD7SrlEmzOKlU/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""data-insight"", ""title"": ""Solar panel prices have fallen by around 20% every time global capacity doubled"", ""authors"": [""Hannah Ritchie""], ""approved-by"": ""Ed"", ""grapher-url"": ""https://ourworldindata.org/grapher/solar-pv-prices-vs-cumulative-capacity""}",1,2024-04-29 11:11:44,2024-06-13 05:47:48,2024-06-13 05:46:39,unlisted,ALBJ4Ls3o6_pt-_QbUwPmzi4harCX-buy51snQPC5j7Of6q3LqUNfhHtv2PBeiqEnWeWIBeP7tvqRmBhKRTt8w,," One of the most transformative changes in technology over the last few decades has been the massive drop in the cost of clean energy. Solar photovoltaic [costs have fallen](https://ourworldindata.org/cheap-renewables-growth) by 90% in the last decade, onshore wind by 70%, and batteries [by more than](https://ourworldindata.org/battery-price-decline) 90%. These technologies have followed a “learning curve” called [Wright’s Law](https://ourworldindata.org/learning-curve). This states that the cost of technology falls consistently as the cumulative production of that technology increases. The chart shows the perfect example of this for solar power. This data comes from the [International Renewable Agency](https://www.irena.org/Publications/2023/Aug/Renewable-Power-Generation-Costs-in-2022), [Greg Nemet](https://pcdb.santafe.edu/graph.php?curve=158), and [Doyne Farmer & François Lafond](https://www.sciencedirect.com/science/article/pii/S0048733315001699). On the horizontal axis, we have the cumulative installed capacity of solar panels, and on the vertical axis, the cost. Both are measured on logarithmic scales, and the trend follows a straight line. That means the fall in cost has been _exponential_. Costs have fallen by around 20% every time the global cumulative capacity doubles. Over four decades, solar power has transformed from one of the most expensive electricity sources to [the cheapest](https://www.carbonbrief.org/solar-is-now-cheapest-electricity-in-history-confirms-iea/) in many countries. [Read more from my colleague, Max Roser, on learning curves](https://ourworldindata.org/learning-curve) →",Solar panel prices have fallen by around 20% every time global capacity doubled 1zJl2JnuDfK616MC65-z0f46oZq6wWe0Gigggc87Uvxk,lucas-rodes-guirao,author,"{""bio"": [{""type"": ""text"", ""value"": [{""text"": ""Lucas is a Senior Data Scientist at Our World in Data, mostly working on the curation of various datasets and the data infrastructure."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Prior to joining us in 2021, he served as a Deep Learning Researcher at the National Institute of Informatics (Tokyo, Japan) and as a Data Scientist for the private sector. He studied an M.Sc. in Electrical Engineering at UPC (Barcelona, Spain) and KTH (Stockholm, Sweden)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Passionate about open-source software, he has authored projects like "", ""spanType"": ""span-simple-text""}, {""url"": ""https://github.com/lucasrodes/whatstk"", ""children"": [{""text"": ""whatstk"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and contributed to pandas."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""body"": [{""rows"": [], ""type"": ""research-and-writing"", ""heading"": ""All work"", ""primary"": [], ""secondary"": [{""value"": {""url"": ""https://docs.google.com/document/d/1y6_r7uCs3gCpshniXdE-nUkWgRxpZL0Tas2zm4ewebI/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1XBMGw4j4jdlh4qt4Z2y2F_4cNFm-U5yrWFY2fRvMt3M/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1sC5hYCG5Tbnt02QzaxHFeGhliSWbwUuyttXKP8TS9xc/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1fdECw4wt6d7_-cIeMcsBfnoUEoeKwF2Z1gj3Y8RCF4g/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1SpyofFwtTSpD69fW6rUq-D-zM6DSsW7I7l0_bNC-AMA/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/19AlSstYCtGglhNNt9mbKGAEf_oZcQTKNjYJ-5BlXSts/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1IIijqBdZ9sev_QZ_iAorz3Rh7GI1RiM3fMrdvvaUOLM/edit""}}, {""value"": {""url"": ""https://ourworldindata.org/population-growth"", ""title"": ""Population Growth"", ""authors"": [""Hannah Ritchie"", ""Lucas Rodés-Guirao"", ""Edouard Mathieu"", ""Marcel Gerber"", ""Esteban Ortiz-Ospina"", ""Joe Hasell"", ""Max Roser""], ""filename"": ""World-Population-Growth.png"", ""subtitle"": ""Explore global and country data on population growth, demography, and how this is changing.""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1wCZsgwS9Tlh8ySeanWnDgoX-fVftlVgzTBW8N8FSQE8/edit""}}, {""value"": {""url"": ""https://ourworldindata.org/ozone-layer"", ""title"": ""Ozone Layer"", ""authors"": [""Hannah Ritchie"", ""Lucas Rodés-Guirao"", ""Max Roser""], ""filename"": ""Ozone-Layer.png"", ""subtitle"": ""Humans were emitting large amounts of gases that depleted the ozone layer. But in the 1980s the world came together to tackle the problem. Emissions have fallen by more than 99%.""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1BiDjrPb1f51E2t2mmtrIBA8LJHKkuecvSUm-b5ffxP8/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1f9abb7XLK8FCTPYJbn66qIfgGIYppr5spposXWRiN6o/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1VfZnfnJWL-t3XTwwvVogsYJTHKp-6GXoFji0UOkNmsc/edit""}}, {""value"": {""url"": ""https://ourworldindata.org/coronavirus"", ""title"": ""Coronavirus Pandemic (COVID-19)"", ""authors"": [""Edouard Mathieu"", ""Hannah Ritchie"", ""Lucas Rodés-Guirao"", ""Cameron Appel"", ""Daniel Gavrilov"", ""Charlie Giattino"", ""Joe Hasell"", ""Bobbie Macdonald"", ""Saloni Dattani"", ""Diana Beltekian"", ""Esteban Ortiz-Ospina"", ""and Max Roser""], ""filename"": ""coronavirus.png"", ""subtitle"": ""We built 207 country profiles which allow you to explore the statistics on the coronavirus pandemic for every country in the world.""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1pB2dwxPSw-i1TWhuEJqx6n3jBTwxB9bMJQNyinB6NOg/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1ppx0jcy-PKs56l587pllO9_MtETIQTngRudb0VfoVbs/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1XrOGw_v7K1af0WVvjBIsQb9C4VesIGrjwEg6IEj-plU/edit""}}], ""parseErrors"": [], ""hide-authors"": true}], ""refs"": {""errors"": [], ""definitions"": {}}, ""role"": ""Senior Data Scientist"", ""type"": ""author"", ""title"": ""Lucas Rodés-Guirao"", ""authors"": [""Our World in Data team""], ""socials"": {""type"": ""socials"", ""links"": [{""url"": ""mailto:lucas@ourworldindata.org"", ""text"": ""lucas@ourworldindata.org"", ""type"": ""email""}, {""url"": ""https://lcsrg.me/"", ""text"": ""lcsrg.me"", ""type"": ""link""}, {""url"": ""https://x.com/lucasrodesg"", ""text"": ""@lucasrodesg"", ""type"": ""x""}], ""parseErrors"": []}, ""featured-image"": ""Lucas-0001-_K3A0183-2.jpg""}",1,2024-05-23 09:47:32,2024-05-28 12:22:00,2024-05-28 12:18:20,unlisted,ALBJ4LvSVNo86_AMdCskQovFvUFzzOWgIC-K0WEDvhvc0T-M9_3DSdx4s8PUpVqp6Q0olqYCblbhG0iOHqm7Fw,,"## All work * https://docs.google.com/document/d/1XBMGw4j4jdlh4qt4Z2y2F_4cNFm-U5yrWFY2fRvMt3M/edit ,* https://docs.google.com/document/d/1sC5hYCG5Tbnt02QzaxHFeGhliSWbwUuyttXKP8TS9xc/edit ,* https://docs.google.com/document/d/1fdECw4wt6d7_-cIeMcsBfnoUEoeKwF2Z1gj3Y8RCF4g/edit ,* https://docs.google.com/document/d/1SpyofFwtTSpD69fW6rUq-D-zM6DSsW7I7l0_bNC-AMA/edit ,* https://docs.google.com/document/d/19AlSstYCtGglhNNt9mbKGAEf_oZcQTKNjYJ-5BlXSts/edit ,* https://docs.google.com/document/d/1IIijqBdZ9sev_QZ_iAorz3Rh7GI1RiM3fMrdvvaUOLM/edit ,* https://ourworldindata.org/population-growth ,* https://docs.google.com/document/d/1wCZsgwS9Tlh8ySeanWnDgoX-fVftlVgzTBW8N8FSQE8/edit ,* https://ourworldindata.org/ozone-layer ,* https://docs.google.com/document/d/1BiDjrPb1f51E2t2mmtrIBA8LJHKkuecvSUm-b5ffxP8/edit ,* https://docs.google.com/document/d/1f9abb7XLK8FCTPYJbn66qIfgGIYppr5spposXWRiN6o/edit ,* https://docs.google.com/document/d/1VfZnfnJWL-t3XTwwvVogsYJTHKp-6GXoFji0UOkNmsc/edit ,* https://ourworldindata.org/coronavirus ,* https://docs.google.com/document/d/1pB2dwxPSw-i1TWhuEJqx6n3jBTwxB9bMJQNyinB6NOg/edit ,* https://docs.google.com/document/d/1ppx0jcy-PKs56l587pllO9_MtETIQTngRudb0VfoVbs/edit ,* https://docs.google.com/document/d/1XrOGw_v7K1af0WVvjBIsQb9C4VesIGrjwEg6IEj-plU/edit * [lucas@ourworldindata.org](mailto:lucas@ourworldindata.org) * [lcsrg.me](https://lcsrg.me/) * [@lucasrodesg](https://x.com/lucasrodesg) Lucas is a Senior Data Scientist at Our World in Data, mostly working on the curation of various datasets and the data infrastructure. Prior to joining us in 2021, he served as a Deep Learning Researcher at the National Institute of Informatics (Tokyo, Japan) and as a Data Scientist for the private sector. He studied an M.Sc. in Electrical Engineering at UPC (Barcelona, Spain) and KTH (Stockholm, Sweden). Passionate about open-source software, he has authored projects like [whatstk](https://github.com/lucasrodes/whatstk) and contributed to pandas.",Lucas Rodés-Guirao 1zEtvZ_MdbQlh1iczfNH-Eswu7N3bBjVWEV3Xp2VfCGI,decarbonizing-energy-progress,article,"{""toc"": [{""slug"": ""undefined-renewables-have-grown-strongly-but-a-lot-of-this-has-simply-offset-a-decline-in-nuclear"", ""text"": ""Renewables have grown strongly, but a lot of this has simply offset a decline in nuclear"", ""title"": ""Renewables have grown strongly, but a lot of this has simply offset a decline in nuclear"", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""In 2021, 18% of the world’s "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/energy-substitution-method"", ""children"": [{""text"": ""primary energy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" came from low-carbon sources. Low-carbon energy is energy from nuclear and renewables."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How has this share changed? Have we made progress on decarbonizing the global energy supply?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the line chart we see how the share of global energy that comes from low-carbon sources has changed over time. It more than doubled in the 20 years from 1970 to 1990: increasing from 6% to 13%. At this point, progress appeared to stall for several decades, but is now rising again."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/low-carbon-share-energy?tab=chart&country=~OWID_WRL"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Renewables have grown strongly, but a lot of this has simply offset a decline in nuclear"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To understand the different rates at which we’ve made progress on decarbonization, it’s useful to break it down into its two components: nuclear energy and renewables. This is shown in the chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""First, we see why we made progress in the 1970s and 80s: nuclear energy was growing quickly, and renewables – mainly hydropower – were also growing, albeit slowly. But throughout the 1990s we see that neither nuclear or renewables made much progress; we were producing more of each source "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/energy?tab=chart&facet=none&country=~OWID_WRL&Total+or+Breakdown=Select+a+source&Select+a+source=Low-carbon&Energy+or+Electricity=Primary+energy&Metric=Annual+consumption"", ""children"": [{""text"": ""in absolute terms"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", but this growth could not outpace the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/energy?tab=chart&facet=none&country=~OWID_WRL&Total+or+Breakdown=Total&Select+a+source=Low-carbon&Energy+or+Electricity=Primary+energy&Metric=Annual+consumption"", ""children"": [{""text"": ""increased demand for energy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" overall. Low-carbon energy’s "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""share"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" didn’t increase much."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What we see in the first decade of the 2000s is a complete divergence of these sources. Renewables went up. Nuclear went down. Countries stopped investing in new nuclear plants; and some closed down. In absolute terms the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/energy?tab=chart&facet=none&country=~OWID_WRL&Total+or+Breakdown=Select+a+source&Select+a+source=Nuclear&Energy+or+Electricity=Primary+energy&Metric=Annual+consumption"", ""children"": [{""text"": ""total amount"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of energy we produced from nuclear stagnated, and for several years actually declined."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/primary-energy-share-nuclear-renewables"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world made a lot of progress on renewables over this period. But unfortunately some of these gains were offset by the decline of nuclear. Only recently – with a rapid growth in wind and solar power – has low-carbon energy made a comeback. We have continued to make progress on decarbonization, but we could have made much more."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Whether we care about decarbonization or the health impacts of energy production, moving away from fossil fuels to renewables and nuclear is crucial – they are "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/safest-sources-of-energy"", ""children"": [{""text"": ""much safer and cleaner"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" than fossil fuel power. 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But this transition needs to happen much faster."", ""featured-image"": ""decarbonizing-thumbnail.png""}",1,2023-10-09 19:23:58,2021-11-30 11:48:25,2023-12-28 16:31:13,unlisted,ALBJ4Lvpa8Kxd9i8h6359DzouqC9A_QYINRwWxNF-Aiy9Nrnn2s8-MU8858SZfSSsyvBwGpySwXe68VRqwDzww,"[{""href"": ""/energy"", ""label"": ""Energy""}, {""label"": ""Decarbonizing Energy""}]","In 2021, 18% of the world’s [primary energy](https://ourworldindata.org/energy-substitution-method) came from low-carbon sources. Low-carbon energy is energy from nuclear and renewables. How has this share changed? Have we made progress on decarbonizing the global energy supply? In the line chart we see how the share of global energy that comes from low-carbon sources has changed over time. It more than doubled in the 20 years from 1970 to 1990: increasing from 6% to 13%. At this point, progress appeared to stall for several decades, but is now rising again. ## Renewables have grown strongly, but a lot of this has simply offset a decline in nuclear To understand the different rates at which we’ve made progress on decarbonization, it’s useful to break it down into its two components: nuclear energy and renewables. This is shown in the chart. First, we see why we made progress in the 1970s and 80s: nuclear energy was growing quickly, and renewables – mainly hydropower – were also growing, albeit slowly. But throughout the 1990s we see that neither nuclear or renewables made much progress; we were producing more of each source [in absolute terms](https://ourworldindata.org/explorers/energy?tab=chart&facet=none&country=~OWID_WRL&Total+or+Breakdown=Select+a+source&Select+a+source=Low-carbon&Energy+or+Electricity=Primary+energy&Metric=Annual+consumption), but this growth could not outpace the [increased demand for energy](https://ourworldindata.org/explorers/energy?tab=chart&facet=none&country=~OWID_WRL&Total+or+Breakdown=Total&Select+a+source=Low-carbon&Energy+or+Electricity=Primary+energy&Metric=Annual+consumption) overall. Low-carbon energy’s _share_ didn’t increase much. What we see in the first decade of the 2000s is a complete divergence of these sources. Renewables went up. Nuclear went down. Countries stopped investing in new nuclear plants; and some closed down. In absolute terms the [total amount](https://ourworldindata.org/explorers/energy?tab=chart&facet=none&country=~OWID_WRL&Total+or+Breakdown=Select+a+source&Select+a+source=Nuclear&Energy+or+Electricity=Primary+energy&Metric=Annual+consumption) of energy we produced from nuclear stagnated, and for several years actually declined. The world made a lot of progress on renewables over this period. But unfortunately some of these gains were offset by the decline of nuclear. Only recently – with a rapid growth in wind and solar power – has low-carbon energy made a comeback. We have continued to make progress on decarbonization, but we could have made much more. Whether we care about decarbonization or the health impacts of energy production, moving away from fossil fuels to renewables and nuclear is crucial – they are [much safer and cleaner](https://ourworldindata.org/safest-sources-of-energy) than fossil fuel power. When progress on these sources work against rather than with each other, it is fossil fuels that win. ---",Is the world making progress in decarbonizing energy? 1zDB6zUS4fw6s3rXHz5Su6rR50Ey_hB75CYDIkj8kO_w,migration,topic-page,"{""toc"": [], ""body"": [{""type"": ""topic-page-intro"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Hundreds of millions of people live in a country that is different from the one in which they were born. In some countries, the majority of the population are immigrants."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Migration has played a crucial role in economic development, education and mobility. 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Explore data on global migration."", ""sticky-nav"": [{""text"": ""Introduction"", ""target"": ""#introduction""}, {""text"": ""Data Explorer"", ""target"": ""#explore-data-on-migration-refugees-and-asylum-seekers""}, {""text"": ""Where do people migrate from and to?"", ""target"": ""#explore-data-on-where-people-migrate-from-and-to""}, {""text"": ""Charts"", ""target"": ""#all-charts""}, {""text"": ""Cite this work"", ""target"": ""#article-citation""}, {""text"": ""Reuse this work"", ""target"": ""#article-licence""}], ""featured-image"": ""migration-featured-image.png""}",1,2024-06-12 14:06:25,2022-11-18 10:14:17,1970-01-01 00:00:00,unlisted,ALBJ4Lv4JqhJYlBloXc9c8zw2tyCIJTj3LfciBKJdubXdaGntfPmkdvAmU2nbLdkmrQBehkUz_ETx_GuyhO4Tw,,,Migration 1yzOrFd6uWvrAl2oFB3S67oOSbhgL1ffPHxjAqS7i-4w,economic-inequality,topic-page,"{""toc"": [{""slug"": ""about-this-data"", ""text"": ""About this data"", ""title"": ""About this data"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""topic-page-intro"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""How are incomes and wealth distributed between people? 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""About this data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This data explorer provides a range of inequality indicators measured according to two different definitions of income obtained from different sources."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""Data from the World Inequality Database relates to inequality before taxes and benefits."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Data from the World Bank relates to either income after taxes and benefits or consumption, depending on the country and year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Further information about the definitions and methods behind this data can be found in 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""https://docs.google.com/document/d/1MqVP5uoCjPdWSq9JPbhRWQS0VdoBeV-ahVlzYJQc-oI/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/15du6zm00Ll6DtJWlhu4o_lZgb72RKFyd43tflYSKIVA/edit""}}]}, {""heading"": ""How is inequality defined and measured?"", ""articles"": [{""value"": {""url"": ""https://docs.google.com/document/d/1w6ymndlipKyB8QEQsvpMvB3qC9Fn1QiLAUV_nnjGWtc/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1rEhPkFIeAvIQeOHj69HHlHLd1MygsBX7jb79MnpFJmI/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/17-2C6H3kPd7w9DxWT2RG2QuLP0Pzxvm4H2RVsWFWUzM/edit""}}]}], ""type"": ""research-and-writing"", ""primary"": [{""value"": {""url"": ""https://docs.google.com/document/d/1M2S6EP-CAZL1Oi4szQ5k0BiuPUdaUDbAXTCjnwsQK3o/edit""}}], ""secondary"": [{""value"": {""url"": ""https://docs.google.com/document/d/1w6ymndlipKyB8QEQsvpMvB3qC9Fn1QiLAUV_nnjGWtc/edit""}}], ""parseErrors"": [], ""hide-authors"": false}, {""top"": [{""url"": ""https://ourworldindata.org/grapher/economic-inequality-gini-index""}], ""type"": ""all-charts"", ""heading"": ""Interactive Charts on Economic Inequality"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""topic-page"", ""title"": ""Economic Inequality"", ""authors"": [""Joe Hasell"", ""Pablo Arriagada"", ""Esteban Ortiz-Ospina"", ""Max Roser""], ""excerpt"": ""See all our data, visualizations, and writing on economic inequality."", ""sticky-nav"": [{""text"": ""Introduction"", ""target"": ""#introduction""}, {""text"": ""Data Explorer"", ""target"": ""#explore-data-on-economic-inequality""}, {""text"": ""Research & Writing"", ""target"": ""#research-writing""}, {""text"": ""Charts"", ""target"": ""#all-charts""}, {""text"": ""Cite This Work"", ""target"": ""#article-citation""}, {""text"": ""Reuse This Work"", ""target"": ""#article-licence""}], ""featured-image"": ""economic-inequality-featured-image.png""}",1,2023-06-27 19:58:20,2023-07-07 08:31:36,2024-01-03 10:47:49,unlisted,ALBJ4Lvuit6LvU1FIJwrepvSICFdIbHY1_ES0s6xSOxbQFlzUq5s-2xqmHHSB_AtRQpswr30QwhLmmotMwuGMQ,,"How are incomes and wealth distributed between people? Both within countries and across the world as a whole? On this page, you can find all our data, visualizations, and writing relating to economic inequality. This evidence shows us that inequality in many countries is very high and, in many cases, has been on the rise. Global economic inequality is vast and compounded by [overlapping inequalities](https://docs.google.com/document/d/15du6zm00Ll6DtJWlhu4o_lZgb72RKFyd43tflYSKIVA/edit) in health, education, and many other dimensions. But economic inequality is not rising everywhere. Within many countries, it has fallen or remained stable. And global inequality – after two centuries of increase – is [now falling](https://docs.google.com/document/d/1q1sbAEqbeOgit1Nzc_mEoHLqEzXs5U4ONgHgI6YQN7k/edit) too. The large differences we see across countries and over time are crucial. They show us that high and rising inequality is not inevitable, and that the extent of inequality today is something that we can change. # Explore Data on Economic Inequality ## About this data This data explorer provides a range of inequality indicators measured according to two different definitions of income obtained from different sources. * Data from the World Inequality Database relates to inequality before taxes and benefits. * Data from the World Bank relates to either income after taxes and benefits or consumption, depending on the country and year. Further information about the definitions and methods behind this data can be found in the article below, where you can also explore and compare a much broader range of indicators from different sources: ### undefined undefined https://docs.google.com/document/d/1rEhPkFIeAvIQeOHj69HHlHLd1MygsBX7jb79MnpFJmI/edit ## Related research and writing * https://docs.google.com/document/d/1M2S6EP-CAZL1Oi4szQ5k0BiuPUdaUDbAXTCjnwsQK3o/edit ,* https://docs.google.com/document/d/1w6ymndlipKyB8QEQsvpMvB3qC9Fn1QiLAUV_nnjGWtc/edit ,* https://docs.google.com/document/d/1VUw9tl8UyGNK05XMJat_19gzAGvGyBxBJsCsDJXvDAY/edit ,* https://docs.google.com/document/d/1XKnumdscnkKcoO9qit4M06VS8DHaR6fysYv-4PsOQfI/edit ,* https://docs.google.com/document/d/1M2S6EP-CAZL1Oi4szQ5k0BiuPUdaUDbAXTCjnwsQK3o/edit ,* https://docs.google.com/document/d/1q1sbAEqbeOgit1Nzc_mEoHLqEzXs5U4ONgHgI6YQN7k/edit ,* https://docs.google.com/document/d/1MqVP5uoCjPdWSq9JPbhRWQS0VdoBeV-ahVlzYJQc-oI/edit ,* https://docs.google.com/document/d/15du6zm00Ll6DtJWlhu4o_lZgb72RKFyd43tflYSKIVA/edit ,* https://docs.google.com/document/d/1w6ymndlipKyB8QEQsvpMvB3qC9Fn1QiLAUV_nnjGWtc/edit ,* https://docs.google.com/document/d/1rEhPkFIeAvIQeOHj69HHlHLd1MygsBX7jb79MnpFJmI/edit ,* https://docs.google.com/document/d/17-2C6H3kPd7w9DxWT2RG2QuLP0Pzxvm4H2RVsWFWUzM/edit ",Economic Inequality 1yQW1TQkTyK55VMJW38X0sM2o_glHT8mjWkxeovASTgs,land-use-diets,article,"{""toc"": [{""slug"": ""livestock-convert-feed-to-high-quality-micronutrient-rich-protein"", ""text"": ""Livestock convert feed to high-quality, micronutrient-rich protein"", ""title"": ""Livestock convert feed to high-quality, micronutrient-rich protein"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""text"": [{""type"": ""text"", ""value"": [{""text"": ""Half of the world’s habitable land is used for agriculture, with most of this used to raise livestock for dairy and meat. Livestock are fed from two sources – lands on which the animals graze and land on which feeding crops, such as soy and cereals, are grown. How much would our agricultural land use decline if the world adopted a plant-based diet?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Research suggests that if everyone shifted to a plant-based diet we would reduce global land use for agriculture by 75%. This large reduction of agricultural land use would be possible thanks to a reduction in land used for grazing and a smaller need for land to grow crops. The research also shows that cutting out beef and dairy (by substituting chicken, eggs, fish or plant-based food) has a "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""much"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" larger impact than eliminating chicken or fish."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Summary"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The expansion of land for agriculture is the leading "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/drivers-of-deforestation"", ""children"": [{""text"": ""driver of deforestation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and biodiversity loss."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/global-land-for-agriculture"", ""children"": [{""text"": ""Half"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of the world’s ice- and desert-free land is used for agriculture. Most of this is for raising livestock – the land requirements of meat and dairy production are equivalent to an area the size of the Americas, spanning all the way from Alaska to Tierra del Fuego."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The land use of livestock is so large because it takes around 100 times as much land to produce a kilocalorie of beef or lamb versus plant-based alternatives. This is shown in the chart."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": ""  The same is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/land-use-protein-poore"", ""children"": [{""text"": ""also true for protein"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – it takes almost 100 times as much land to produce a gram of protein from beef or lamb, versus peas or tofu."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Of course the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""type"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of land used to raise cows or sheep is not the same as cropland for cereals, potatoes or beans. Livestock can be raised on pasture grasslands, or on steep hills where it is not possible to grow crops. Two-thirds of pastures are unsuitable for growing crops."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This raises the question of whether we could, or should, stop using it for agriculture at all. We could let natural vegetation and ecosystems return to these lands, with large benefits for biodiversity and carbon sequestration."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In an upcoming article we will look at the carbon opportunity costs of using land for agriculture."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One concern is whether we would be able to grow enough food for everyone on the cropland that is left. The research suggests that it’s possible to feed everyone in the world a nutritious diet on existing croplands, but only if we saw a widespread shift towards plant-based diets."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/land-use-kcal-poore"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Related charts"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 5, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/land-use-protein-poore"", ""type"": ""prominent-link"", ""title"": ""Land use of foods per 100 grams of protein"", ""description"": """", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/land-use-per-kg-poore"", ""type"": ""prominent-link"", ""title"": ""Land use of foods per kilogram"", ""description"": """", ""parseErrors"": []}, {""text"": [{""text"": ""More plant-based diets tend to need less cropland"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we would shift towards a more plant-based diet we don’t only need less agricultural land overall, we also need less "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""cropland"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". This might go against our intuition: if we substitute beans, peas, tofu and cereals for meat and dairy, surely we would need more cropland to grow them?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Let’s look at why this is not the case. In the chart here we see the amount of agricultural land the world would need to provide food for everyone. This comes from the work of Joseph Poore and Thomas Nemecek, the largest meta-analysis of global food systems to date."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The top bar shows the current land use based on the global average diet in 2010."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we see, almost three-quarters of this land is used as pasture, the remaining quarter is cropland."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" If we combine pastures and cropland for animal feed, around 80% of all agricultural land is used for meat and dairy production."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Land-use-of-different-diets-Poore-Nemecek.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This has a large impact on how land requirements change as we shift towards a more plant-based diet. If the world population ate less meat and dairy we would be eating more crops. The consequence – as the following bar chart shows – would be that the ‘human food’ component of cropland would increase while the land area used for animal feed would shrink."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the hypothetical scenario in which the entire world adopted a vegan diet the researchers estimate that our total agricultural land use would shrink from 4.1 billion hectares to 1 billion hectares. A reduction of 75%. That’s equal to an area the size of North America and Brazil combined."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But importantly large land use reductions would be possible even without a fully vegan diet. Cutting out beef, mutton and dairy makes the biggest difference to agricultural land use as it would free up the land that is used for pastures. But it’s not just pasture; it also reduces the amount of cropland we need."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is an important insight from this research: cutting out beef and dairy (by substituting chicken, eggs, fish or plant-based food) has a "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""much"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" larger impact than eliminating chicken or fish."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Less than half of the world’s cereals are fed directly to humans"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How is it possible that producing "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""more"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" crops for human consumption needs "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""less"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" cropland? The answer becomes clear when we step back and look at the bigger picture of how much crop we actually produce, and how this is used."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart we see the breakdown of what the world’s cereals are used for. This is split into three categories: direct human food (the rice, oats, wheat, bread etc. that we eat); animal feed; and industrial uses (which is mainly biofuels)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Less than half – only 48% – of the world’s cereals are eaten by humans. 41% is used for animal feed, and 11% for biofuels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In many countries, the share that is for human consumption is even smaller. We see this in the map. In most countries across Europe it’s less than one-third of cereal production is used for human consumption, and in the US only 10% is."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It’s not just cereals that are diverted towards animal feed and biofuels. It’s also true of many oilcrops. As we look at in more detail "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://ourworldindata.org/soy#more-than-three-quarters-of-global-soy-is-fed-to-animals"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""text"": "", only 7% of soy goes towards human foods such as tofu, tempeh, soy milk and other substitute products. Most of the rest goes towards oil production which is split between soybean meal for animal feed and soybean oil. These are co-products, although by economic value, animal feed dominates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""left"": [{""url"": ""https://ourworldindata.org/grapher/cereal-distribution-to-uses?tab=chart&stackMode=relative®ion=World"", ""type"": ""chart"", ""parseErrors"": []}], ""type"": ""side-by-side"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/share-cereal-human-food?stackMode=absolute®ion=World"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Related charts"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 5, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-cereals-animal-feed"", ""type"": ""prominent-link"", ""title"": ""Share of cereals allocated to animal feed"", ""description"": """", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-cereals-industrial-uses"", ""type"": ""prominent-link"", ""title"": ""Share of cereals allocated to industrial uses (e.g. biofuels)"", ""description"": """", ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Livestock waste a lot of energy and protein, but do produce more nutrient-dense protein sources"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Cereals fed to animals are not wasted: they are converted to meat and dairy, and consumed by humans in the end. But, in terms of calories and "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""total"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" protein, this process is very inefficient. ["", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""What’s true is that animals do produce high-quality, micronutrient-rich protein – see the box on this below"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""]."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" When you feed an animal, not all of this energy goes into producing additional meat, milk or eggs. Most is used to simply keep the animal alive. This is exactly the same for us: most of the calories we eat are used to keep us alive and maintain our body weight. It’s only when we eat in excess that we gain weight."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the charts here we see the energy and protein efficiency of different animal products."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This tells us what percentage of the calories or grams of protein that we feed livestock are later available to consume as meat and dairy. As an example: beef has an energy efficiency of about 2%. This means that for every 100 kilocalories you feed a cow, you only get 2 kilocalories of beef back. In general we see that cows are the least efficient, followed by lamb, pigs then poultry. As a rule of thumb: smaller animals are more efficient. That’s why chicken and fish tend to have a lower environmental impact."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is why eating less meat would mean eliminating large losses of calories and thereby reduce the amount of farmland we need. 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"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Science"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""360"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(6392), 987-992."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""If the world adopted a plant-based diet, we would reduce global agricultural land use from 4 to 1 billion hectares"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""We could reduce the amount of land used for grazing and croplands used to grow animal feed."", ""dateline"": ""March 4, 2021"", ""subtitle"": ""We could reduce the amount of land used for grazing and croplands used to grow animal feed."", ""sidebar-toc"": false, ""featured-image"": ""Land-use-different-diets-thumbnail.png""}",1,2024-02-22 11:18:19,2021-03-04 12:00:00,2024-02-28 12:38:35,listed,ALBJ4Lv1PyJly6kAybkVEcV6dI-aAvakSqKM7n3VRW7hS6EVIs7Qg67U97SfGYiipoRKi4COeGo0sPbXthQcAg,," The expansion of land for agriculture is the leading [driver of deforestation](https://ourworldindata.org/drivers-of-deforestation) and biodiversity loss. [Half](https://ourworldindata.org/global-land-for-agriculture) of the world’s ice- and desert-free land is used for agriculture. Most of this is for raising livestock – the land requirements of meat and dairy production are equivalent to an area the size of the Americas, spanning all the way from Alaska to Tierra del Fuego. The land use of livestock is so large because it takes around 100 times as much land to produce a kilocalorie of beef or lamb versus plant-based alternatives. This is shown in the chart.1  The same is [also true for protein](https://ourworldindata.org/grapher/land-use-protein-poore) – it takes almost 100 times as much land to produce a gram of protein from beef or lamb, versus peas or tofu. Of course the _type_ of land used to raise cows or sheep is not the same as cropland for cereals, potatoes or beans. Livestock can be raised on pasture grasslands, or on steep hills where it is not possible to grow crops. Two-thirds of pastures are unsuitable for growing crops.2 This raises the question of whether we could, or should, stop using it for agriculture at all. We could let natural vegetation and ecosystems return to these lands, with large benefits for biodiversity and carbon sequestration.3 In an upcoming article we will look at the carbon opportunity costs of using land for agriculture. One concern is whether we would be able to grow enough food for everyone on the cropland that is left. The research suggests that it’s possible to feed everyone in the world a nutritious diet on existing croplands, but only if we saw a widespread shift towards plant-based diets. ##### Related charts ### Land use of foods per 100 grams of protein https://ourworldindata.org/grapher/land-use-protein-poore ### Land use of foods per kilogram https://ourworldindata.org/grapher/land-use-per-kg-poore # More plant-based diets tend to need less cropland If we would shift towards a more plant-based diet we don’t only need less agricultural land overall, we also need less _cropland_. This might go against our intuition: if we substitute beans, peas, tofu and cereals for meat and dairy, surely we would need more cropland to grow them? Let’s look at why this is not the case. In the chart here we see the amount of agricultural land the world would need to provide food for everyone. This comes from the work of Joseph Poore and Thomas Nemecek, the largest meta-analysis of global food systems to date.4 The top bar shows the current land use based on the global average diet in 2010. As we see, almost three-quarters of this land is used as pasture, the remaining quarter is cropland.5 If we combine pastures and cropland for animal feed, around 80% of all agricultural land is used for meat and dairy production. This has a large impact on how land requirements change as we shift towards a more plant-based diet. If the world population ate less meat and dairy we would be eating more crops. The consequence – as the following bar chart shows – would be that the ‘human food’ component of cropland would increase while the land area used for animal feed would shrink.6 In the hypothetical scenario in which the entire world adopted a vegan diet the researchers estimate that our total agricultural land use would shrink from 4.1 billion hectares to 1 billion hectares. A reduction of 75%. That’s equal to an area the size of North America and Brazil combined. But importantly large land use reductions would be possible even without a fully vegan diet. Cutting out beef, mutton and dairy makes the biggest difference to agricultural land use as it would free up the land that is used for pastures. But it’s not just pasture; it also reduces the amount of cropland we need. This is an important insight from this research: cutting out beef and dairy (by substituting chicken, eggs, fish or plant-based food) has a _much_ larger impact than eliminating chicken or fish. # Less than half of the world’s cereals are fed directly to humans How is it possible that producing _more_ crops for human consumption needs _less_ cropland? The answer becomes clear when we step back and look at the bigger picture of how much crop we actually produce, and how this is used. In the chart we see the breakdown of what the world’s cereals are used for. This is split into three categories: direct human food (the rice, oats, wheat, bread etc. that we eat); animal feed; and industrial uses (which is mainly biofuels). Less than half – only 48% – of the world’s cereals are eaten by humans. 41% is used for animal feed, and 11% for biofuels. In many countries, the share that is for human consumption is even smaller. We see this in the map. In most countries across Europe it’s less than one-third of cereal production is used for human consumption, and in the US only 10% is.7 It’s not just cereals that are diverted towards animal feed and biofuels. It’s also true of many oilcrops. As we look at in more detail **[here](https://ourworldindata.org/soy#more-than-three-quarters-of-global-soy-is-fed-to-animals)**, only 7% of soy goes towards human foods such as tofu, tempeh, soy milk and other substitute products. Most of the rest goes towards oil production which is split between soybean meal for animal feed and soybean oil. These are co-products, although by economic value, animal feed dominates. ##### Related charts ### Share of cereals allocated to animal feed https://ourworldindata.org/grapher/share-cereals-animal-feed ### Share of cereals allocated to industrial uses (e.g. biofuels) https://ourworldindata.org/grapher/share-cereals-industrial-uses # Livestock waste a lot of energy and protein, but do produce more nutrient-dense protein sources Cereals fed to animals are not wasted: they are converted to meat and dairy, and consumed by humans in the end. But, in terms of calories and _total_ protein, this process is very inefficient. [_What’s true is that animals do produce high-quality, micronutrient-rich protein – see the box on this below_].8 When you feed an animal, not all of this energy goes into producing additional meat, milk or eggs. Most is used to simply keep the animal alive. This is exactly the same for us: most of the calories we eat are used to keep us alive and maintain our body weight. It’s only when we eat in excess that we gain weight. In the charts here we see the energy and protein efficiency of different animal products.9 This tells us what percentage of the calories or grams of protein that we feed livestock are later available to consume as meat and dairy. As an example: beef has an energy efficiency of about 2%. This means that for every 100 kilocalories you feed a cow, you only get 2 kilocalories of beef back. In general we see that cows are the least efficient, followed by lamb, pigs then poultry. As a rule of thumb: smaller animals are more efficient. That’s why chicken and fish tend to have a lower environmental impact. This is why eating less meat would mean eliminating large losses of calories and thereby reduce the amount of farmland we need. This would free up billions of hectares for natural vegetation, forests and ecosystems to return. ## Livestock convert feed to high-quality, micronutrient-rich protein As we’ve seen above, animals lose a lot of energy and total protein when converting this to meat and dairy products. But we also need to consider protein _quality_ and provision of micronutrients – essential vitamins and minerals we need to function well. Some, but not all, plant-based products contain high-quality protein. Legumes, such as beans, peas, tofu and other soy products do. Cereals, on their own, don’t – although a complete protein profile can be achieved when mixing them with legumes in your diet. Cereals are great at providing energy, and some protein but they’re missing many essential elements. They are a low-quality protein source. Protein is made up of building blocks called ‘amino acids’ – we need to make sure we’re getting enough of each of these individual amino acids.10 Cereals have an ‘incomplete’ amino acid profile meaning they are lacking in some of them.11 Cereals also lack a number of important micronutrients, such as calcium, iron and B-vitamins. In fact, vitamin B12 is one that you can only get from animal products, or from food supplements. Individual animal products – meat, dairy, fish, eggs – do have a complete amino acid profile. Animals are effective in taking energy-dense but low-quality protein cereals, and converting them into high-quality protein sources. The downside is that they waste a lot of energy and total protein in the process. The key point is that in switching to a vegan diet we cannot simply divert cereals from animal feed to human food. For proper nutrition, we will have to change the types of crops we grow. Not all crops provide low-quality protein – legumes such as peas, beans, lentils and products such as tofu have a good amino acid profile; when mixed with cereals in a person’s diet, it’s possible to get the full range of essential elements.12 There is also a lot of potential to mimic the animal conversion process in the lab – either through lab-grown meat or fermentation processes that make meat substitutes. These would allow us to reap the benefits of converting carbohydrates and sugars into high-quality protein without all of the waste that comes with it. --- # More of our articles on this topic: ### Food production is responsible for one-quarter of the world’s greenhouse gas emissions https://ourworldindata.org/food-ghg-emissions ### You want to reduce the carbon footprint of your food? Focus on what you eat, not whether your food is local https://ourworldindata.org/food-choice-vs-eating-local ### How does the carbon footprint of foods compare across the world? https://ourworldindata.org/less-meat-or-sustainable-meat This data is based on the global median land use of different food products as presented in Poore and Nemecek (2018). This meta-analysis looked at the environmental impacts of foods covering 38,000 farms in 119 countries. For some foods there is significant variability from the median land use depending on how it is produced. We look at these differences **[here](https://ourworldindata.org/environmental-impacts-of-food#distribution-of-land-use-for-foods)**. Poore, J., & Nemecek, T. (2018). [Reducing food’s environmental impacts through producers and consumers](https://science.sciencemag.org/content/360/6392/987). _Science_, _360_(6392), 987-992. An estimated 65% of land used for grass for grazing cattle is not suitable for growing crops. Mottet, A., de Haan, C., Falcucci, A., Tempio, G., Opio, C., & Gerber, P. (2017). [Livestock: on our plates or eating at our table? A new analysis of the feed/food debate](https://www.sciencedirect.com/science/article/pii/S2211912416300013). _Global Food Security_, _14_, 1-8. Poore, J., & Nemecek, T. (2018). [Reducing food’s environmental impacts through producers and consumers](https://science.sciencemag.org/content/360/6392/987). _Science_, _360_(6392), 987-992. Hayek, M. N., Harwatt, H., Ripple, W. J., & Mueller, N. D. (2020). [The carbon opportunity cost of animal-sourced food production on land](https://www.nature.com/articles/s41893-020-00603-4). _Nature Sustainability_, 1-4. Poore, J., & Nemecek, T. (2018). [Reducing food’s environmental impacts through producers and consumers](https://science.sciencemag.org/content/360/6392/987). _Science_, _360_(6392), 987-992. Note that this breakdown of agricultural land use differs slightly from the breakdown of global land use from the UN Food and Agriculture Organization (FAO) for a few reasons. First, this view only includes cropland and pasture used to produce food. Allocation of crops towards industrial uses e.g. biofuels is not included. In UN FAO breakdowns, it is included. Secondly, the amount of land that qualifies as ‘pasture’ depends on definitions surrounding livestock density and other aspects of land management. The extent of ‘rangelands’ – land used to raise livestock but at a relatively low density – can vary from study-to-study. So, while the UN FAO data suggests 50% of habitable land is used for agriculture, Poore and Nemecek (2018) put this figure at 43%. This data is sourced from the meta-analysis study by Joseph Poore and Thomas Nemecek (2018), published in _Science_. Many other studies have looked at this question and found exactly the same result: that if everyone shifted to a vegan diet, we would need _less_ agricultural land (and cropland) specifically. Hayek, M. N., Harwatt, H., Ripple, W. J., & Mueller, N. D. (2020). [The carbon opportunity cost of animal-sourced food production on land](https://www.nature.com/articles/s41893-020-00603-4). _Nature Sustainability_, 1-4. Searchinger, T. D., Wirsenius, S., Beringer, T., & Dumas, P. (2018). [Assessing the efficiency of changes in land use for mitigating climate change](https://www.nature.com/articles/s41586-018-0757-z). _Nature_, _564_(7735), 249-253. There is a strong rich-poor [split across countries](https://ourworldindata.org/grapher/cereals-human-food-vs-gdp): people in poorer countries [get most of their calories](https://ourworldindata.org/grapher/share-of-energy-from-cereals-roots-and-tubers-vs-gdp-per-capita) from cereals as they cannot afford much meat and dairy. This means they cannot afford to divert cereals towards livestock or biofuels. In India, 93% of cereals are consumed by humans; 95% in Kenya; and 96% in Botswana. Tilman, D., & Clark, M. (2014). [Global diets link environmental sustainability and human health](https://www.nature.com/articles/nature13959). _Nature_, _515_(7528), 518-522. Shepon, A., Eshel, G., Noor, E., & Milo, R. (2016). [Energy and protein feed-to-food conversion efficiencies in the US and potential food security gains from dietary changes](https://iopscience.iop.org/article/10.1088/1748-9326/11/10/105002). _Environmental Research Letters_, _11_(10), 105002. This is shown as the average conversion efficiency. It can vary a bit depending on the breed of livestock, what they’re fed, and how they’re managed. But the overall magnitudes are similar. Alexander, P., Brown, C., Arneth, A., Finnigan, J., & Rounsevell, M. D. (2016). [Human appropriation of land for food: The role of diet](https://www.sciencedirect.com/science/article/pii/S0959378016302370). _Global Environmental Change_, _41_, 88-98. World Health Organization, & United Nations University. (2007). _[Protein and amino acid requirements in human nutrition](https://apps.who.int/iris/bitstream/handle/10665/43411/WHO_TRS_935_eng.pdf?ua=1)_[ (Vol. 935)](https://apps.who.int/iris/bitstream/handle/10665/43411/WHO_TRS_935_eng.pdf?ua=1). World Health Organization. One way of comparing the _quality_ of different protein sources is using their Protein Digestibility-Corrected Amino Acid Score (PDCAAS). This score looks not only at the total protein they provide but also digestibility, and whether there are particular deficiencies of specific amino acids. Cereals in particular are often limited in the amino acid, lysine. This gives them a low PDCAAS score of 42, compared to beef which achieves 92. Schaafsma, G. (2000). [The protein digestibility–corrected amino acid score](https://academic.oup.com/jn/article/130/7/1865S/4686203). _The Journal of Nutrition_, _130_(7), 1865S-1867S. Young, V. R., & Pellett, P. L. (1994). [Plant proteins in relation to human protein and amino acid nutrition](https://pubmed.ncbi.nlm.nih.gov/8172124/). _The American Journal of Clinical Nutrition_, _59_(5), 1203S-1212S. As we noted earlier, protein quality can be scored in terms of its Protein Digestibility-Corrected Amino Acid Score (PDCAAS). Soy achieves a PDCAAS of 0.92, comparable to beef at 0.94. Schaafsma, G. (2000). [The protein digestibility–corrected amino acid score](https://academic.oup.com/jn/article/130/7/1865S/4686203). _The Journal of Nutrition_, _130_(7), 1865S-1867S.","If the world adopted a plant-based diet, we would reduce global agricultural land use from 4 to 1 billion hectares" 1yH7P70MBCWsNIx-WQPmZnVqXL4Mrpxrp6J1Jpa6qBko,cheap-renewables-growth,article,"{""toc"": [{""slug"": ""undefined-the-price-of-electricity-from-the-long-standing-sources-fossil-fuels-and-nuclear-power"", ""text"": ""The price of electricity from the long-standing sources: fossil fuels and nuclear power"", ""title"": ""The price of electricity from the long-standing sources: fossil fuels and nuclear power"", ""isSubheading"": false}, {""slug"": ""undefined-the-price-decline-of-electricity-from-renewable-sources"", ""text"": ""The price decline of electricity from renewable sources"", ""title"": ""The price decline of electricity from renewable sources"", ""isSubheading"": false}, {""slug"": ""undefined-why-is-this-happening-learning-curves-and-the-price-of-solar-photovoltaics-modules"", ""text"": ""Why is this happening? Learning curves and the price of solar photovoltaics modules"", ""title"": ""Why is this happening? Learning curves and the price of solar photovoltaics modules"", ""isSubheading"": false}, {""slug"": ""undefined-a-short-history-of-solar-from-outer-space-to-the-cheapest-source-of-energy-on-earth"", ""text"": ""A short history of solar: From outer space to the cheapest source of energy on earth"", ""title"": ""A short history of solar: From outer space to the cheapest source of energy on earth"", ""isSubheading"": false}, {""slug"": ""undefined-how-moore-s-law-and-wright-s-law-can-help-us-to-get-our-expectations-for-the-future-right"", ""text"": ""How Moore’s Law and Wright’s Law can help us to get our expectations for the future right"", ""title"": ""How Moore’s Law and Wright’s Law can help us to get our expectations for the future right"", ""isSubheading"": false}, {""slug"": ""undefined-predicting-the-future-the-laws-of-gordon-moore-and-theodore-paul-wright"", ""text"": ""Predicting the future: The laws of Gordon Moore and Theodore Paul Wright"", ""title"": ""Predicting the future: The laws of Gordon Moore and Theodore Paul Wright"", ""isSubheading"": true}, {""slug"": ""undefined-wright-s-law-helps-us-to-get-our-expectations-for-the-future-right"", ""text"": ""Wright’s Law helps us to get our expectations for the future right"", ""title"": ""Wright’s Law helps us to get our expectations for the future right"", ""isSubheading"": true}, {""slug"": ""undefined-do-electricity-prices-follow-learning-curves"", ""text"": ""Do electricity prices follow learning curves?"", ""title"": ""Do electricity prices follow learning curves?"", ""isSubheading"": false}, {""slug"": ""undefined-fossil-fuels-and-nuclear-do-not-follow-learning-curves"", ""text"": ""Fossil fuels and nuclear do not follow learning curves"", ""title"": ""Fossil fuels and nuclear do not follow learning curves"", ""isSubheading"": false}, {""slug"": ""undefined-electricity-from-gas-should-we-expect-that-the-price-continues-to-fall"", ""text"": ""Electricity from gas: should we expect that the price continues to fall?"", ""title"": ""Electricity from gas: should we expect that the price continues to fall?"", ""isSubheading"": true}, {""slug"": ""undefined-why-did-nuclear-power-get-more-expensive-what-can-reverse-that-trend"", ""text"": ""Why did nuclear power get more expensive? What can reverse that trend?"", ""title"": ""Why did nuclear power get more expensive? What can reverse that trend?"", ""isSubheading"": true}, {""slug"": ""undefined-batteries-and-electricity-storage-follow-learning-curves-too"", ""text"": ""Batteries and electricity storage follow learning curves too"", ""title"": ""Batteries and electricity storage follow learning curves too"", ""isSubheading"": true}, {""slug"": ""undefined-scaling-up-low-carbon-sources-leads-to-lower-prices-let-s-not-waste-this-opportunity-for-our-planet-and-economy"", ""text"": ""Scaling up low-carbon sources leads to lower prices; let’s not waste this opportunity for our planet and economy"", ""title"": ""Scaling up low-carbon sources leads to lower prices; let’s not waste this opportunity for our planet and economy"", ""isSubheading"": false}, {""slug"": ""undefined-making-renewable-energy-irresistible-technological-progress-somewhere-turns-into-progress-everywhere"", ""text"": ""Making renewable energy irresistible: Technological progress somewhere turns into progress everywhere"", ""title"": ""Making renewable energy irresistible: Technological progress somewhere turns into progress everywhere"", ""isSubheading"": true}, {""slug"": ""undefined-conclusion"", ""text"": ""Conclusion"", ""title"": ""Conclusion"", ""isSubheading"": false}], ""body"": [{""text"": [{""type"": ""text"", ""value"": [{""text"": ""For the world to transition to low-carbon electricity, energy from these sources needs to be cheaper than electricity from fossil fuels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Fossil fuels dominate the global power supply because until very recently electricity from fossil fuels was far cheaper than electricity from renewables. This has dramatically changed within the last decade. In most places in the world power from new renewables is now cheaper than power from new fossil fuels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The fundamental driver of this change is that renewable energy technologies follow learning curves, which means that with each doubling of the cumulative installed capacity their price declines by the same fraction. The price of electricity from fossil fuel sources however does not follow learning curves so that we should expect that the price difference between expensive fossil fuels and cheap renewables will become even larger in the future."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is an argument for large investments into scaling up renewable technologies now. Increasing installed capacity has the extremely important positive consequence that it drives down the price and thereby makes renewable energy sources more attractive, earlier. In the coming years most of the additional demand for new electricity will come from low- and middle-income countries; we have the opportunity now to ensure that much of the new power supply will be provided by low-carbon sources."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Falling energy prices also mean that the real income of people rises. Investments to scale up energy production with cheap electric power from renewable sources are therefore not only an opportunity to reduce emissions, but also to achieve more economic growth – particularly for the poorest places in the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Summary"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world’s energy supply today is neither safe nor sustainable. What can we do to change this and make progress against this twin-problem of the status quo?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To see the way forward we have to understand the present. Today fossil fuels – coal, oil, and gas – account for "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/energy-mix#global-primary-energy-how-has-the-mix-changed-over-centuries"", ""children"": [{""text"": ""79%"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of the world’s energy production and as the chart below shows they have very large negative side effects. The bars to the left show the number of deaths and the bars on the right compare the greenhouse gas emissions. My colleague Hannah Ritchie explains the data in this chart in detail in her post "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/safest-sources-of-energy"", ""children"": [{""text"": ""‘What are the safest sources of energy?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""’."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This makes two things very clear. As the burning of fossil fuels "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/global-co2-emissions-fossil-land"", ""children"": [{""text"": ""accounts for 87%"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of the world’s CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" emissions, a world run on fossil fuels is not sustainable, they endanger the lives and livelihoods of future generations and the biosphere around us. And the very same energy sources lead to the deaths of many people "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""right now"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" – the air pollution from burning fossil fuels kills 3.6 million people in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/pollution-deaths-from-fossil-fuels"", ""children"": [{""text"": ""countries around the world"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" every year; this is "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""6-times"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" the annual death toll of all murders, war deaths, and terrorist attacks combined."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is important to keep in mind that electric energy is only one of several forms of energy that humanity relies on; the transition to low-carbon energy is therefore a bigger task than the transition to low-carbon electricity."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What the chart makes clear is that the alternatives to fossil fuels – renewable energy sources and nuclear power – are orders of magnitude safer and cleaner than fossil fuels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Why then is the world relying on fossil fuels?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Fossil fuels dominate the world’s energy supply because in the past they were "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""cheaper"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" than all other sources of energy. If we want the world to be powered by safer and cleaner alternatives, we have to make sure that those alternatives are cheaper than fossil fuels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""5-Bar-chart-–-What-is-the-safest-form-of-energy.png"", ""parseErrors"": []}, {""text"": [{""text"": ""The price of electricity from the long-standing sources: fossil fuels and nuclear power"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world’s electricity supply is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/electricity-prod-source-stacked?stackMode=relative&time=earliest..latest"", ""children"": [{""text"": ""dominated"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" by fossil fuels. Coal is by far the biggest source, supplying 37% of electricity; gas is second and supplies 24%. Burning these fossil fuels for electricity and heat is the largest single source of global greenhouse gases, causing 30% of global emissions."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart here shows how the electricity prices from the long-standing sources of power – fossil fuels and nuclear – have changed over the last decade. The data is published by Lazard."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To make comparisons on a consistent basis, energy prices are expressed in ‘levelized costs of energy’ (LCOE). You can think of LCOE from the perspective of someone who is considering building a power plant. If you are in that situation then the LCOE is the answer to the following question: What would be the minimum price that my customers would need to pay so that the power plant would break even over its lifetime?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""LCOE captures the cost of building the power plant itself as well as the ongoing costs for fuel and operating the power plant over its lifetime. It however does not take into account costs and benefits at an energy system level: such as price reductions due to low-carbon generation and higher systemic costs when storage or backup power is needed due to the variable output of renewable sources – we will return to the aspect of storage costs later."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This makes clear that it is a very crucial metric. If you as the power plant builder pick an energy source that has an LCOE that is higher than the price of the alternatives you will struggle to find someone who is willing to buy your expensive electricity."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What you see in the chart is that within the last 10 years the price of electricity from nuclear became more expensive, gas power became less expensive, and the price of coal power – the world’s largest source of electricity – stayed almost the same. Later we will see what is behind these price changes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""Price-of-electricity-new-fossil-and-nuclear.png"", ""parseErrors"": []}, {""text"": [{""text"": ""The price decline of electricity from renewable sources"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we want to transition to renewables, it is their price relative to fossil fuels that matters."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This chart here is identical to the previous one, but now also includes the price of electricity from renewable sources."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All of these prices – renewables as well as fossil fuels – are without subsidies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Look at the change in solar and wind energy in recent years. Just 10 years ago it wasn’t even close: it was much cheaper to build a new power plant that burns fossil fuels than to build a new solar photovoltaic (PV) or wind plant. Wind was 22%, and solar 223% more expensive than coal."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But in the last few years this has changed entirely."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Electricity from utility-scale solar photovoltaics cost $359 per MWh in 2009. Within just one decade the price declined by 89% and the relative price flipped: the electricity price that you need to charge to break even with the new average coal plant is now much higher than what you can offer your customers when you build a wind or solar plant."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It’s hard to overstate what a rare achievement these rapid price changes represent. Imagine if some other good had fallen in price as rapidly as renewable electricity: Imagine you’d found a great place to live back in 2009 and at the time you thought it’d be worth paying $3590 in rent for it. If housing had then seen the price decline that we’ve seen for solar it would have meant that by 2019 you’d pay just $400 for the same place."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I emphasized that it is the relative price that matters for the decision of which type of power plants are built. Did the price decline of renewables matter for the decisions of actual power plant builders in recent years? Yes it did. As you see in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/energy"", ""children"": [{""text"": ""our Energy Explorer"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", wind and solar energy were scaled up rapidly in recent years; in 2019 renewables accounted for 72% of all new capacity additions worldwide."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""Price-of-electricity-new-renewables-vs-new-fossil-no-geo.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Why is this happening? Learning curves and the price of solar photovoltaics modules"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How can this be? Why do we see the cost of renewable energy decline so very fast?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The costs of fossil fuels and nuclear power depend largely on two factors, the price of the fuel that they burn and the power plant’s operating costs."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Renewable energy plants are different: their operating costs are comparatively low and they don’t have to pay for any fuel; their fuel doesn’t have to be dug out of the ground, their fuel – the wind and sunlight – comes to them. What is determining the cost of renewable power is the cost of the power plant, the cost of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""the technology itself"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To understand why solar "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""power"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" got so cheap we have to understand why solar "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""technology"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" got cheap. For this, let’s go back in time for a moment."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The first price point for usable solar technology that I can find is from the year 1956. At that time the cost of just one watt of solar photovoltaic capacity was $1,865 (adjusted for inflation and in 2019 prices)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" One watt isn’t much. Today one single solar panel of the type homeowners put on their roofs produces around 320 watts of power."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This means that at the price of 1956 one of today’s solar modules would cost $596,800."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At this price – more than half a million dollars for a single panel – solar was obviously hopelessly uncompetitive with fossil fuels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Then why didn’t the history of solar technology end right there?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are two reasons why instead of dying, solar has developed to become the world’s cheapest source of electricity today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Even at the very high price, solar technology did find a use. It is a technology that literally came from outer space. The very first practical use of solar power was to supply electricity for a satellite, the Vanguard I satellite in 1958. It was in this high-tech niche where someone was willing to pay for solar technology even at that extremely high price."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The second important reason is that the price of solar modules declined when more of them were produced. More production gave us the chance to learn how to improve the production process: a classic case of learning-by-doing. The initial demand in the high-tech sector meant that some solar technology was produced and this initial production started a virtuous cycle of increasing demand and falling prices."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The visualization shows this mechanism. To satisfy increasing demand more solar modules get deployed, which leads to falling prices; at those lower prices the technology becomes cost-effective in new applications, which in turn means that demand increases. In this positive feedback loop solar technology has powered itself forward ever since its early days in outer space."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""narrow"", ""type"": ""image"", ""filename"": ""technology-cycle.png"", ""parseErrors"": []}, {""text"": [{""text"": ""A short history of solar: From outer space to the cheapest source of energy on earth"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""During the 1960s the main application of solar remained in satellites. But the virtuous cycle was set in motion and this meant that slowly, but steadily, the price of solar modules declined."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""With falling prices the technology came down from space to our planet. The first terrestrial applications in the 1970s were in remote locations where the connection to the wider electrical grid is costly – lighthouses, remote railroad crossings, or the refrigeration of "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/vaccination"", ""children"": [{""text"": ""vaccines"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The data point for 1976 in the top left corner of the chart shows the state of solar technology at the time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Back then the price of a solar module, adjusted for inflation, was US-$106 per watt. And as you see on the bottom axis, global installed solar PV capacity was only 0.3 megawatts. Relative to 1956 this was already a price decline of 94%, but relative to the world’s energy demand solar was still very expensive and therefore very small: a capacity of 0.3 megawatts is enough to provide electricity for about 20 people per year."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The time-series in the chart shows how the price of solar modules changed from then until now. The so-called ‘learning effect’ in solar technology is incredibly strong: while the installed capacity increased exponentially, the price of solar modules "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""declinedexponentially"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". The fact that both metrics changed exponentially can be nicely seen in this chart because both axes are logarithmic. On a logarithmic axis a measure that declines exponentially "", ""spanType"": ""span-simple-text""}, {""url"": ""https://blog.datawrapper.de/weeklychart-logscale/"", ""children"": [{""text"": ""follows a straight line"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This straight line that represents the relationship between "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""experience "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""– measured as the cumulative installed capacity of the technology – and the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""price"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of that technology is called the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""learning curve"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of that technology. The relative price decline associated with each doubling of experience is the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""learning rate"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of a technology."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is the virtuous cycle in action. More deployment means falling prices, which means more deployment. With solar technology it was for a long time the case that its increased deployment was made possible through government subsidies and mandates – arguably the most positive effect of these policies is that they too drove down the price of these new technologies along the learning curve. Paying for renewables at a high price point earlier allows everyone to pay less for them later."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""That more production leads to falling prices is not surprising – such ‘economies of scale’ are found in many corners of manufacturing. If you are already making one pizza, it isn’t that much extra work to make a second one."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What is truly mind blowing about solar technology is how very strong this effect is: For more than four decades each doubling of global cumulative capacity was associated with the same relative decline in prices."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The advances that made this price reduction possible span the entire production process of solar modules:"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-15"", ""children"": [{""children"": [{""text"": ""15"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" larger, more efficient factories are producing the modules; R&D efforts increase; technological advances increase the efficiency of the panels; engineering advances improve the production processes of the silicon ingots and wafers; the mining and processing of the raw materials increases in scale and becomes cheaper; operational experience accumulates; the modules are more durable and live longer; market competition ensures that profits are low; and capital costs for the production decline. It is a myriad of small improvements across a large collective process that drives this continuous price decline."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The learning rate of solar PV modules is 20.2%."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-16"", ""children"": [{""children"": [{""text"": ""16"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" With each doubling of the installed cumulative capacity the price of solar modules declines by 20.2%."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-17"", ""children"": [{""children"": [{""text"": ""17"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The high learning rate meant that the core technology of solar electricity declined rapidly. The price of solar modules declined from $106 to $0.38 per watt. A decline of 99.6%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To get our expectations for the future right we ought to pay a lot of attention to those technologies that follow learning curves. Initially we might only find them on a high-tech satellite out in space, but the future belongs to them. Renewable energy sources are not the only case; the most well-known case is the computer and the corresponding historical development there is ‘Moore’s Law’. If you are interested in getting your expectations about the future right, you are interested in how Moore’s Law helps us to see the future of technological development, and you want to know about whether it is indeed the case that scaled-up production "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""causes "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""declining prices you can read the following information box that takes a deeper look at it."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""solar-pv-prices-vs-cumulative-capacity.png"", ""parseErrors"": []}, {""text"": [{""text"": ""How Moore’s Law and Wright’s Law can help us to get our expectations for the future right"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Predicting the future: The laws of Gordon Moore and Theodore Paul Wright"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Solar modules are not the only technology where we see exponential progress. The case of exponential technological change that everyone knows of is Moore’s law – the observation of Intel’s co-founder Gordon Moore who noticed that the number of transistors on microprocessors doubled every two years. He first made this observation back in 1965 and until today this extraordinarily fast rate of technological progress still applies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Integrated circuits are the fundamental technology of computers and Moore’s law is what has driven the exponential progress in computers in recent decades – computers became rapidly cheaper, more energy efficient, and faster."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As you might have noticed Moore’s law is not stated in the same way that I’ve been looking at solar module prices. Moore’s law describes technological change as a function of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""time; for solar,"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" I am looking at price changes as a function of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""experience"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" – measured as the total amount of solar modules that were ever installed. This relationship, that each doubling in experience leads to the same relative decline in prices, was discovered much earlier than Moore’s law, by aerospace engineer Theodore Paul Wright in 1936."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-18"", ""children"": [{""children"": [{""text"": ""18"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" After him it is called "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Wright’s Law"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". Moore’s observation for the progress in computing technology can be seen as a special case of Wright’s Law."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-19"", ""children"": [{""children"": [{""text"": ""19"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Solar panels and computer chips are not the only technologies that follow his law. Have a look at "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/costs-of-66-different-technologies-over-time"", ""children"": [{""text"": ""our visualization"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of the price declines of 66 different technologies and the research referenced in the footnote"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-20"", ""children"": [{""children"": [{""text"": ""20"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How do we know that increasing experience is causing lower prices? After all it could be the other way around – production only increases after costs have fallen. In most settings this is difficult to disentangle empirically, but researchers François Lafond, Diana Greenwald, and Doyne Farmer found an instance where this question can be answered. In their paper ‘Can Stimulating Demand Drive Costs Down?’, they study the price changes at a time when reverse causality can be ruled out, when demand was clearly not the consequence of lower prices: the demand for military technology in the Second World War."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-21"", ""children"": [{""children"": [{""text"": ""21"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Their finding is that for technologies for which Wright’s Law applies, it is mostly the cumulative experience that determines the price. As demand for weapons grew production experience increased sharply and prices declined. When the war was over and demand shrank, the price decline reverted back to a slower rate. This is suggesting that it is really the cumulative experience that is driving the price decline that we are interested in."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""Transistor-Count-over-time.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Wright’s Law helps us to get our expectations for the future right"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you want to know what the future looks like one of the most useful questions to ask is which technologies follow Wright’s Law and which do not."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most technologies obviously do not follow Wright’s Law – the prices of bicycles, fridges, or coal power plants do not decline exponentially as we produce more of them. But those which do follow Wright’s Law – like computers, solar PV, and batteries – are the ones to look out for. They might initially only be found in very niche applications, but a few decades later they are everywhere."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you are unaware that technology follows Wright’s Law you can get your predictions very wrong. At the dawn of the computer age in 1943 IBM president Thomas Watson famously said \""I think there is a world market for maybe five computers.\"""", ""spanType"": ""span-simple-text""}, {""url"": ""#note-22"", ""children"": [{""children"": [{""text"": ""22"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" At the price point of computers at the time that was perhaps perfectly true, but what he didn’t foresee was how rapidly the price of computers would fall. From its initial niche when there was perhaps truly only demand for five computers they expanded to more and more applications and the virtuous cycle meant that the price of computers declined further and further. The exponential progress of computers expanded their use from a tiny niche to the defining technology of our time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Solar modules are on the same trajectory, as we’ve seen before. At the price of solar modules in the 1950s it would have sounded quite reasonable to say, “I think there is a world market for maybe five solar modules.” But as a prediction for the future this statement too would have been ridiculously wrong."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To get our expectations about the future right we are well advised to take the exponential change of Wright’s Law seriously. My colleagues Doyne Farmer, François Lafond, Penny Mealy, Rupert Way, Matt Ives, Linus Mattauch, Cameron Hepburn and others have done important pioneering work in this field. A central paper of their work is Farmer’s and Lafond’s ‘How predictable is technological progress?’ from 2016."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-23"", ""children"": [{""children"": [{""text"": ""23"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The focus of this research paper is the price of solar modules so that we avoid repeating Watson’s mistake for solar technology. They lay out in detail what I discussed here: how solar modules decline in price, how demand is driving this change, and how we can learn about the future by relying on these insights."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To get our expectations for the future right we ought to pay attention to those technologies that follow Wright’s law. Initially we might only find them on a high-tech satellite out in space, but the future belongs to them."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Do electricity prices follow learning curves?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Solar PV modules might very well follow a rapidly declining learning curve, but solar modules themselves are not what we want. We want the electricity that they produce. Does the price of solar "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""electricity"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" follow a learning curve?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The visualization shows the relevant data."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-24"", ""children"": [{""children"": [{""text"": ""24"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" On the vertical axis you see again the LCOE price for electricity and on the horizontal axis you now find the cumulative installed capacity."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-25"", ""children"": [{""children"": [{""text"": ""25"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" As in the solar module chart, both variables are plotted on logarithmic scales so that the line on the charts represents the learning rate for these technologies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In bright orange you see the development for the price of power from solar PV over the last decade. The learning curve relationship that we saw for the price of solar "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""modules"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" also holds for the price of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""electricity"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". The learning rate is actually even faster: At each doubling of installed solar capacity the price of solar electricity declined by 36% – compared to 20% for solar modules."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Wind power – shown in blue – also follows a learning curve. The onshore wind industry achieved a learning rate of 23%. Every doubling of capacity was associated with a price decline of almost a quarter."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Offshore wind had a learning rate of 10% and is still relatively expensive – only 25% cheaper than nuclear and a bit more expensive than coal. But for two reasons experts expect the power from offshore wind to become very cheap in the coming years, larger wind turbine sizes and the fact that the consistent winds out on the sea allows higher load factors."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-26"", ""children"": [{""children"": [{""text"": ""26"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The obvious similarity of onshore and offshore wind also means that learning effects in one industry can be transferred to the other."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""3-Learning-curves-for-electricity-prices.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Fossil fuels and nuclear do not follow learning curves"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Electricity generation from renewables is getting rapidly cheaper. What about its competitors? Let’s look first at coal."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Why is electricity from coal not getting cheaper?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Coal, the world’s largest source of electricity, is also included in the chart. The global price of electricity from new coal (LCOE) declined from $111 to $109. While solar got 89% cheaper and wind 70%, the price of electricity from coal declined by merely 2%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The stagnating price of coal power in the last decade is not unusual. The historical development of the price of coal power is nowhere close to what we’ve been seeing for renewable power. Neither the price of the coal nor the price of the coal plants followed a learning curve, the prices didn’t even decline over the long run."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-27"", ""children"": [{""children"": [{""text"": ""27"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Electricity from coal was historically cheap and still is, but it is not getting cheaper. There are two reasons we shouldn’t expect this to change much in the future:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""First, there is little room for improving the efficiency of coal power plants substantially. Typical plants have efficiencies of around 33%, while the most efficient ones today reach 47%."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-28"", ""children"": [{""children"": [{""text"": ""28"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Even a dramatic, unprecedented improvement from an efficiency of one-third to two-thirds would only correspond to the progress that solar PV modules make every 7.5 years."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-29"", ""children"": [{""children"": [{""text"": ""29"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Second, the price of electricity from all fossil fuel is not only determined by the technology but to a significant extent by the cost of the fuel itself. The cost of coal that the power plant burns makes up about 40% of total costs."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-30"", ""children"": [{""children"": [{""text"": ""30"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": ""This means that for all non-renewable power plants which have these fuel costs there is a hard lower bound to how much the cost of their electricity can possibly decrease. Even if the price for constructing the power plant would decline, the price of the fuel means that there is a floor below which the price of electricity cannot pass."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For these reasons it should not be surprising that coal power does not follow a learning curve."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Electricity from gas: should we expect that the price continues to fall?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Electricity from gas, the second largest fossil fuel source, did become cheaper over the last decade."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-31"", ""children"": [{""children"": [{""text"": ""31"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" As we saw above, electricity from combined cycle gas plants declined by 32% to a global average cost of $56 per MWh."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-32"", ""children"": [{""children"": [{""text"": ""32"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The costs of building a gas plant declined during some periods in the last 70 years, as Rubin et al (2015) show."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-33"", ""children"": [{""children"": [{""text"": ""33"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" But the main reason the price of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""gas electricity "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""declined over the last decade is that the price of gas itself happened to decline over this particular period. After a peak in 2008 the price of gas declined steeply. The increased supply from fracking is one key reason. This price decline of gas, however, is not part of a long-run development. The price of gas today is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/fossil-fuel-price-index"", ""children"": [{""text"": ""higher"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" than two or three decades ago."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For the same reasons as discussed for coal – limited learning and fuel costs as a floor – we should therefore not expect the price of electricity from gas to decline significantly over the coming decades and we should certainly not expect a learning curve effect similar to what we are seeing for renewables."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Why did nuclear power get more expensive? What can reverse that trend?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For nuclear power you see the data since 2009 in the chart. Nuclear power has increased in price."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This increase is part of a longer term trend. In many places building a power plant has become more expensive as the studies reviewed in Rubin et al (2015) document."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-33"", ""children"": [{""children"": [{""text"": ""33"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This is of course very unfortunate, since nuclear is both a low-carbon source of electricity and one of the safest sources of electricity as we have seen in the very first chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One reason for rising prices is increased regulation for nuclear power, which has the important benefit of increased safety. A second reason is that the world has not built many nuclear power plants in recent years so that supply chains are small, uncompetitive, and are not benefiting from economies of scale."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-34"", ""children"": [{""children"": [{""text"": ""34"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Both of these reasons explain why the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""global average"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" LCOE price has gone up. But for nuclear there are large differences in price trends between countries: Prices and construction times have increased significantly in the US and the UK, while France and South Korea were at least able to keep prices and construction times constant."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-35"", ""children"": [{""children"": [{""text"": ""35"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Michel Berthélemy and Lina Escobar Rangel (2015) explain that those countries that were able to avoid price surges are countries that do not stand out in regulating nuclear power less, but in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""standardizing"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" the construction of reactors more."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-35"", ""children"": [{""children"": [{""text"": ""35"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Learning, after all, means transferring the knowledge gathered in one instance to another. No repetition, no learning."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is in sharp contrast with renewables in particular. While nuclear technology is not very standardized and gets build very rarely, solar PV modules and wind plants are the exact opposite, very standardized and extremely often built."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-36"", ""children"": [{""children"": [{""text"": ""36"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One hope is that a new boom in nuclear power and increased standardization of the reactors would lead to declining costs of nuclear power."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But there is no strong price decline anywhere, and certainly nothing that could be characterized by a steep learning curve."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But nuclear could still become more important in the future because it can complement renewables where these energy sources have their weaknesses: First, intermittency of electricity from renewables remains a challenge and a viable energy mix of the future post-carbon world will likely include all low-carbon sources, renewables as well as nuclear power. And second, the land use of renewables is large and a big environmental benefit of nuclear power is that it uses very little land."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-37"", ""children"": [{""children"": [{""text"": ""37"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And beyond the existing nuclear fission reactors there are several teams working towards nuclear fusion reactors, which would potentially entirely change the world’s energy supply."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-38"", ""children"": [{""children"": [{""text"": ""38"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To make nuclear reactors competitive with fossil fuels is again an argument for carbon taxes. Nuclear reactors kill 350-times less people per unit of energy than fossil fuel plants, and as a low-carbon technology they can be key in making the transition away from fossil fuels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Batteries and electricity storage follow learning curves too"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One of the downsides of renewable sources is their intermittent supply cycle. The sun doesn’t always shine and the wind doesn't always blow. Technologies like batteries that store electric power are key to balance the changing supply from renewables with the inflexible demand for electricity."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Fortunately electricity storage technologies are also among the few technologies that are following learning curves – their learning curve are indeed "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""very "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""steep, as the chart here shows."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart is from my colleague Hannah Ritchie; she documents "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/battery-price-decline"", ""children"": [{""text"": ""in her article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that the price of batteries declined by 97% in the last three decades."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-39"", ""children"": [{""children"": [{""text"": ""39"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At their current price there might only be demand for five large power storage systems in the world, but as a prediction for the future this might sound foolish one day (if you don’t know what I’m alluding to, you skipped reading the text in the fold-out box above)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""Battery-cost-learning-curve.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Scaling up low-carbon sources leads to lower prices; let’s not waste this opportunity for our planet and economy"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The takeaway of the previous discussion is that renewables follow steep learning curves and fossil fuels do not. A key reason is that renewables do not have fuel costs and comparatively small operating and maintenance costs, which means that the LCOE of renewable energy scales with the cost of their technologies. And the key technologies of renewable energy systems – solar, wind, and batteries – themselves follow a learning curve: each doubling of their installed capacity leads to the same decline of costs."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we are serious about making the transition to a low-carbon global energy system we have a fantastic opportunity in front of us. Scaling up renewable energy systems doesn’t only have the direct benefit of more low-carbon energy, but has an indirect side effect that is even more important: "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""cheaper"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" energy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The learning rates for wind and solar PV are exceptionally fast. It is extremely rare to find technologies of this kind."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Solar and wind have one more big advantage. While there is often little agreement in how to reduce greenhouse gas emissions, expanding solar and wind power are two options that are hugely popular with large majorities. Even in the often polarized US, renewables have the support of strong majorities of Democrats and Republicans. 85% of Americans are in favour of expanding wind power and 92% are in favor of expanding solar power, and in other countries the support is often even higher."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-40"", ""children"": [{""children"": [{""text"": ""40"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Today, at a time when the global economy – and workers around the world – suffer greatly from the COVID-19 recession and when interest rates are low (or even negative), scaling up renewable energy systems offers us a great chance to move forward. It is rare to have a policy option that leads to more jobs, cheaper prices for consumers, and a greener, safer planet."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-41"", ""children"": [{""children"": [{""text"": ""41"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The more renewable energy technologies we deploy, the more their costs will fall. More growth will mean even more growth."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Making renewable energy irresistible: Technological progress somewhere turns into progress everywhere"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One last argument on why lower prices due to technological change are so crucial for making the transition to the post-carbon world. If rich countries make investments into renewable technology that drive down the price along the learning curves, they are not just working towards the transition from fossil fuels to renewable energy for themselves, but for the entire world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The relative price of fossil fuels and renewables is key to anyone’s decision of which power plant to build. Making low-carbon technology cheap is a policy goal that doesn’t only reduce emissions in your own country but in the entire world, forever."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Driving down the price of low-carbon energy should be seen as one of the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://techcrunch.com/2019/02/15/how-to-decarbonize-america-and-the-world/"", ""children"": [{""text"": ""most important goals"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (and achievements) of clean energy policy, because it matters beyond the borders of the country that is adopting that policy. This is the beautiful thing about technology: once it is invented somewhere it can help everywhere."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The biggest growth in electricity demand in the coming years will not come from rich countries, but the poorer, yet rapidly developing countries in Africa and Asia."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-42"", ""children"": [{""children"": [{""text"": ""42"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The steep decline of solar power is a particularly fortunate development for many of these countries that often have "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/Solar_power#/media/File:SolarGIS-Solar-map-World-map-en.png"", ""children"": [{""text"": ""sunny climates"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Energy systems have very long path dependencies, since it is very costly to build a power plant or to decide to shut a power plant down. Investments in renewable technologies now will therefore have very long-term benefits. Every instance when a country or an electricity company decides to build a low-carbon power plant instead of a coal plant is a win for decades. Low prices are the key argument to convince the world – especially those places that have the least money – to build low-carbon power systems for a sustainable future."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Conclusion"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One of the very worst misconceptions about the challenge of climate change is that it is an easy problem to solve. It is not."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Climate policy is exceedingly difficult"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-43"", ""children"": [{""children"": [{""text"": ""43"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" and the technological challenges are much larger than the electricity sector alone since it is only one of several big sectors that need to be decarbonized. We need change and technological innovation across all these sectors at a scale that matches the problem and the problem is big."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But what the consideration of changing electricity prices has shown is that we have a clear option in front of us where we are able to make very important progress. Low-carbon technologies that were so expensive just a few decades ago that they were only affordable for satellites have came down steadily in price and now provide the cheapest electricity on the planet (which implies that they are now the cheapest source of energy that humanity ever had access to)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Driving down the costs of renewables is key to a green, low-carbon future, but it also has a big benefit for people today: Your real income is the ratio between what you are paid and the price of the goods and services you pay for – that is why falling energy prices means that people’s real income is growing. Falling energy prices means economic growth and less poverty."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The reason we can hope for a future in which renewables are deployed rapidly and where fossil fuel plants become increasingly unprofitable is that renewables follow steep learning curves, and fossil fuels do not. We are heading towards a future in which the disadvantage of fossil fuels will keep increasing."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But limiting climate change is a race against time and we have a long way to go. There is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://thebreakthrough.org/issues/energy/peak-co2-emissions-2019"", ""children"": [{""text"": ""a good chance"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that the world has reached the peak of greenhouse gas emissions last year. A huge milestone, but the peak is not the goal; we need to get all the way down to net-zero."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The argument for scaling these technologies up sooner rather than later is that we are getting to the low-carbon, low-cost future "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""faster"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". This ensures that the power plants that will be built in the coming years are not fossil fuel plants but renewables."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is key to bringing down greenhouse gas emissions fast. And it has the side effects that it saves people from air pollution and it reduces energy prices – which means growing incomes and declining poverty."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""I would like to thank Hannah Ritchie, François Lafond, Rupert Way, Marcel Gerber, Ernst van Woerden, Charlie Giattino, and Breck Yunits for reading drafts of this and for their very helpful comments and ideas."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Acknowledgements"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""065df89be2dfb4bd9ea4b7d064701dd31535e60a"": {""id"": ""065df89be2dfb4bd9ea4b7d064701dd31535e60a"", ""index"": 9, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""J. Perlin (1999) – From space to earth: the story of solar electricity. aatech publications, Ann Arbor, MI (1999) via Doyne Farmer and Fracois Lafond (2016) – How predictable is technological progress? Research Policy. Volume 45, Issue 3, April 2016, Pages 647-665. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/j.respol.2015.11.001"", ""children"": [{""text"": ""https://doi.org/10.1016/j.respol.2015.11.001"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""$256 in 1956 adjusted for prices – using the US GDP deflator – equals $1865 in 2019 US-$ according to ("", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.multpl.com/gdp-deflator"", ""children"": [{""text"": ""https://www.multpl.com/gdp-deflator"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""08979ebf6b379b9910b83b0ebc7844fd7d9c265e"": {""id"": ""08979ebf6b379b9910b83b0ebc7844fd7d9c265e"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""“Enhanced levelised cost” is an approach that aims to adjust for this, but its measurement is still in its early stages. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.carbonbrief.org/wind-and-solar-are-30-50-cheaper-than-thought-admits-uk-government"", ""children"": [{""text"": ""Simon Evans discusses ‘enhanced levelised costs’ for different electricity sources in the UK"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""15c73e61637da3a2b55561ac02071dbbdd4b42c9"": {""id"": ""15c73e61637da3a2b55561ac02071dbbdd4b42c9"", ""index"": 42, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Carbon pricing is a policy that would make those who actually cause emissions pay for them (the richest people in the world that enjoy the best living conditions in human history), but most governments fail to implement carbon prices, and where they exist they are often too low (which has the consequence that the poorest people on the planet are ‘paying’ most for carbon emissions, since it is them who are suffering the severest consequences)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""1623f28cdb098cda3fcaf86245f44328f7bf6b15"": {""id"": ""1623f28cdb098cda3fcaf86245f44328f7bf6b15"", ""index"": 13, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""How much electricity can be generated from 0.3 megawatts of electricity?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As a back-of-the-envelope calculation, I used the oldest data for Germany that I could find, which relates to the 1990s, and took an average to average over better and worse years. In the 1990s Germany had 48.5 MW of solar capacity and generated 23,750 MWh of electricity. This means that in these circumstances and with this technology (surely much better than the technology in 1976) they generated 145,040 kWh per solar PV capacity of 0.3 MW."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The electricity demand of a person in Germany is 7,333kWh per year so that 0.3MW could provide electricity for 20 people (145,040kWh/7,333kWh=19.78)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""1b73ad0869803fcc26110fae5672d7aac84a7459"": {""id"": ""1b73ad0869803fcc26110fae5672d7aac84a7459"", ""index"": 34, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See Michel Berthélemy and Lina Escobar Rangel (2015) – Nuclear reactors' construction costs: The role of lead-time, standardization and technological progress. In Energy Policy Volume 82, July 2015, Pages 118-130. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/j.enpol.2015.03.015"", ""children"": [{""text"": ""https://doi.org/10.1016/j.enpol.2015.03.015"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""1e2ec326708c1cdaa8c4f8a0c22d81b1c72c3980"": {""id"": ""1e2ec326708c1cdaa8c4f8a0c22d81b1c72c3980"", ""index"": 24, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For fossil fuels and nuclear we show installed capacity at each point in time (because we are not aware of any data on the cumulatively built capacity for these energy sources). I am however not expecting a large difference between installed and cumulatively built capacity – especially over a 10-year time span and for power sources that have been scaled up largely before 2009."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""21257911829137c751ddb46d292e59d8d3db7a0b"": {""id"": ""21257911829137c751ddb46d292e59d8d3db7a0b"", ""index"": 23, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""IRENA 2020 for all data on renewable sources; Lazard for the price of electricity from nuclear and coal – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://pris.iaea.org/PRIS/WorldStatistics/WorldTrendNuclearPowerCapacity.aspx"", ""children"": [{""text"": ""IAEA for nuclear capacity"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://globalenergymonitor.org/coal/global-coal-plant-tracker/"", ""children"": [{""text"": ""Global Energy Monitor for coal capacity"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""2c6d66c53b64b93260b9d12579df4dc455bedaff"": {""id"": ""2c6d66c53b64b93260b9d12579df4dc455bedaff"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""In 2016 (the latest sectoral breakdown available) global "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/total-ghg-emissions?tab=chart&time=earliest..latest&country=~OWID_WRL"", ""children"": [{""text"": ""greenhouse gas emissions were"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" 49.36 billion tonnes CO2eq. Electricity and heat generation was responsible for 15.01 billion tonnes CO2eq."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Electricity and heat generation therefore accounted for [49.36 / 15.01 * 100 = 30%] of global emissions. This data is sourced from "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.climatewatchdata.org/ghg-emissions"", ""children"": [{""text"": ""Climate Watch"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and the World Resources Institute."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""2f4f9ef9116a383c7205c1cf4c75c8fb78d1e9b9"": {""id"": ""2f4f9ef9116a383c7205c1cf4c75c8fb78d1e9b9"", ""index"": 26, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The price of coal plants over time was studied in McNerney et al (2011) and the authors find that after a decline of construction costs from 1902 until around 1970, the price then "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""increased "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""for two decades from 1970 until 1990"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "". "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""They attribute this cost increase to increased restrictions on the tolerable pollution (air pollution has "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/outdoor-air-pollution#the-long-term-decline-of-air-pollution-in-rich-countries"", ""children"": [{""text"": ""fallen rapidly"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in industrialized countries since 1970). From around 1990 onwards the price of coal plants remained largely unchanged."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""J. McNerney, J.D. Farmer, J.E. Trancik (2011) – Historical costs of coal-fired electricity and implications for the future Energy Policy, 39 (6) (2011), pp. 3042-3054 "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/j.enpol.2011.01.037"", ""children"": [{""text"": ""https://doi.org/10.1016/j.enpol.2011.01.037"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The price of coal itself has fluctuated over the last 150 years, but without a clear long run trend as the same authors show. Falling transportation costs have made coal cheaper for power plants, but more recently "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/coal-prices?time=2001..latest"", ""children"": [{""text"": ""the price of coal increased"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and overall the price of coal has not declined over the long run."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""30cc37d93453ddd7ed2e3151f5bd60caa2c81c50"": {""id"": ""30cc37d93453ddd7ed2e3151f5bd60caa2c81c50"", ""index"": 17, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Theodore Paul Wright (1936) – Factors affecting the cost of airplanes. J. Aeronaut. Sci., 3 (4) (1936), pp. 122-128"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""319533e2c8bdb380f574fb51214ddda7bf8fdfba"": {""id"": ""319533e2c8bdb380f574fb51214ddda7bf8fdfba"", ""index"": 19, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Nagy B, Farmer JD, Bui QM, Trancik JE (2013) Statistical Basis for Predicting Technological Progress. PLoS ONE 8(2): e52669. https://doi.org/10.1371/journal.pone.0052669"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Many more references can be found in Doyne Farmer and Fracois Lafond (2016) – How predictable is technological progress? Research Policy. Volume 45, Issue 3, April 2016, Pages 647-665. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/j.respol.2015.11.001"", ""children"": [{""text"": ""https://doi.org/10.1016/j.respol.2015.11.001"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The price of Ford’s Model T followed Wright’s law: each doubling of cumulative production led to the same relative decline in prices. What’s fascinating is that this decline hasn’t stopped until today. An 8hp car, as the Model T, costs what you’d expect: See Sam Korus (2019) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ark-invest.com/analyst-research/wrights-law-predicts-teslas-gross-margin/"", ""children"": [{""text"": ""Wright’s Law Predicted 109 Years of Auto Production Costs, and Now Tesla’s"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3d0e311367941f52bfe2a06acfc2a7ff1efcc4eb"": {""id"": ""3d0e311367941f52bfe2a06acfc2a7ff1efcc4eb"", ""index"": 38, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See also Schmidt, O., Hawkes, A., Gambhir, A. et al. The future cost of electrical energy storage based on experience rates. Nat Energy 2, 17110 (2017). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1038/nenergy.2017.110"", ""children"": [{""text"": ""https://doi.org/10.1038/nenergy.2017.110"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""An updated dataset from 2018 by the authors is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://figshare.com/articles/Update_2018_-_The_future_cost_of_electrical_energy_storage_based_on_experience_rates/7012202"", ""children"": [{""text"": ""available on FigShare here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Annual updates can be found via Bloomberg NEF, for example "", ""spanType"": ""span-simple-text""}, {""url"": ""https://about.bnef.com/blog/battery-pack-prices-fall-as-market-ramps-up-with-market-average-at-156-kwh-in-2019/"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3f7144555d0c6413e57e4c7187bb794e9115eebd"": {""id"": ""3f7144555d0c6413e57e4c7187bb794e9115eebd"", ""index"": 18, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Plausibly it isn’t just the passing of time that drives the progress in computer chips, but there too it is the learning that comes with continuously expanding the production of these chips. Lafond et al (2018) explain that the two laws produce the same forecasts when cumulative production grows exponentially, which is the case when production grows exponentially. More precisely, if production grows exponentially with some noise/fluctuations, then cumulative production grows exponentially with very little noise/fluctuations. As a result, the log of cumulative production is a linear trend and therefore predicting cost by the linear trend of time or the linear trend of log cumulative production give the same results."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Fracois Lafond, Aimee G. Bailey, Jan D. Bakker, Dylan Rebois, Rubina Zadourian, Patrick McSharry, and "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.doynefarmer.com/"", ""children"": [{""text"": ""J. Doyne Farmer"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (2018) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://francoislafond.files.wordpress.com/2015/11/wrightslawpaper20.pdf"", ""children"": [{""text"": ""How well do experience curves predict technological progress? A method for making distributional forecasts"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" In Technological Forecasting and Social Change  128, pp 104-117, 2018. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.arxiv.org/abs/1703.05979"", ""children"": [{""text"": ""arXiv"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.sciencedirect.com/science/article/pii/S0040162517303736"", ""children"": [{""text"": ""Publisher"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.dropbox.com/sh/w7jvzijblb4nkex/AAC2R-ml3JvIjFfBZtUTPlkta?dl=0"", ""children"": [{""text"": ""Data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://francoislafond.files.wordpress.com/2019/12/forecast_tech_progress-1.zip"", ""children"": [{""text"": ""Code"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""See also Nagy B, Farmer JD, Bui QM, Trancik JE (2013) Statistical Basis for Predicting Technological Progress. PLoS ONE 8(2): e52669. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1371/journal.pone.0052669"", ""children"": [{""text"": ""https://doi.org/10.1371/journal.pone.0052669"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Wright’s law for solar PV modules has also been given its own name; some call it "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/Swanson%27s_law"", ""children"": [{""text"": ""Swanson’s Law (Wiki)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""44b320cd0c5411e375d65d94af288c98a5b077cb"": {""id"": ""44b320cd0c5411e375d65d94af288c98a5b077cb"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""It is very hard to find anything else that declines in price just as fast as electricity from renewable sources.The report by IRENA finds that for the 531 individual items that are used to compile the UK’s Consumer Price Index (CPI), only five items have declined more rapidly: strawberries, fruit smoothies, internet computer games, household cleaner and underground/metro fares outside London. But of course most people spend more money on electricity than on strawberries.IRENA (2020) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.irena.org/publications/2020/Jun/Renewable-Power-Costs-in-2019"", ""children"": [{""text"": ""Renewable Power Generation Costs in 2019"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", International Renewable Energy Agency"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""4ddb7e945f39abb440f91979e8ac7920354a4638"": {""id"": ""4ddb7e945f39abb440f91979e8ac7920354a4638"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The data source is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.lazard.com/perspective/lcoe2019"", ""children"": [{""text"": ""Lazard's Levelized Cost of Energy 2019"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – the big advantage of this source is that it includes the cost of electricity from a wide range of sources."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""4f409cab134f4194229eb2907258a7377ed3d2b0"": {""id"": ""4f409cab134f4194229eb2907258a7377ed3d2b0"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""IRENA (2020) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.irena.org/publications/2020/Jun/Renewable-Power-Costs-in-2019"", ""children"": [{""text"": ""Renewable Power Generation Costs in 2019"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", International Renewable Energy Agency"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""5277ecf4e1f4740d8fc6014e35562e33150fdb5b"": {""id"": ""5277ecf4e1f4740d8fc6014e35562e33150fdb5b"", ""index"": 30, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""There are arguments for and against gas as a source of electricity. In comparison with coal, the world’s dominating source of electricity, gas is both safer and cleaner, as we see in the first chart: the death rate from air pollution and accidents is 9-times lower and the greenhouse gas emissions are 40% lower per unit of produced energy. A third important consideration is that while power from gas peakers is expensive they can react quickly and provide electricity at peak times or when the output from other sources, especially renewables, drops."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On the other hand it is of course the case that gas is much more deadly and emits much more carbon than nuclear and renewables."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Good carbon pricing could strike a balance where the low-carbon alternatives can continue to grow and gas can take over from coal. At a higher carbon price, gas combined with CCS – carbon capture and storage – can become cost-effective sooner. The UK has implemented a carbon price and the government there expects that from 2025 onwards the levelised cost for gas-with-CCS to be cheaper than unabated gas. See: Department for Business, Energy & Industrial Strategy (2020) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.gov.uk/government/publications/beis-electricity-generation-costs-2020"", ""children"": [{""text"": ""BEIS electricity generation cost report"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Published 24 August 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""See also the discussion of this report: Simon Evans (2020) –"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.carbonbrief.org/wind-and-solar-are-30-50-cheaper-than-thought-admits-uk-government"", ""children"": [{""text"": "" Wind and solar are 30-50% cheaper than thought,admits UK government"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". In Carbon Brief."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""59ed097bd502cd855af2336a3891f736106b2107"": {""id"": ""59ed097bd502cd855af2336a3891f736106b2107"", ""index"": 16, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Since it is sometimes wrongly claimed: It is not the case that a constant learning rate implies that the cost of a technology eventually would need to decline to 0."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This misunderstanding does not consider the driving force appropriately. It is the doubling"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""of the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""cumulative "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""number of units produced that drives the cost decline. Achieving a doubling of that becomes harder and harder as total production increases. Once the cumulative production is already very high, each doubling of cumulative capacity will take longer and longer. Eventually demand will level off such that the price decline slows down and would stop when the cumulative production of the technology satisfies demand."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""687d4221641dbc7925dc95b61f47db809fa6d86f"": {""id"": ""687d4221641dbc7925dc95b61f47db809fa6d86f"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The other two big energy sectors are heat and transport; in the coming years it is very likely that the share of electric energy will increase, because a larger share of transport will be electrified."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.iea.org/reports/world-energy-outlook-2019/electricity"", ""children"": [{""text"": ""IEA reports"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that electricity’s share in total final energy consumption was 19% in 2018 and expects it to increase to  24% in 2040."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""746b73583e2c80e0183f052a8fd208ff562e2e63"": {""id"": ""746b73583e2c80e0183f052a8fd208ff562e2e63"", ""index"": 41, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.iea.org/reports/world-energy-outlook-2020/outlook-for-electricity#abstract"", ""children"": [{""text"": ""IEA World Energy Outlook 2020 section on electricity"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7f22de3492365714bd2ca7731d7b38fedb1d060b"": {""id"": ""7f22de3492365714bd2ca7731d7b38fedb1d060b"", ""index"": 12, ""content"": [{""type"": ""text"", ""value"": [{""url"": ""https://www1.eere.energy.gov/solar/pdfs/solar_timeline.pdf"", ""children"": [{""text"": ""The History of Solar"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". US Department of Energy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""8f6a7fb0bdc2378dfe1f539d5d0cd314be706e77"": {""id"": ""8f6a7fb0bdc2378dfe1f539d5d0cd314be706e77"", ""index"": 40, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Two papers to read on this point:Rupert Way, François Lafond, Fabrizio Lillo, Valentyn Panchenko, J. Doyne Farmer (2019) – Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves. In Journal of Economic Dynamics and Control. Volume 101, April 2019, Pages 211-238."", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/j.jedc.2018.10.006"", ""children"": [{""text"": "" https://doi.org/10.1016/j.jedc.2018.10.006"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Farmer, J.D., Hepburn, C., Ives, M.C., Hale, T., Wetzer, T., Mealy, P., Rafaty, R., Srivastav, S. & Way, R. (2019). 'Sensitive intervention points in the post-carbon transition'. Science, 364(6436), pp. 132-134."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9148dc2a6f962d6eba55d1ac0fb5428d7bba01b2"": {""id"": ""9148dc2a6f962d6eba55d1ac0fb5428d7bba01b2"", ""index"": 35, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""A rough back of the envelope calculation "", ""spanType"": ""span-simple-text""}, {""url"": ""https://cleantechnica.com/2020/11/13/what-does-bill-gates-favorite-energy-guru-vaclav-smil-get-wrong/"", ""children"": [{""text"": ""by Michael Barnard"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" makes this clear \""There is about 650 gigawatts (GW) of capacity of wind energy right now, as one example. The average wind turbine is about 2 megawatts (MW) in capacity globally, as new ones are almost always bigger and often much bigger. That means that there are about 325,000 wind turbines that have been built, and it means that there are almost a million wind turbine blades. Similarly, there’s about about 584 GW of solar globally. The average solar panel is about 200 Watts in capacity, so that’s about 3 billion solar panels installed already.\"""", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""99b519e2e1d36a97fd3e71370e9a5821f08d5b0b"": {""id"": ""99b519e2e1d36a97fd3e71370e9a5821f08d5b0b"", ""index"": 22, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Doyne Farmer and Fracois Lafond (2016) – How predictable is technological progress? Research Policy. Volume 45, Issue 3, April 2016, Pages 647-665. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/j.respol.2015.11.001"", ""children"": [{""text"": ""https://doi.org/10.1016/j.respol.2015.11.001"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""See also: de La Tour, A., Glachant, M. & Ménière, Y. (2013) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.sciencedirect.com/science/article/abs/pii/S0360544213007883"", ""children"": [{""text"": ""Predicting the costs of photovoltaic solar modules in 2020 using experience curve models"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". In "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Energy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" 62, 341–348."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""99cd091300d0e408225c0d5755ff325367fc343f"": {""id"": ""99cd091300d0e408225c0d5755ff325367fc343f"", ""index"": 37, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Recent relevant coverage includes "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.nytimes.com/2020/09/29/climate/nuclear-fusion-reactor.html?referringSource=articleShare"", ""children"": [{""text"": ""Compact Nuclear Fusion Reactor Is ‘Very Likely to Work,’ Studies Suggest"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (in the New York Times) and somewhat dated, but still relevant and fascinating "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.newyorker.com/magazine/2014/03/03/a-star-in-a-bottle"", ""children"": [{""text"": ""A Star in a Bottle"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in the New Yorker."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9acbef1a897f9e3904de3651294ef961475439b9"": {""id"": ""9acbef1a897f9e3904de3651294ef961475439b9"", ""index"": 28, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Doyne Farmer and Francois Lafond (2016) – How predictable is technological progress? Research Policy. Volume 45, Issue 3, April 2016, Pages 647-665. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/j.respol.2015.11.001"", ""children"": [{""text"": ""doi.org/10.1016/j.respol.2015.11.001"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9afeaef4dac3104552eafc78f23a3534f2aa6028"": {""id"": ""9afeaef4dac3104552eafc78f23a3534f2aa6028"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""In a study published in the Proceedings of the National Academy of Sciences, Jos Lelieveld et al. (2019) estimated that 5.6 million people died from anthropogenically caused air pollution. Of these 5.6 million, 3.6 million were attributed to fossil fuels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Lelieveld, J., Klingmüller, K., Pozzer, A., Burnett, R. T., Haines, A., & Ramanathan, V. (2019). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.pnas.org/content/116/15/7192/"", ""children"": [{""text"": ""Effects of fossil fuel and total anthropogenic emission removal on public health and climate"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Proceedings of the National Academy of Sciences, 116(15), 7192-7197"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The death toll of the three counts of violence for 2017 "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/annual-number-of-deaths-by-cause"", ""children"": [{""text"": ""according to the IHME"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" is 561,511."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""• Homicides: 405,346 deaths"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""• War battles: 129,720 deaths"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""• Terrorism: 26,445 deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a5f5c17679fca3136fb72e0a4b73b0fb09922c68"": {""id"": ""a5f5c17679fca3136fb72e0a4b73b0fb09922c68"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""In the following section we will look into their cost structures in detail."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a91a79895a27cd1b5727a22eec45504081ca8578"": {""id"": ""a91a79895a27cd1b5727a22eec45504081ca8578"", ""index"": 11, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This is the price per watt multiplied by the output of today’s typical solar panel: 320W * 1865$/W= $596,800."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b9e9dfcd8e7563afcb66e58823c7a1efce43463c"": {""id"": ""b9e9dfcd8e7563afcb66e58823c7a1efce43463c"", ""index"": 36, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""David J. 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In Energy Policy, 123:700-710, 2018, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://dx.doi.org/10.2139/ssrn.2891516"", ""children"": [{""text"": ""http://dx.doi.org/10.2139/ssrn.2891516"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c89aa1c590495d1bf3fadfa0d26ceb5b6f0f1fdf"": {""id"": ""c89aa1c590495d1bf3fadfa0d26ceb5b6f0f1fdf"", ""index"": 15, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""As one would expect, the exact learning rate differs slightly across studies, mostly due to differences in the chosen data source, the chosen proxy measure for ‘experience’, the geographic location or the considered time-span."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To give the fairest estimate and avoid relying on one unusual datapoint I am therefore reporting an average across several experience curve studies for PV that was conducted by de La Tour et al. 2013. The authors find an average learning rate over many studies of 20.2% (see Table 1 of their publication)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""de La Tour, A., Glachant, M. & Ménière, Y. (2013) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.sciencedirect.com/science/article/abs/pii/S0360544213007883"", ""children"": [{""text"": ""Predicting the costs of photovoltaic solar modules in 2020 using experience curve models"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". In "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Energy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" 62, 341–348."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The learning rate implied by the data that I’m presenting here is very similar (22.5%)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d23f027f43a3e75bc4dc01b1bc729fea64772191"": {""id"": ""d23f027f43a3e75bc4dc01b1bc729fea64772191"", ""index"": 27, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Dawn Santoianni (2015) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.worldcoal.org/setting-benchmark-worlds-most-efficient-coal-fired-power-plants"", ""children"": [{""text"": ""Setting the Benchmark: The World's Most Efficient Coal-Fired Power Plants"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in Worldcoal"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""da0113dabe21063c7fccb3a6f073d55cc07fc275"": {""id"": ""da0113dabe21063c7fccb3a6f073d55cc07fc275"", ""index"": 25, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The UK government expects offshore wind to become cheaper than onshore wind by the mid-2030s. Department for Business, Energy & Industrial Strategy (2020) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.gov.uk/government/publications/beis-electricity-generation-costs-2020"", ""children"": [{""text"": ""BEIS electricity generation cost report"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Published 24 August 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""See also the discussion of this report: Simon Evans (2020) –"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.carbonbrief.org/wind-and-solar-are-30-50-cheaper-than-thought-admits-uk-government"", ""children"": [{""text"": "" Wind and solar are 30-50% cheaper than thought,admits UK government"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". In Carbon Brief."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""dde630e9b251ecd8e2c7aeda711f39711092a459"": {""id"": ""dde630e9b251ecd8e2c7aeda711f39711092a459"", ""index"": 39, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Cary Funk and Meg Hefferon (2019) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.pewresearch.org/science/2019/11/25/u-s-public-views-on-climate-and-energy/"", ""children"": [{""text"": ""U.S. Public Views on Climate and Energy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Pew Research Center."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On other countries see "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.pewresearch.org/science/wp-content/uploads/sites/16/2020/09/PS_2020.09.29_international-science_TOPLINE.pdf"", ""children"": [{""text"": ""Pew Research (2020) – International Science Survey 2019-2020"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". September 29, 2020 Release"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e0816f16093a795176260fff16d6d4e5c1ae7dc2"": {""id"": ""e0816f16093a795176260fff16d6d4e5c1ae7dc2"", ""index"": 29, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""J. McNerney, J.D. Farmer, J.E. Trancik (2011) – Historical costs of coal-fired electricity and implications for the future Energy Policy, 39 (6) (2011), pp. 3042-3054 "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/j.enpol.2011.01.037"", ""children"": [{""text"": ""https://doi.org/10.1016/j.enpol.2011.01.037"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e09a76344e68bafcdd1309d96542a79e704d0fe7"": {""id"": ""e09a76344e68bafcdd1309d96542a79e704d0fe7"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This goal – the alternative energy source generating power at a levelized cost of energy (LCOE) that is equal (or lower) than the currently dominating source of energy – is referred to as ‘grid parity’."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e2733b1df7d7a5fd34ea29b57bf59032dc40cd10"": {""id"": ""e2733b1df7d7a5fd34ea29b57bf59032dc40cd10"", ""index"": 10, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Ben Zientara (2020) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.solarpowerrocks.com/solar-basics/how-much-electricity-does-a-solar-panel-produce/"", ""children"": [{""text"": ""How much electricity does a solar panel produce?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" Updated version from 4/2/2020"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e8c2eff74a39e6b652786a71e54519129c6b0fb2"": {""id"": ""e8c2eff74a39e6b652786a71e54519129c6b0fb2"", ""index"": 32, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Edward S.Rubin, Inês M.L.Azevedo, Paulina Jaramillo, Sonia Yeh (2015) – A review of learning rates for electricity supply technologies. In Energy Policy. Volume 86, November 2015, Pages 198-218. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/j.enpol.2015.06.011"", ""children"": [{""text"": ""https://doi.org/10.1016/j.enpol.2015.06.011"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f092bb2a10e917d09b514b84466af2a10f96cdff"": {""id"": ""f092bb2a10e917d09b514b84466af2a10f96cdff"", ""index"": 21, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The first reference to Watson saying this is in an article from Der Spiegel from 26th May 1965 – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.spiegel.de/spiegel/print/d-46272769.html"", ""children"": [{""text"": ""Sieg der Mikrosekunde"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f1229d9ec76b0d3d5f5ad9b03e77582ad7e67205"": {""id"": ""f1229d9ec76b0d3d5f5ad9b03e77582ad7e67205"", ""index"": 31, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""In the visualization I am not able to show gas electricity. This is because the price between gas peaker and combined cycles differs significantly, and I am not aware of any global data on the capacity of each of these sources. If you know of data that would allow the addition of gas to the visualization please get in touch with me. Thank you."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f36ebe6194ca80c6d726e55486aea698e5e517b8"": {""id"": ""f36ebe6194ca80c6d726e55486aea698e5e517b8"", ""index"": 20, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Lafond, Francois and Greenwald, Diana Seave and Farmer, J. Doyne, Can Stimulating Demand Drive Costs Down? World War II as a Natural Experiment (June 1, 2020). "", ""spanType"": ""span-simple-text""}, {""url"": ""http://dx.doi.org/10.2139/ssrn.3519913"", ""children"": [{""text"": ""http://dx.doi.org/10.2139/ssrn.3519913"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Why did renewables become so cheap so fast?"", ""authors"": [""Max Roser""], ""excerpt"": ""In most places power from new renewables is now cheaper than new fossil fuels."", ""dateline"": ""December 1, 2020"", ""subtitle"": ""In most places power from new renewables is now cheaper than new fossil fuels."", ""featured-image"": ""cheap-renewables-growth-featured-image.png""}",1,2023-09-01 15:14:31,2020-12-01 11:00:00,2024-03-18 15:41:59,listed,ALBJ4LvUhA13Pu8NvLSup9eaygnKBGGZboUPViENNGURvgUFj4SbHm7MW84SwLOJWi8KYm998PtOOCoRsxHuwQ,," The world’s energy supply today is neither safe nor sustainable. What can we do to change this and make progress against this twin-problem of the status quo? To see the way forward we have to understand the present. Today fossil fuels – coal, oil, and gas – account for [79%](https://ourworldindata.org/energy-mix#global-primary-energy-how-has-the-mix-changed-over-centuries) of the world’s energy production and as the chart below shows they have very large negative side effects. The bars to the left show the number of deaths and the bars on the right compare the greenhouse gas emissions. My colleague Hannah Ritchie explains the data in this chart in detail in her post [‘What are the safest sources of energy?](https://ourworldindata.org/safest-sources-of-energy)’. This makes two things very clear. As the burning of fossil fuels [accounts for 87%](https://ourworldindata.org/grapher/global-co2-emissions-fossil-land) of the world’s CO2 emissions, a world run on fossil fuels is not sustainable, they endanger the lives and livelihoods of future generations and the biosphere around us. And the very same energy sources lead to the deaths of many people _right now_ – the air pollution from burning fossil fuels kills 3.6 million people in [countries around the world](https://ourworldindata.org/grapher/pollution-deaths-from-fossil-fuels) every year; this is _6-times_ the annual death toll of all murders, war deaths, and terrorist attacks combined.1 It is important to keep in mind that electric energy is only one of several forms of energy that humanity relies on; the transition to low-carbon energy is therefore a bigger task than the transition to low-carbon electricity.2 What the chart makes clear is that the alternatives to fossil fuels – renewable energy sources and nuclear power – are orders of magnitude safer and cleaner than fossil fuels. Why then is the world relying on fossil fuels? Fossil fuels dominate the world’s energy supply because in the past they were _cheaper_ than all other sources of energy. If we want the world to be powered by safer and cleaner alternatives, we have to make sure that those alternatives are cheaper than fossil fuels. ## The price of electricity from the long-standing sources: fossil fuels and nuclear power The world’s electricity supply is [dominated](https://ourworldindata.org/grapher/electricity-prod-source-stacked?stackMode=relative&time=earliest..latest) by fossil fuels. Coal is by far the biggest source, supplying 37% of electricity; gas is second and supplies 24%. Burning these fossil fuels for electricity and heat is the largest single source of global greenhouse gases, causing 30% of global emissions.3 The chart here shows how the electricity prices from the long-standing sources of power – fossil fuels and nuclear – have changed over the last decade. The data is published by Lazard.4 To make comparisons on a consistent basis, energy prices are expressed in ‘levelized costs of energy’ (LCOE). You can think of LCOE from the perspective of someone who is considering building a power plant. If you are in that situation then the LCOE is the answer to the following question: What would be the minimum price that my customers would need to pay so that the power plant would break even over its lifetime? LCOE captures the cost of building the power plant itself as well as the ongoing costs for fuel and operating the power plant over its lifetime. It however does not take into account costs and benefits at an energy system level: such as price reductions due to low-carbon generation and higher systemic costs when storage or backup power is needed due to the variable output of renewable sources – we will return to the aspect of storage costs later.5 This makes clear that it is a very crucial metric. If you as the power plant builder pick an energy source that has an LCOE that is higher than the price of the alternatives you will struggle to find someone who is willing to buy your expensive electricity. What you see in the chart is that within the last 10 years the price of electricity from nuclear became more expensive, gas power became less expensive, and the price of coal power – the world’s largest source of electricity – stayed almost the same. Later we will see what is behind these price changes. ## The price decline of electricity from renewable sources If we want to transition to renewables, it is their price relative to fossil fuels that matters.6 This chart here is identical to the previous one, but now also includes the price of electricity from renewable sources. All of these prices – renewables as well as fossil fuels – are without subsidies. Look at the change in solar and wind energy in recent years. Just 10 years ago it wasn’t even close: it was much cheaper to build a new power plant that burns fossil fuels than to build a new solar photovoltaic (PV) or wind plant. Wind was 22%, and solar 223% more expensive than coal. But in the last few years this has changed entirely. Electricity from utility-scale solar photovoltaics cost $359 per MWh in 2009. Within just one decade the price declined by 89% and the relative price flipped: the electricity price that you need to charge to break even with the new average coal plant is now much higher than what you can offer your customers when you build a wind or solar plant. It’s hard to overstate what a rare achievement these rapid price changes represent. Imagine if some other good had fallen in price as rapidly as renewable electricity: Imagine you’d found a great place to live back in 2009 and at the time you thought it’d be worth paying $3590 in rent for it. If housing had then seen the price decline that we’ve seen for solar it would have meant that by 2019 you’d pay just $400 for the same place.7 I emphasized that it is the relative price that matters for the decision of which type of power plants are built. Did the price decline of renewables matter for the decisions of actual power plant builders in recent years? Yes it did. As you see in [our Energy Explorer](https://ourworldindata.org/explorers/energy), wind and solar energy were scaled up rapidly in recent years; in 2019 renewables accounted for 72% of all new capacity additions worldwide.8 ## Why is this happening? Learning curves and the price of solar photovoltaics modules How can this be? Why do we see the cost of renewable energy decline so very fast? The costs of fossil fuels and nuclear power depend largely on two factors, the price of the fuel that they burn and the power plant’s operating costs.9 Renewable energy plants are different: their operating costs are comparatively low and they don’t have to pay for any fuel; their fuel doesn’t have to be dug out of the ground, their fuel – the wind and sunlight – comes to them. What is determining the cost of renewable power is the cost of the power plant, the cost of _the technology itself_. To understand why solar _power_ got so cheap we have to understand why solar _technology_ got cheap. For this, let’s go back in time for a moment. The first price point for usable solar technology that I can find is from the year 1956. At that time the cost of just one watt of solar photovoltaic capacity was $1,865 (adjusted for inflation and in 2019 prices).10 One watt isn’t much. Today one single solar panel of the type homeowners put on their roofs produces around 320 watts of power.11 This means that at the price of 1956 one of today’s solar modules would cost $596,800.12 At this price – more than half a million dollars for a single panel – solar was obviously hopelessly uncompetitive with fossil fuels. Then why didn’t the history of solar technology end right there? There are two reasons why instead of dying, solar has developed to become the world’s cheapest source of electricity today. Even at the very high price, solar technology did find a use. It is a technology that literally came from outer space. The very first practical use of solar power was to supply electricity for a satellite, the Vanguard I satellite in 1958. It was in this high-tech niche where someone was willing to pay for solar technology even at that extremely high price. The second important reason is that the price of solar modules declined when more of them were produced. More production gave us the chance to learn how to improve the production process: a classic case of learning-by-doing. The initial demand in the high-tech sector meant that some solar technology was produced and this initial production started a virtuous cycle of increasing demand and falling prices. The visualization shows this mechanism. To satisfy increasing demand more solar modules get deployed, which leads to falling prices; at those lower prices the technology becomes cost-effective in new applications, which in turn means that demand increases. In this positive feedback loop solar technology has powered itself forward ever since its early days in outer space. ## A short history of solar: From outer space to the cheapest source of energy on earth During the 1960s the main application of solar remained in satellites. But the virtuous cycle was set in motion and this meant that slowly, but steadily, the price of solar modules declined. With falling prices the technology came down from space to our planet. The first terrestrial applications in the 1970s were in remote locations where the connection to the wider electrical grid is costly – lighthouses, remote railroad crossings, or the refrigeration of [vaccines](https://ourworldindata.org/vaccination).13 The data point for 1976 in the top left corner of the chart shows the state of solar technology at the time. Back then the price of a solar module, adjusted for inflation, was US-$106 per watt. And as you see on the bottom axis, global installed solar PV capacity was only 0.3 megawatts. Relative to 1956 this was already a price decline of 94%, but relative to the world’s energy demand solar was still very expensive and therefore very small: a capacity of 0.3 megawatts is enough to provide electricity for about 20 people per year.14 The time-series in the chart shows how the price of solar modules changed from then until now. The so-called ‘learning effect’ in solar technology is incredibly strong: while the installed capacity increased exponentially, the price of solar modules _declinedexponentially_. The fact that both metrics changed exponentially can be nicely seen in this chart because both axes are logarithmic. On a logarithmic axis a measure that declines exponentially [follows a straight line](https://blog.datawrapper.de/weeklychart-logscale/). This straight line that represents the relationship between _experience _– measured as the cumulative installed capacity of the technology – and the _price_ of that technology is called the _learning curve_ of that technology. The relative price decline associated with each doubling of experience is the _learning rate_ of a technology. This is the virtuous cycle in action. More deployment means falling prices, which means more deployment. With solar technology it was for a long time the case that its increased deployment was made possible through government subsidies and mandates – arguably the most positive effect of these policies is that they too drove down the price of these new technologies along the learning curve. Paying for renewables at a high price point earlier allows everyone to pay less for them later. That more production leads to falling prices is not surprising – such ‘economies of scale’ are found in many corners of manufacturing. If you are already making one pizza, it isn’t that much extra work to make a second one. What is truly mind blowing about solar technology is how very strong this effect is: For more than four decades each doubling of global cumulative capacity was associated with the same relative decline in prices. The advances that made this price reduction possible span the entire production process of solar modules:15 larger, more efficient factories are producing the modules; R&D efforts increase; technological advances increase the efficiency of the panels; engineering advances improve the production processes of the silicon ingots and wafers; the mining and processing of the raw materials increases in scale and becomes cheaper; operational experience accumulates; the modules are more durable and live longer; market competition ensures that profits are low; and capital costs for the production decline. It is a myriad of small improvements across a large collective process that drives this continuous price decline. The learning rate of solar PV modules is 20.2%.16 With each doubling of the installed cumulative capacity the price of solar modules declines by 20.2%.17 The high learning rate meant that the core technology of solar electricity declined rapidly. The price of solar modules declined from $106 to $0.38 per watt. A decline of 99.6%. To get our expectations for the future right we ought to pay a lot of attention to those technologies that follow learning curves. Initially we might only find them on a high-tech satellite out in space, but the future belongs to them. Renewable energy sources are not the only case; the most well-known case is the computer and the corresponding historical development there is ‘Moore’s Law’. If you are interested in getting your expectations about the future right, you are interested in how Moore’s Law helps us to see the future of technological development, and you want to know about whether it is indeed the case that scaled-up production _causes _declining prices you can read the following information box that takes a deeper look at it. ## How Moore’s Law and Wright’s Law can help us to get our expectations for the future right ### Predicting the future: The laws of Gordon Moore and Theodore Paul Wright Solar modules are not the only technology where we see exponential progress. The case of exponential technological change that everyone knows of is Moore’s law – the observation of Intel’s co-founder Gordon Moore who noticed that the number of transistors on microprocessors doubled every two years. He first made this observation back in 1965 and until today this extraordinarily fast rate of technological progress still applies. Integrated circuits are the fundamental technology of computers and Moore’s law is what has driven the exponential progress in computers in recent decades – computers became rapidly cheaper, more energy efficient, and faster. As you might have noticed Moore’s law is not stated in the same way that I’ve been looking at solar module prices. Moore’s law describes technological change as a function of _time; for solar,_ I am looking at price changes as a function of _experience_ – measured as the total amount of solar modules that were ever installed. This relationship, that each doubling in experience leads to the same relative decline in prices, was discovered much earlier than Moore’s law, by aerospace engineer Theodore Paul Wright in 1936.18 After him it is called _Wright’s Law_. Moore’s observation for the progress in computing technology can be seen as a special case of Wright’s Law.19 Solar panels and computer chips are not the only technologies that follow his law. Have a look at [our visualization](https://ourworldindata.org/grapher/costs-of-66-different-technologies-over-time) of the price declines of 66 different technologies and the research referenced in the footnote20 How do we know that increasing experience is causing lower prices? After all it could be the other way around – production only increases after costs have fallen. In most settings this is difficult to disentangle empirically, but researchers François Lafond, Diana Greenwald, and Doyne Farmer found an instance where this question can be answered. In their paper ‘Can Stimulating Demand Drive Costs Down?’, they study the price changes at a time when reverse causality can be ruled out, when demand was clearly not the consequence of lower prices: the demand for military technology in the Second World War.21 Their finding is that for technologies for which Wright’s Law applies, it is mostly the cumulative experience that determines the price. As demand for weapons grew production experience increased sharply and prices declined. When the war was over and demand shrank, the price decline reverted back to a slower rate. This is suggesting that it is really the cumulative experience that is driving the price decline that we are interested in. ### Wright’s Law helps us to get our expectations for the future right If you want to know what the future looks like one of the most useful questions to ask is which technologies follow Wright’s Law and which do not. Most technologies obviously do not follow Wright’s Law – the prices of bicycles, fridges, or coal power plants do not decline exponentially as we produce more of them. But those which do follow Wright’s Law – like computers, solar PV, and batteries – are the ones to look out for. They might initially only be found in very niche applications, but a few decades later they are everywhere. If you are unaware that technology follows Wright’s Law you can get your predictions very wrong. At the dawn of the computer age in 1943 IBM president Thomas Watson famously said ""I think there is a world market for maybe five computers.""22 At the price point of computers at the time that was perhaps perfectly true, but what he didn’t foresee was how rapidly the price of computers would fall. From its initial niche when there was perhaps truly only demand for five computers they expanded to more and more applications and the virtuous cycle meant that the price of computers declined further and further. The exponential progress of computers expanded their use from a tiny niche to the defining technology of our time. Solar modules are on the same trajectory, as we’ve seen before. At the price of solar modules in the 1950s it would have sounded quite reasonable to say, “I think there is a world market for maybe five solar modules.” But as a prediction for the future this statement too would have been ridiculously wrong. To get our expectations about the future right we are well advised to take the exponential change of Wright’s Law seriously. My colleagues Doyne Farmer, François Lafond, Penny Mealy, Rupert Way, Matt Ives, Linus Mattauch, Cameron Hepburn and others have done important pioneering work in this field. A central paper of their work is Farmer’s and Lafond’s ‘How predictable is technological progress?’ from 2016.23 The focus of this research paper is the price of solar modules so that we avoid repeating Watson’s mistake for solar technology. They lay out in detail what I discussed here: how solar modules decline in price, how demand is driving this change, and how we can learn about the future by relying on these insights. To get our expectations for the future right we ought to pay attention to those technologies that follow Wright’s law. Initially we might only find them on a high-tech satellite out in space, but the future belongs to them. ## Do electricity prices follow learning curves? Solar PV modules might very well follow a rapidly declining learning curve, but solar modules themselves are not what we want. We want the electricity that they produce. Does the price of solar _electricity_ follow a learning curve? The visualization shows the relevant data.24 On the vertical axis you see again the LCOE price for electricity and on the horizontal axis you now find the cumulative installed capacity.25 As in the solar module chart, both variables are plotted on logarithmic scales so that the line on the charts represents the learning rate for these technologies. In bright orange you see the development for the price of power from solar PV over the last decade. The learning curve relationship that we saw for the price of solar _modules_ also holds for the price of _electricity_. The learning rate is actually even faster: At each doubling of installed solar capacity the price of solar electricity declined by 36% – compared to 20% for solar modules. Wind power – shown in blue – also follows a learning curve. The onshore wind industry achieved a learning rate of 23%. Every doubling of capacity was associated with a price decline of almost a quarter. Offshore wind had a learning rate of 10% and is still relatively expensive – only 25% cheaper than nuclear and a bit more expensive than coal. But for two reasons experts expect the power from offshore wind to become very cheap in the coming years, larger wind turbine sizes and the fact that the consistent winds out on the sea allows higher load factors.26 The obvious similarity of onshore and offshore wind also means that learning effects in one industry can be transferred to the other. ## Fossil fuels and nuclear do not follow learning curves Electricity generation from renewables is getting rapidly cheaper. What about its competitors? Let’s look first at coal. Why is electricity from coal not getting cheaper? Coal, the world’s largest source of electricity, is also included in the chart. The global price of electricity from new coal (LCOE) declined from $111 to $109. While solar got 89% cheaper and wind 70%, the price of electricity from coal declined by merely 2%. The stagnating price of coal power in the last decade is not unusual. The historical development of the price of coal power is nowhere close to what we’ve been seeing for renewable power. Neither the price of the coal nor the price of the coal plants followed a learning curve, the prices didn’t even decline over the long run.27 Electricity from coal was historically cheap and still is, but it is not getting cheaper. There are two reasons we shouldn’t expect this to change much in the future: First, there is little room for improving the efficiency of coal power plants substantially. Typical plants have efficiencies of around 33%, while the most efficient ones today reach 47%.28 Even a dramatic, unprecedented improvement from an efficiency of one-third to two-thirds would only correspond to the progress that solar PV modules make every 7.5 years.29 Second, the price of electricity from all fossil fuel is not only determined by the technology but to a significant extent by the cost of the fuel itself. The cost of coal that the power plant burns makes up about 40% of total costs.30This means that for all non-renewable power plants which have these fuel costs there is a hard lower bound to how much the cost of their electricity can possibly decrease. Even if the price for constructing the power plant would decline, the price of the fuel means that there is a floor below which the price of electricity cannot pass. For these reasons it should not be surprising that coal power does not follow a learning curve. ### Electricity from gas: should we expect that the price continues to fall? Electricity from gas, the second largest fossil fuel source, did become cheaper over the last decade.31 As we saw above, electricity from combined cycle gas plants declined by 32% to a global average cost of $56 per MWh.32 The costs of building a gas plant declined during some periods in the last 70 years, as Rubin et al (2015) show.33 But the main reason the price of _gas electricity _declined over the last decade is that the price of gas itself happened to decline over this particular period. After a peak in 2008 the price of gas declined steeply. The increased supply from fracking is one key reason. This price decline of gas, however, is not part of a long-run development. The price of gas today is [higher](https://ourworldindata.org/grapher/fossil-fuel-price-index) than two or three decades ago. For the same reasons as discussed for coal – limited learning and fuel costs as a floor – we should therefore not expect the price of electricity from gas to decline significantly over the coming decades and we should certainly not expect a learning curve effect similar to what we are seeing for renewables. ### Why did nuclear power get more expensive? What can reverse that trend? For nuclear power you see the data since 2009 in the chart. Nuclear power has increased in price. This increase is part of a longer term trend. In many places building a power plant has become more expensive as the studies reviewed in Rubin et al (2015) document.33 This is of course very unfortunate, since nuclear is both a low-carbon source of electricity and one of the safest sources of electricity as we have seen in the very first chart. One reason for rising prices is increased regulation for nuclear power, which has the important benefit of increased safety. A second reason is that the world has not built many nuclear power plants in recent years so that supply chains are small, uncompetitive, and are not benefiting from economies of scale.34 Both of these reasons explain why the _global average_ LCOE price has gone up. But for nuclear there are large differences in price trends between countries: Prices and construction times have increased significantly in the US and the UK, while France and South Korea were at least able to keep prices and construction times constant.35 Michel Berthélemy and Lina Escobar Rangel (2015) explain that those countries that were able to avoid price surges are countries that do not stand out in regulating nuclear power less, but in _standardizing_ the construction of reactors more.35 Learning, after all, means transferring the knowledge gathered in one instance to another. No repetition, no learning. This is in sharp contrast with renewables in particular. While nuclear technology is not very standardized and gets build very rarely, solar PV modules and wind plants are the exact opposite, very standardized and extremely often built.36 One hope is that a new boom in nuclear power and increased standardization of the reactors would lead to declining costs of nuclear power. But there is no strong price decline anywhere, and certainly nothing that could be characterized by a steep learning curve. But nuclear could still become more important in the future because it can complement renewables where these energy sources have their weaknesses: First, intermittency of electricity from renewables remains a challenge and a viable energy mix of the future post-carbon world will likely include all low-carbon sources, renewables as well as nuclear power. And second, the land use of renewables is large and a big environmental benefit of nuclear power is that it uses very little land.37 And beyond the existing nuclear fission reactors there are several teams working towards nuclear fusion reactors, which would potentially entirely change the world’s energy supply.38 To make nuclear reactors competitive with fossil fuels is again an argument for carbon taxes. Nuclear reactors kill 350-times less people per unit of energy than fossil fuel plants, and as a low-carbon technology they can be key in making the transition away from fossil fuels. ### Batteries and electricity storage follow learning curves too One of the downsides of renewable sources is their intermittent supply cycle. The sun doesn’t always shine and the wind doesn't always blow. Technologies like batteries that store electric power are key to balance the changing supply from renewables with the inflexible demand for electricity. Fortunately electricity storage technologies are also among the few technologies that are following learning curves – their learning curve are indeed _very _steep, as the chart here shows. This chart is from my colleague Hannah Ritchie; she documents [in her article](https://ourworldindata.org/battery-price-decline) that the price of batteries declined by 97% in the last three decades.39 At their current price there might only be demand for five large power storage systems in the world, but as a prediction for the future this might sound foolish one day (if you don’t know what I’m alluding to, you skipped reading the text in the fold-out box above). ## Scaling up low-carbon sources leads to lower prices; let’s not waste this opportunity for our planet and economy The takeaway of the previous discussion is that renewables follow steep learning curves and fossil fuels do not. A key reason is that renewables do not have fuel costs and comparatively small operating and maintenance costs, which means that the LCOE of renewable energy scales with the cost of their technologies. And the key technologies of renewable energy systems – solar, wind, and batteries – themselves follow a learning curve: each doubling of their installed capacity leads to the same decline of costs. If we are serious about making the transition to a low-carbon global energy system we have a fantastic opportunity in front of us. Scaling up renewable energy systems doesn’t only have the direct benefit of more low-carbon energy, but has an indirect side effect that is even more important: _cheaper_ energy. The learning rates for wind and solar PV are exceptionally fast. It is extremely rare to find technologies of this kind. Solar and wind have one more big advantage. While there is often little agreement in how to reduce greenhouse gas emissions, expanding solar and wind power are two options that are hugely popular with large majorities. Even in the often polarized US, renewables have the support of strong majorities of Democrats and Republicans. 85% of Americans are in favour of expanding wind power and 92% are in favor of expanding solar power, and in other countries the support is often even higher.40 Today, at a time when the global economy – and workers around the world – suffer greatly from the COVID-19 recession and when interest rates are low (or even negative), scaling up renewable energy systems offers us a great chance to move forward. It is rare to have a policy option that leads to more jobs, cheaper prices for consumers, and a greener, safer planet.41 The more renewable energy technologies we deploy, the more their costs will fall. More growth will mean even more growth. ### Making renewable energy irresistible: Technological progress somewhere turns into progress everywhere One last argument on why lower prices due to technological change are so crucial for making the transition to the post-carbon world. If rich countries make investments into renewable technology that drive down the price along the learning curves, they are not just working towards the transition from fossil fuels to renewable energy for themselves, but for the entire world. The relative price of fossil fuels and renewables is key to anyone’s decision of which power plant to build. Making low-carbon technology cheap is a policy goal that doesn’t only reduce emissions in your own country but in the entire world, forever. Driving down the price of low-carbon energy should be seen as one of the [most important goals](https://techcrunch.com/2019/02/15/how-to-decarbonize-america-and-the-world/) (and achievements) of clean energy policy, because it matters beyond the borders of the country that is adopting that policy. This is the beautiful thing about technology: once it is invented somewhere it can help everywhere. The biggest growth in electricity demand in the coming years will not come from rich countries, but the poorer, yet rapidly developing countries in Africa and Asia.42 The steep decline of solar power is a particularly fortunate development for many of these countries that often have [sunny climates](https://en.wikipedia.org/wiki/Solar_power#/media/File:SolarGIS-Solar-map-World-map-en.png). Energy systems have very long path dependencies, since it is very costly to build a power plant or to decide to shut a power plant down. Investments in renewable technologies now will therefore have very long-term benefits. Every instance when a country or an electricity company decides to build a low-carbon power plant instead of a coal plant is a win for decades. Low prices are the key argument to convince the world – especially those places that have the least money – to build low-carbon power systems for a sustainable future. ## Conclusion One of the very worst misconceptions about the challenge of climate change is that it is an easy problem to solve. It is not. Climate policy is exceedingly difficult43 and the technological challenges are much larger than the electricity sector alone since it is only one of several big sectors that need to be decarbonized. We need change and technological innovation across all these sectors at a scale that matches the problem and the problem is big. But what the consideration of changing electricity prices has shown is that we have a clear option in front of us where we are able to make very important progress. Low-carbon technologies that were so expensive just a few decades ago that they were only affordable for satellites have came down steadily in price and now provide the cheapest electricity on the planet (which implies that they are now the cheapest source of energy that humanity ever had access to). Driving down the costs of renewables is key to a green, low-carbon future, but it also has a big benefit for people today: Your real income is the ratio between what you are paid and the price of the goods and services you pay for – that is why falling energy prices means that people’s real income is growing. Falling energy prices means economic growth and less poverty. The reason we can hope for a future in which renewables are deployed rapidly and where fossil fuel plants become increasingly unprofitable is that renewables follow steep learning curves, and fossil fuels do not. We are heading towards a future in which the disadvantage of fossil fuels will keep increasing. But limiting climate change is a race against time and we have a long way to go. There is [a good chance](https://thebreakthrough.org/issues/energy/peak-co2-emissions-2019) that the world has reached the peak of greenhouse gas emissions last year. A huge milestone, but the peak is not the goal; we need to get all the way down to net-zero. The argument for scaling these technologies up sooner rather than later is that we are getting to the low-carbon, low-cost future _faster_. This ensures that the power plants that will be built in the coming years are not fossil fuel plants but renewables. This is key to bringing down greenhouse gas emissions fast. And it has the side effects that it saves people from air pollution and it reduces energy prices – which means growing incomes and declining poverty. J. Perlin (1999) – From space to earth: the story of solar electricity. aatech publications, Ann Arbor, MI (1999) via Doyne Farmer and Fracois Lafond (2016) – How predictable is technological progress? Research Policy. Volume 45, Issue 3, April 2016, Pages 647-665. [https://doi.org/10.1016/j.respol.2015.11.001](https://doi.org/10.1016/j.respol.2015.11.001) $256 in 1956 adjusted for prices – using the US GDP deflator – equals $1865 in 2019 US-$ according to ([https://www.multpl.com/gdp-deflator](https://www.multpl.com/gdp-deflator) “Enhanced levelised cost” is an approach that aims to adjust for this, but its measurement is still in its early stages. [Simon Evans discusses ‘enhanced levelised costs’ for different electricity sources in the UK](https://www.carbonbrief.org/wind-and-solar-are-30-50-cheaper-than-thought-admits-uk-government). Carbon pricing is a policy that would make those who actually cause emissions pay for them (the richest people in the world that enjoy the best living conditions in human history), but most governments fail to implement carbon prices, and where they exist they are often too low (which has the consequence that the poorest people on the planet are ‘paying’ most for carbon emissions, since it is them who are suffering the severest consequences). How much electricity can be generated from 0.3 megawatts of electricity? As a back-of-the-envelope calculation, I used the oldest data for Germany that I could find, which relates to the 1990s, and took an average to average over better and worse years. In the 1990s Germany had 48.5 MW of solar capacity and generated 23,750 MWh of electricity. This means that in these circumstances and with this technology (surely much better than the technology in 1976) they generated 145,040 kWh per solar PV capacity of 0.3 MW. The electricity demand of a person in Germany is 7,333kWh per year so that 0.3MW could provide electricity for 20 people (145,040kWh/7,333kWh=19.78). See Michel Berthélemy and Lina Escobar Rangel (2015) – Nuclear reactors' construction costs: The role of lead-time, standardization and technological progress. In Energy Policy Volume 82, July 2015, Pages 118-130. [https://doi.org/10.1016/j.enpol.2015.03.015](https://doi.org/10.1016/j.enpol.2015.03.015) For fossil fuels and nuclear we show installed capacity at each point in time (because we are not aware of any data on the cumulatively built capacity for these energy sources). I am however not expecting a large difference between installed and cumulatively built capacity – especially over a 10-year time span and for power sources that have been scaled up largely before 2009. IRENA 2020 for all data on renewable sources; Lazard for the price of electricity from nuclear and coal – [IAEA for nuclear capacity](https://pris.iaea.org/PRIS/WorldStatistics/WorldTrendNuclearPowerCapacity.aspx) and the [Global Energy Monitor for coal capacity](https://globalenergymonitor.org/coal/global-coal-plant-tracker/). In 2016 (the latest sectoral breakdown available) global [greenhouse gas emissions were](https://ourworldindata.org/grapher/total-ghg-emissions?tab=chart&time=earliest..latest&country=~OWID_WRL) 49.36 billion tonnes CO2eq. Electricity and heat generation was responsible for 15.01 billion tonnes CO2eq. Electricity and heat generation therefore accounted for [49.36 / 15.01 * 100 = 30%] of global emissions. This data is sourced from [Climate Watch](https://www.climatewatchdata.org/ghg-emissions) and the World Resources Institute. The price of coal plants over time was studied in McNerney et al (2011) and the authors find that after a decline of construction costs from 1902 until around 1970, the price then _increased _for two decades from 1970 until 1990_. _They attribute this cost increase to increased restrictions on the tolerable pollution (air pollution has [fallen rapidly](https://ourworldindata.org/outdoor-air-pollution#the-long-term-decline-of-air-pollution-in-rich-countries) in industrialized countries since 1970). From around 1990 onwards the price of coal plants remained largely unchanged. J. McNerney, J.D. Farmer, J.E. Trancik (2011) – Historical costs of coal-fired electricity and implications for the future Energy Policy, 39 (6) (2011), pp. 3042-3054 [https://doi.org/10.1016/j.enpol.2011.01.037](https://doi.org/10.1016/j.enpol.2011.01.037) The price of coal itself has fluctuated over the last 150 years, but without a clear long run trend as the same authors show. Falling transportation costs have made coal cheaper for power plants, but more recently [the price of coal increased](https://ourworldindata.org/grapher/coal-prices?time=2001..latest) and overall the price of coal has not declined over the long run. Theodore Paul Wright (1936) – Factors affecting the cost of airplanes. J. Aeronaut. Sci., 3 (4) (1936), pp. 122-128 Nagy B, Farmer JD, Bui QM, Trancik JE (2013) Statistical Basis for Predicting Technological Progress. PLoS ONE 8(2): e52669. https://doi.org/10.1371/journal.pone.0052669 Many more references can be found in Doyne Farmer and Fracois Lafond (2016) – How predictable is technological progress? Research Policy. Volume 45, Issue 3, April 2016, Pages 647-665. [https://doi.org/10.1016/j.respol.2015.11.001](https://doi.org/10.1016/j.respol.2015.11.001) The price of Ford’s Model T followed Wright’s law: each doubling of cumulative production led to the same relative decline in prices. What’s fascinating is that this decline hasn’t stopped until today. An 8hp car, as the Model T, costs what you’d expect: See Sam Korus (2019) – [Wright’s Law Predicted 109 Years of Auto Production Costs, and Now Tesla’s](https://ark-invest.com/analyst-research/wrights-law-predicts-teslas-gross-margin/) See also Schmidt, O., Hawkes, A., Gambhir, A. et al. The future cost of electrical energy storage based on experience rates. Nat Energy 2, 17110 (2017). [https://doi.org/10.1038/nenergy.2017.110](https://doi.org/10.1038/nenergy.2017.110) An updated dataset from 2018 by the authors is [available on FigShare here](https://figshare.com/articles/Update_2018_-_The_future_cost_of_electrical_energy_storage_based_on_experience_rates/7012202) Annual updates can be found via Bloomberg NEF, for example [here](https://about.bnef.com/blog/battery-pack-prices-fall-as-market-ramps-up-with-market-average-at-156-kwh-in-2019/). Plausibly it isn’t just the passing of time that drives the progress in computer chips, but there too it is the learning that comes with continuously expanding the production of these chips. Lafond et al (2018) explain that the two laws produce the same forecasts when cumulative production grows exponentially, which is the case when production grows exponentially. More precisely, if production grows exponentially with some noise/fluctuations, then cumulative production grows exponentially with very little noise/fluctuations. As a result, the log of cumulative production is a linear trend and therefore predicting cost by the linear trend of time or the linear trend of log cumulative production give the same results. Fracois Lafond, Aimee G. Bailey, Jan D. Bakker, Dylan Rebois, Rubina Zadourian, Patrick McSharry, and [J. Doyne Farmer](http://www.doynefarmer.com/) (2018) – [How well do experience curves predict technological progress? A method for making distributional forecasts](https://francoislafond.files.wordpress.com/2015/11/wrightslawpaper20.pdf) In Technological Forecasting and Social Change  128, pp 104-117, 2018. [arXiv](https://www.arxiv.org/abs/1703.05979), [Publisher](http://www.sciencedirect.com/science/article/pii/S0040162517303736), [Data](https://www.dropbox.com/sh/w7jvzijblb4nkex/AAC2R-ml3JvIjFfBZtUTPlkta?dl=0), [Code](https://francoislafond.files.wordpress.com/2019/12/forecast_tech_progress-1.zip). See also Nagy B, Farmer JD, Bui QM, Trancik JE (2013) Statistical Basis for Predicting Technological Progress. PLoS ONE 8(2): e52669. [https://doi.org/10.1371/journal.pone.0052669](https://doi.org/10.1371/journal.pone.0052669) Wright’s law for solar PV modules has also been given its own name; some call it [Swanson’s Law (Wiki)](https://en.wikipedia.org/wiki/Swanson%27s_law). It is very hard to find anything else that declines in price just as fast as electricity from renewable sources.The report by IRENA finds that for the 531 individual items that are used to compile the UK’s Consumer Price Index (CPI), only five items have declined more rapidly: strawberries, fruit smoothies, internet computer games, household cleaner and underground/metro fares outside London. But of course most people spend more money on electricity than on strawberries.IRENA (2020) – [Renewable Power Generation Costs in 2019](https://www.irena.org/publications/2020/Jun/Renewable-Power-Costs-in-2019), International Renewable Energy Agency The data source is [Lazard's Levelized Cost of Energy 2019](https://www.lazard.com/perspective/lcoe2019) – the big advantage of this source is that it includes the cost of electricity from a wide range of sources. IRENA (2020) – [Renewable Power Generation Costs in 2019](https://www.irena.org/publications/2020/Jun/Renewable-Power-Costs-in-2019), International Renewable Energy Agency There are arguments for and against gas as a source of electricity. In comparison with coal, the world’s dominating source of electricity, gas is both safer and cleaner, as we see in the first chart: the death rate from air pollution and accidents is 9-times lower and the greenhouse gas emissions are 40% lower per unit of produced energy. A third important consideration is that while power from gas peakers is expensive they can react quickly and provide electricity at peak times or when the output from other sources, especially renewables, drops. On the other hand it is of course the case that gas is much more deadly and emits much more carbon than nuclear and renewables. Good carbon pricing could strike a balance where the low-carbon alternatives can continue to grow and gas can take over from coal. At a higher carbon price, gas combined with CCS – carbon capture and storage – can become cost-effective sooner. The UK has implemented a carbon price and the government there expects that from 2025 onwards the levelised cost for gas-with-CCS to be cheaper than unabated gas. See: Department for Business, Energy & Industrial Strategy (2020) – [BEIS electricity generation cost report](https://www.gov.uk/government/publications/beis-electricity-generation-costs-2020). Published 24 August 2020. See also the discussion of this report: Simon Evans (2020) –[ Wind and solar are 30-50% cheaper than thought,admits UK government](https://www.carbonbrief.org/wind-and-solar-are-30-50-cheaper-than-thought-admits-uk-government). In Carbon Brief. Since it is sometimes wrongly claimed: It is not the case that a constant learning rate implies that the cost of a technology eventually would need to decline to 0. This misunderstanding does not consider the driving force appropriately. It is the doubling_ _of the _cumulative _number of units produced that drives the cost decline. Achieving a doubling of that becomes harder and harder as total production increases. Once the cumulative production is already very high, each doubling of cumulative capacity will take longer and longer. Eventually demand will level off such that the price decline slows down and would stop when the cumulative production of the technology satisfies demand. The other two big energy sectors are heat and transport; in the coming years it is very likely that the share of electric energy will increase, because a larger share of transport will be electrified. The [IEA reports](https://www.iea.org/reports/world-energy-outlook-2019/electricity) that electricity’s share in total final energy consumption was 19% in 2018 and expects it to increase to  24% in 2040. See the [IEA World Energy Outlook 2020 section on electricity](https://www.iea.org/reports/world-energy-outlook-2020/outlook-for-electricity#abstract). [The History of Solar](https://www1.eere.energy.gov/solar/pdfs/solar_timeline.pdf). US Department of Energy. Two papers to read on this point:Rupert Way, François Lafond, Fabrizio Lillo, Valentyn Panchenko, J. Doyne Farmer (2019) – Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves. In Journal of Economic Dynamics and Control. Volume 101, April 2019, Pages 211-238.[ https://doi.org/10.1016/j.jedc.2018.10.006](https://doi.org/10.1016/j.jedc.2018.10.006) Farmer, J.D., Hepburn, C., Ives, M.C., Hale, T., Wetzer, T., Mealy, P., Rafaty, R., Srivastav, S. & Way, R. (2019). 'Sensitive intervention points in the post-carbon transition'. Science, 364(6436), pp. 132-134. A rough back of the envelope calculation [by Michael Barnard](https://cleantechnica.com/2020/11/13/what-does-bill-gates-favorite-energy-guru-vaclav-smil-get-wrong/) makes this clear ""There is about 650 gigawatts (GW) of capacity of wind energy right now, as one example. The average wind turbine is about 2 megawatts (MW) in capacity globally, as new ones are almost always bigger and often much bigger. That means that there are about 325,000 wind turbines that have been built, and it means that there are almost a million wind turbine blades. Similarly, there’s about about 584 GW of solar globally. The average solar panel is about 200 Watts in capacity, so that’s about 3 billion solar panels installed already."" Doyne Farmer and Fracois Lafond (2016) – How predictable is technological progress? Research Policy. Volume 45, Issue 3, April 2016, Pages 647-665. [https://doi.org/10.1016/j.respol.2015.11.001](https://doi.org/10.1016/j.respol.2015.11.001) See also: de La Tour, A., Glachant, M. & Ménière, Y. (2013) – [Predicting the costs of photovoltaic solar modules in 2020 using experience curve models](https://www.sciencedirect.com/science/article/abs/pii/S0360544213007883). In _Energy_ 62, 341–348. Recent relevant coverage includes [Compact Nuclear Fusion Reactor Is ‘Very Likely to Work,’ Studies Suggest](https://www.nytimes.com/2020/09/29/climate/nuclear-fusion-reactor.html?referringSource=articleShare) (in the New York Times) and somewhat dated, but still relevant and fascinating [A Star in a Bottle](https://www.newyorker.com/magazine/2014/03/03/a-star-in-a-bottle) in the New Yorker. Doyne Farmer and Francois Lafond (2016) – How predictable is technological progress? Research Policy. Volume 45, Issue 3, April 2016, Pages 647-665. [doi.org/10.1016/j.respol.2015.11.001](https://doi.org/10.1016/j.respol.2015.11.001) In a study published in the Proceedings of the National Academy of Sciences, Jos Lelieveld et al. (2019) estimated that 5.6 million people died from anthropogenically caused air pollution. Of these 5.6 million, 3.6 million were attributed to fossil fuels. Lelieveld, J., Klingmüller, K., Pozzer, A., Burnett, R. T., Haines, A., & Ramanathan, V. (2019). [Effects of fossil fuel and total anthropogenic emission removal on public health and climate](https://www.pnas.org/content/116/15/7192/). Proceedings of the National Academy of Sciences, 116(15), 7192-7197 The death toll of the three counts of violence for 2017 [according to the IHME](https://ourworldindata.org/grapher/annual-number-of-deaths-by-cause) is 561,511. • Homicides: 405,346 deaths • War battles: 129,720 deaths • Terrorism: 26,445 deaths. In the following section we will look into their cost structures in detail. This is the price per watt multiplied by the output of today’s typical solar panel: 320W * 1865$/W= $596,800. David J. C. MacKay (2008) – Sustainable Energy – without the hot air. Online at WithoutHotAir.com Michael Fitzpatrick (2017) – Nuclear power is set to get a lot safer (and cheaper) – here’s why [https://theconversation.com/nuclear-power-is-set-to-get-a-lot-safer-and-cheaper-heres-why-62207](https://theconversation.com/nuclear-power-is-set-to-get-a-lot-safer-and-cheaper-heres-why-62207%7B/ref) Kavlak, Goksin and McNerney, James and Trancik, Jessika E. (2017) – Evaluating the Causes of Cost Reduction in Photovoltaic Modules (August 9, 2017). In Energy Policy, 123:700-710, 2018, [http://dx.doi.org/10.2139/ssrn.2891516](https://dx.doi.org/10.2139/ssrn.2891516) As one would expect, the exact learning rate differs slightly across studies, mostly due to differences in the chosen data source, the chosen proxy measure for ‘experience’, the geographic location or the considered time-span. To give the fairest estimate and avoid relying on one unusual datapoint I am therefore reporting an average across several experience curve studies for PV that was conducted by de La Tour et al. 2013. The authors find an average learning rate over many studies of 20.2% (see Table 1 of their publication). de La Tour, A., Glachant, M. & Ménière, Y. (2013) – [Predicting the costs of photovoltaic solar modules in 2020 using experience curve models](https://www.sciencedirect.com/science/article/abs/pii/S0360544213007883). In _Energy_ 62, 341–348. The learning rate implied by the data that I’m presenting here is very similar (22.5%). Dawn Santoianni (2015) – [Setting the Benchmark: The World's Most Efficient Coal-Fired Power Plants](https://www.worldcoal.org/setting-benchmark-worlds-most-efficient-coal-fired-power-plants) in Worldcoal The UK government expects offshore wind to become cheaper than onshore wind by the mid-2030s. Department for Business, Energy & Industrial Strategy (2020) – [BEIS electricity generation cost report](https://www.gov.uk/government/publications/beis-electricity-generation-costs-2020). Published 24 August 2020. See also the discussion of this report: Simon Evans (2020) –[ Wind and solar are 30-50% cheaper than thought,admits UK government](https://www.carbonbrief.org/wind-and-solar-are-30-50-cheaper-than-thought-admits-uk-government). In Carbon Brief. Cary Funk and Meg Hefferon (2019) – [U.S. Public Views on Climate and Energy](https://www.pewresearch.org/science/2019/11/25/u-s-public-views-on-climate-and-energy/). Pew Research Center. On other countries see [Pew Research (2020) – International Science Survey 2019-2020](https://www.pewresearch.org/science/wp-content/uploads/sites/16/2020/09/PS_2020.09.29_international-science_TOPLINE.pdf). September 29, 2020 Release J. McNerney, J.D. Farmer, J.E. Trancik (2011) – Historical costs of coal-fired electricity and implications for the future Energy Policy, 39 (6) (2011), pp. 3042-3054 [https://doi.org/10.1016/j.enpol.2011.01.037](https://doi.org/10.1016/j.enpol.2011.01.037) This goal – the alternative energy source generating power at a levelized cost of energy (LCOE) that is equal (or lower) than the currently dominating source of energy – is referred to as ‘grid parity’. Ben Zientara (2020) – [How much electricity does a solar panel produce?](https://www.solarpowerrocks.com/solar-basics/how-much-electricity-does-a-solar-panel-produce/) Updated version from 4/2/2020 Edward S.Rubin, Inês M.L.Azevedo, Paulina Jaramillo, Sonia Yeh (2015) – A review of learning rates for electricity supply technologies. In Energy Policy. Volume 86, November 2015, Pages 198-218. [https://doi.org/10.1016/j.enpol.2015.06.011](https://doi.org/10.1016/j.enpol.2015.06.011) The first reference to Watson saying this is in an article from Der Spiegel from 26th May 1965 – [Sieg der Mikrosekunde](https://www.spiegel.de/spiegel/print/d-46272769.html) In the visualization I am not able to show gas electricity. This is because the price between gas peaker and combined cycles differs significantly, and I am not aware of any global data on the capacity of each of these sources. If you know of data that would allow the addition of gas to the visualization please get in touch with me. Thank you. Lafond, Francois and Greenwald, Diana Seave and Farmer, J. Doyne, Can Stimulating Demand Drive Costs Down? World War II as a Natural Experiment (June 1, 2020). [http://dx.doi.org/10.2139/ssrn.3519913](http://dx.doi.org/10.2139/ssrn.3519913)",Why did renewables become so cheap so fast? 1yFkBbkw2__5nQj5e4bgJo5WHT4T5Et8mPG9ZNrBx33k,sulfur-dioxide-emissions-from-shipping-dropped-sharply-with-the-introduction-of-new-rules-in-2020,data-insight,"{""body"": [{""size"": ""narrow"", ""type"": ""image"", ""filename"": ""so2-shipping-desktop.png"", ""parseErrors"": [], ""smallFilename"": ""so2-shipping-mobile.png""}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""url"": ""https://github.com/JGCRI/CEDS/wiki/Release-Notes"", ""children"": [{""text"": ""Community Emissions Data System (CEDS)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" produces invaluable long-term data on the emissions of air pollutants worldwide. It has just published its latest update, extending this data to 2022."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One of the most striking changes in air pollution trends has been the abrupt drop in sulfur dioxide (SO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "") emissions from shipping. As you can see in the chart — where shipping is highlighted in red — there was a dramatic fall from over 10 million tonnes a year in 2019 to 3 million tonnes a year later."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The change resulted from the International Maritime Organization’s strict limits on marine fuels, introduced in 2020: the maximum percentage of sulfur allowed in these fuels fell from 3.5% to 0.5%. All ships worldwide had to comply."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This drop is positive for tackling local air pollution and acid rain. However, it also has implications for climate change since SO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.carbonbrief.org/analysis-how-low-sulphur-shipping-rules-are-affecting-global-warming/"", ""children"": [{""text"": ""has masked some of the warming"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" caused by greenhouse gases."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/explorers/air-pollution"", ""children"": [{""text"": ""Explore all of the updated data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" →"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""data-insight"", ""title"": ""Sulfur dioxide emissions from shipping dropped sharply with the introduction of new rules in 2020"", ""authors"": [""Hannah Ritchie""], ""approved-by"": ""Ed"", ""grapher-url"": ""https://ourworldindata.org/explorers/air-pollution?facet=metric&uniformYAxis=0&country=~OWID_WRL&Pollutant=Sulphur+dioxide+%28SO%E2%82%82%29&Sector=Breakdown+by+sector&Per+capita=false""}",1,2024-04-29 10:53:32,2024-05-10 07:38:27,2024-05-07 14:45:25,unlisted,ALBJ4LvE4HNQZcQU4N-YFO2lMHixviJxxGSQOcsTZ4dPcQDI_v3L8wrq4EFAbjg-spvUaRaUOiAXbw4Uc5nDDA,," The [Community Emissions Data System (CEDS)](https://github.com/JGCRI/CEDS/wiki/Release-Notes) produces invaluable long-term data on the emissions of air pollutants worldwide. It has just published its latest update, extending this data to 2022. One of the most striking changes in air pollution trends has been the abrupt drop in sulfur dioxide (SO2) emissions from shipping. As you can see in the chart — where shipping is highlighted in red — there was a dramatic fall from over 10 million tonnes a year in 2019 to 3 million tonnes a year later. The change resulted from the International Maritime Organization’s strict limits on marine fuels, introduced in 2020: the maximum percentage of sulfur allowed in these fuels fell from 3.5% to 0.5%. All ships worldwide had to comply. This drop is positive for tackling local air pollution and acid rain. However, it also has implications for climate change since SO2 [has masked some of the warming](https://www.carbonbrief.org/analysis-how-low-sulphur-shipping-rules-are-affecting-global-warming/) caused by greenhouse gases. [Explore all of the updated data](https://ourworldindata.org/explorers/air-pollution) →",Sulfur dioxide emissions from shipping dropped sharply with the introduction of new rules in 2020 1y6_r7uCs3gCpshniXdE-nUkWgRxpZL0Tas2zm4ewebI,un-population-2024-revision,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""We can’t understand the world without understanding demographic change."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How many people are alive today? How many are born; how many die? What do we expect populations to look like in the future?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The United Nations updates its big dataset — the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://population.un.org/wpp/"", ""children"": [{""text"": ""World Population Prospects"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" — every two years to answer these questions. It just released its latest edition today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We’ve updated all of our population-related datasets and charts with this new release. You can explore all the trends for every country in our "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography"", ""children"": [{""text"": ""Population and Demography Data Explorer"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this article, we wanted to provide key insights from this latest wave of data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""The world population is projected to peak slightly earlier than in previous projections"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The United Nations doesn’t only publish historical estimates of how population and demographic trends have changed in the past; it also makes projections for what the future might look like. To be clear, these are projections, "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""not predictions"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of changes in the future."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In its 2022 publication, the UN estimated that, in its "", ""spanType"": ""span-simple-text""}, {""id"": ""un-projection-scenarios"", ""children"": [{""text"": ""medium scenario"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", the global population would peak in 2086 at around 10.4 billion people."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This year’s edition brings this peak forward slightly to 2084, with the population topping at just under 10.3 billion."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart below compares the two revisions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This isn’t the first time the projected peak has been pulled earlier. According to "", ""spanType"": ""span-simple-text""}, {""url"": ""https://population.un.org/wpp/Publications/Files/WPP2019_Highlights.pdf"", ""children"": [{""text"": ""its 2019 edition"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", the global population would reach 10.9 billion by 2100 and keep growing. The 2022 revision was the first to project a peak in the 21st century."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/un-population-2024-vs-2022"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Not every country has seen a drop in projected population compared to the last edition. The chart below shows the differences between the two UN revisions, region by region. Note that the vertical axis scale for each region is different, allowing you to see the changes more clearly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The latest UN revision has downgraded its future population estimates in Asia, Africa, and Latin America but "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""increased"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" its projections for Europe and North America."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/un-population-2024-vs-2022?uniformYAxis=0&country=OWID_WRL~Africa+%28UN%29~Asia+%28UN%29~Europe+%28UN%29~Northern+America+%28UN%29~Latin+America+and+the+Caribbean+%28UN%29"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Falling fertility rates are driving this slowdown in population growth"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Although the global population is expected to increase for many more decades, the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography?facet=none&country=~OWID_WRL&Metric=Population+growth+rate&Sex=Both+sexes&Age+group=Total&Projection+Scenario=Medium"", ""children"": [{""text"": ""population growth "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""children"": [{""url"": ""https://ourworldindata.org/explorers/population-and-demography?facet=none&country=~OWID_WRL&Metric=Population+growth+rate&Sex=Both+sexes&Age+group=Total&Projection+Scenario=Medium"", ""children"": [{""text"": ""rate"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""text"": "" is slowing rapidly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is driven by a dramatic reduction in fertility rates, which measure the average number of children per woman. The global fertility rate has "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography?facet=none&Metric=Fertility+rate&Sex=Both+sexes&Age+group=Total&Projection+Scenario=Medium&country=~OWID_WRL"", ""children"": [{""text"": ""more than halved"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" since the 1960s, from over 5 children per woman to 2.3."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This has happened at different rates worldwide, as you can see in the chart. Fertility rates in Europe, the Americas, and Asia are now below or close to 2 children per woman."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Across Africa, this figure is higher but has also fallen significantly. In the 1970s, it was almost 7 children per woman. Today, it’s almost 4. 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You can see this crossover in the chart below."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The UN also expects that China’s population has peaked and is declining. This is because of a rapid drop in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography?facet=none&country=~CHN&Metric=Fertility+rate&Sex=Both+sexes&Age+group=Total&Projection+Scenario=None"", ""children"": [{""text"": ""China’s fertility rates"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", which have been below the “replacement rate” — the average number of children per woman needed to keep the population constant from one generation to the next — for a long time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography?facet=none&country=CHN~IND&Metric=Population&Sex=Both+sexes&Age+group=Total&Projection+Scenario=Medium&hideControls=true&tab=chart"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Life expectancy is returning to pre-pandemic levels"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/coronavirus"", ""children"": [{""text"": ""COVID-19 pandemic"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" led to a large number of deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The number of deaths in 2020 was around 5 million higher than in 2019. In 2021, there were an additional 10 million."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This led to a significant drop in life expectancy across the world. But, rates are now returning to pre-pandemic levels. You can see this rebound in the chart below."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Globally, life expectancy in 2022 roughly matched the rate in 2019. And it increased again in 2023."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography?facet=none&country=OWID_WRL~Africa+%28UN%29~Asia+%28UN%29~Northern+America+%28UN%29~Latin+America+and+the+Caribbean+%28UN%29~Europe+%28UN%29&Metric=Life+expectancy&Sex=Both+sexes&Age+group=At+birth&Projection+Scenario=None&hideControls=true&tab=chart"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Six million people fled Ukraine in 2022 and 2023"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Millions of Ukrainians fled the country in 2022 as a result of the Russian invasion."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The UN estimates that "", ""spanType"": ""span-simple-text""}, {""id"": ""netmigration"", ""children"": [{""text"": ""net migration"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""out"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of Ukraine was 5.7 million in 2022 and 300,000 in 2023, or around 6 million over these two years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Many more have been displaced "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""within"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" Ukraine, but these internal migrants are not captured in international migration statistics."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This point also applies to more recent conflicts — such as those in Palestine and Sudan — where most displacement has been within the country rather than across country borders."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography?facet=none&country=~UKR&Metric=Net+migration&Sex=Both+sexes&Age+group=Total&Projection+Scenario=None&hideControls=true&tab=chart"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Read our key findings from the previous edition of the UN’s population data:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1wCZsgwS9Tlh8ySeanWnDgoX-fVftlVgzTBW8N8FSQE8/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""article"", ""title"": ""Peak global population and other key findings from the 2024 UN World Population Prospects"", ""authors"": [""Hannah Ritchie"", ""Lucas Rodés-Guirao""], ""excerpt"": ""Falling fertility rates, migration movements, and China’s population decline."", ""dateline"": ""July 11, 2024"", ""subtitle"": ""Falling fertility rates, migration movements, and China’s population decline."", ""featured-image"": ""un-population-featured-image.png""}",1,2024-06-18 12:53:35,2024-07-11 22:00:00,2024-07-12 05:17:09,listed,ALBJ4Lsl6EMiPnBh0P6ltsRLzOShT-8sYBtgMyyQiRpk01p2ZB4I-GerIeXpL0c9MRBWZWMdOZgFrfPnn4cRrw,,"We can’t understand the world without understanding demographic change. How many people are alive today? How many are born; how many die? What do we expect populations to look like in the future? The United Nations updates its big dataset — the [World Population Prospects](https://population.un.org/wpp/) — every two years to answer these questions. It just released its latest edition today. We’ve updated all of our population-related datasets and charts with this new release. You can explore all the trends for every country in our [Population and Demography Data Explorer](https://ourworldindata.org/explorers/population-and-demography). In this article, we wanted to provide key insights from this latest wave of data. # The world population is projected to peak slightly earlier than in previous projections The United Nations doesn’t only publish historical estimates of how population and demographic trends have changed in the past; it also makes projections for what the future might look like. To be clear, these are projections, _not predictions_ of changes in the future. In its 2022 publication, the UN estimated that, in its medium scenario, the global population would peak in 2086 at around 10.4 billion people. This year’s edition brings this peak forward slightly to 2084, with the population topping at just under 10.3 billion. The chart below compares the two revisions. This isn’t the first time the projected peak has been pulled earlier. According to [its 2019 edition](https://population.un.org/wpp/Publications/Files/WPP2019_Highlights.pdf), the global population would reach 10.9 billion by 2100 and keep growing. The 2022 revision was the first to project a peak in the 21st century. Not every country has seen a drop in projected population compared to the last edition. The chart below shows the differences between the two UN revisions, region by region. Note that the vertical axis scale for each region is different, allowing you to see the changes more clearly. The latest UN revision has downgraded its future population estimates in Asia, Africa, and Latin America but _increased_ its projections for Europe and North America. # Falling fertility rates are driving this slowdown in population growth Although the global population is expected to increase for many more decades, the [population growth ](https://ourworldindata.org/explorers/population-and-demography?facet=none&country=~OWID_WRL&Metric=Population+growth+rate&Sex=Both+sexes&Age+group=Total&Projection+Scenario=Medium)_[rate](https://ourworldindata.org/explorers/population-and-demography?facet=none&country=~OWID_WRL&Metric=Population+growth+rate&Sex=Both+sexes&Age+group=Total&Projection+Scenario=Medium)_ is slowing rapidly. This is driven by a dramatic reduction in fertility rates, which measure the average number of children per woman. The global fertility rate has [more than halved](https://ourworldindata.org/explorers/population-and-demography?facet=none&Metric=Fertility+rate&Sex=Both+sexes&Age+group=Total&Projection+Scenario=Medium&country=~OWID_WRL) since the 1960s, from over 5 children per woman to 2.3. This has happened at different rates worldwide, as you can see in the chart. Fertility rates in Europe, the Americas, and Asia are now below or close to 2 children per woman. Across Africa, this figure is higher but has also fallen significantly. In the 1970s, it was almost 7 children per woman. Today, it’s almost 4. And the UN expects rates to keep dropping to less than 3 in 2050 and approaching 2 by the end of the century. # China’s population may have already peaked In its 2022 revision, the UN projected that India would overtake China to become the world’s most populous country in 2023. Its new estimates confirm this. You can see this crossover in the chart below. The UN also expects that China’s population has peaked and is declining. This is because of a rapid drop in [China’s fertility rates](https://ourworldindata.org/explorers/population-and-demography?facet=none&country=~CHN&Metric=Fertility+rate&Sex=Both+sexes&Age+group=Total&Projection+Scenario=None), which have been below the “replacement rate” — the average number of children per woman needed to keep the population constant from one generation to the next — for a long time. # Life expectancy is returning to pre-pandemic levels The [COVID-19 pandemic](http://ourworldindata.org/coronavirus) led to a large number of deaths. The number of deaths in 2020 was around 5 million higher than in 2019. In 2021, there were an additional 10 million. This led to a significant drop in life expectancy across the world. But, rates are now returning to pre-pandemic levels. You can see this rebound in the chart below. Globally, life expectancy in 2022 roughly matched the rate in 2019. And it increased again in 2023. # Six million people fled Ukraine in 2022 and 2023 Millions of Ukrainians fled the country in 2022 as a result of the Russian invasion. The UN estimates that net migration _out_ of Ukraine was 5.7 million in 2022 and 300,000 in 2023, or around 6 million over these two years. Many more have been displaced _within_ Ukraine, but these internal migrants are not captured in international migration statistics. This point also applies to more recent conflicts — such as those in Palestine and Sudan — where most displacement has been within the country rather than across country borders. 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But it's unlikely that would add up to ~80% of users."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This again indicates our survey respondents are much more engaged than the average user."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What topics have our users read about and explored on Our World in Data?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Unsurprisingly, COVID-19 is the most popular topic among respondents right now. However, there are several other topics that are popular as well. In fact, the survey audience seems to read and explore a much more varied array of topics than our full audience. Overall, about "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""57% of page views are COVID-19 related"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" currently. But for "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""the survey audience, the share of responses in which COVID-19 is mentioned as a topic of interest, is only 19%."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""Where do we go from here?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The results of this survey provide some interesting avenues for further exploration. To really get to the why of a user's visit, we need to talk to our users, ask follow up questions, and digest their answers. This is what we're currently doing."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""article"", ""title"": ""Audience Survey - Who uses Our World in Data, and for what purpose?"", ""authors"": [""Ernst van Woerden""], ""excerpt"": ""Millions of people use Our World in Data every month. We looked at survey responses to understand who uses our work, and what for."", ""dateline"": ""October 5, 2020"", ""subtitle"": ""Millions of people use Our World in Data every month. We looked at survey responses to understand who uses our work, and what for."", ""sidebar-toc"": false, ""featured-image"": ""Audience-survey-results-1.png""}",1,2024-02-22 03:09:20,2020-10-05 07:14:00,2024-02-23 09:14:42,listed,ALBJ4LvBSNhMIFFeVoRSmDfTbAfEBFOCM51O5fZVqHZRoXRPUZ4I4RKiRSXfNsQcBqNxUZtKYvpjFT2jZrfANA,,"# Our World in Data as a museum Lately, I've been using the analogy of a museum to describe Our World in Data. A museum offers its visitors a variety of experiences: you can book a guided tour and hear all the detailed stories behind the artworks, explore for yourself and wander around without a predetermined purpose, or maybe you're only interested in one specific exhibition, or even just a single work of art. Our articles are the guided tour, our [data](https://ourworldindata.org/coronavirus-data-explorer) [explorers](https://ourworldindata.org/explorers/co2?tab=chart&xScale=linear&yScale=linear&stackMode=absolute&endpointsOnly=0&time=earliest..latest&country=China~United%20States~India~United%20Kingdom~World®ion=World&Gas%20=CO%E2%82%82&Accounting%20=Production-based&Fuel%20=Total&Count%20=Per%20capita&Relative%20to%20world%20total%20=) (and charts, to some extent) allow for freeform discovery, and our search and navigation support quick and focused data lookups. We are a multi-purpose publication, and we serve a large variety of visitors. We're also a small team, so we need to be clear about our focus. Should we optimize for in-depth reading of our articles, remove all clutter, and offer a clean, distraction-free reading experience? Should we strive to offer as many interactive controls and options as possible for exploring our data, even on mobile? Or should we make it really easy and fast to look up specific data points? Can we build one structure that offers a home to these very different user behaviors? # The what, the who, and the how Our analytics data tells us which pages are popular (these days [COVID-19](https://owid.cloud/coronavirus) is at the top), where our users are coming from (mostly Google), and on which device type people are more likely to interact with our visualizations (desktop). While that is highly useful data, it doesn't give a clear idea of **who our users are**, and **how they use Our World in Data**. --- # The Survey On September 8, we sent out [a survey to learn more about our audience](https://owid.cloud/help-us-improve-our-world-in-data). The main questions we wanted to answer: * Who is our audience? What different types of users visit us, and what does the distribution of their occupations look like? * What do people visit us for? Do they mostly use the visualizations, or read the articles? How many of our users come to us to quickly look up information? * What's the relation between our users' occupations and how they use Our World in Data? Are there any key combinations we should focus on? We posted the survey on social media, on our website, and we featured it in [our newsletter](https://staging-owid.netlify.app/subscribe). And this is where the first interesting insight emerged: **the most responses by far came from our newsletter.** This means our survey population is most likely not a representative sample of our total audience. In fact, it would be fair to assume that survey respondents are the more active part of our newsletter readers, which makes for an even more biased sample. To see how this subset of active newsletter readers matches up to our total audience, we've contrasted the responses to data from our analytics, where possible. # The gist of it: our newsletter readers are power users These are they key differences between the newsletter readers and our total audience, as we know them through analytics: * They visit us more often than average, mostly on desktop, and are mostly knowledge workers: business people, people in technology, the medical field, and educators and researchers. * Downloading data for further analysis is up to 12 times more popular for this group than it is for the average user, especially for business people and researchers. * Exploratory behavior and using us as a reference seem to be the most popular behaviors. * Desktop users spend about 3 times as much time on Our World in Data as mobile users, and are more likely to interact with our visualizations. In contrast, mobile use seems mainly about using us as a quick reference, or passively reading entries and looking at charts. Not entirely surprising, but good to confirm nonetheless. * The difference in sharing behavior of the survey respondents and our total audience is staggering: 80% vs 1% (although that 1% doesn't include informal sharing, or sharing screenshots and links). * The survey audience reads and explores a larger variety of our topics than the average user. This might be partly because of the particular focus right now, the ""average user"" at the moment is probably someone mostly interested in COVID-19. # Who is our (newsletter) audience? Business people and people in technology make up the majority of newsletter readers. In part, this is due to the fact that [most people in rich countries work in services](https://ourworldindata.org/grapher/employment-by-economic-sector). Since we always have a big focus on health it makes sense that people from the medical field are interested in our work – the fact that we live through a pandemic might have increased this further. Researchers and educators also represent a sizable group, which makes sense as these two groups would have a natural interest in the world's data on global problems and how to make progress. Most newsletter readers say they visit us every month, which matches analytics data from our total audience. An interesting difference is that a much larger portion of newsletter readers seem to come back to Our World in Data on a weekly basis. We roughly send a newsletter every week, so that may not be a coincidence. # What does our (newsletter) audience use Our World in Data for? Most respondents **(25%)** say they explore our data using our charts and visualizations. In our total audience, about **16%** of users interact with a visualization. A second popular use is fact-checking and referencing **(22%).** Surprisingly, about **17.5%** of respondents say they download data to conduct their own analysis, whereas among our total audience, **1.6% of sessions have a CSV download or a Github visit** from our website. Fact-checking, reading an article, and browsing are behaviors that are quite difficult to measure with analytics; is a high number of link clicks a sign of browsing around, or does it indicate someone on a mission to find a specific fact or data point? Does a short visit mean the user found what she is looking for, or that she got stuck and gave up quickly? The survey provides us with a glimpse of how our (newsletter) audience uses Our World in Data, but to get to a more in-depth understanding, we'll need to talk to our users in more detail (which I'm doing almost daily right now). # How does behavior differ across occupations? Overall, exploring our visualizations and using us as a reference seem to be similarly popular amongst the three biggest occupation categories. * Business people like to do their own analysis, compared to other categories. * Medical professionals and people in technology look at charts and specific data points; a difference between the two is that people in technology reportedly read more articles. * Retirees seem to do a lot of fact-checking and specific data point look-ups, but don't conduct their own analysis. * Researchers mostly explore our data by using our visualizations. * Students read our articles, possibly because they have assignments that require a deeper understanding of a topic. * The policy and writer groups are small in numbers but potentially very high in impact and reach, respectively. # How do people share our work? About **80%** of respondents say they share our work with others, mostly to explain things to friends, family, or coworkers. Two other notable sharing behaviors are sharing on social media or embedding a chart, as well as using our material in presentations and articles. But again, data from our analytics paints a different picture: **1% of usersclick on the share tab in a chart or download a PNG/SVG image of a chart.** That of course doesn't include screenshots, copying links directly from the browser address bar, and other forms of informal sharing such as word of mouth. But it's unlikely that would add up to ~80% of users. This again indicates our survey respondents are much more engaged than the average user. # What topics have our users read about and explored on Our World in Data? Unsurprisingly, COVID-19 is the most popular topic among respondents right now. However, there are several other topics that are popular as well. In fact, the survey audience seems to read and explore a much more varied array of topics than our full audience. Overall, about **57% of page views are COVID-19 related** currently. But for **the survey audience, the share of responses in which COVID-19 is mentioned as a topic of interest, is only 19%.** --- # Where do we go from here? The results of this survey provide some interesting avenues for further exploration. To really get to the why of a user's visit, we need to talk to our users, ask follow up questions, and digest their answers. This is what we're currently doing.","Audience Survey - Who uses Our World in Data, and for what purpose?" 1xvpuhv6qiU_cIKj8RfBPuW_t6sBwTruu7PQWWfHHRCs,quaternary-megafauna-extinction,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Humans have had such a profound impact on the planet’s ecosystems and climate that Earth might be defined by a new geological epoch: the Anthropocene (where “anthropo” means “human”)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Some think this new epoch should start during the Industrial Revolution, and some at the advent of agriculture 10,000 to 15,000 years ago. This feeds into the popular notion that environmental destruction is a recent phenomenon."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The lives of our ancestors are often romanticized. Many think they lived in balance with nature, unlike modern society where we fight against it. But when we look at the evidence of human impacts over millennia, it's hard to see how this was true."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Our ancient ancestors contributed to the extinction of many of the world's largest mammals ('megafauna'). This was during an event known as the Quaternary megafauna extinction (QME)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The extent of these extinctions across continents is shown in the chart. Between 52,000 and 9,000 BCE, more than 178 species of the world’s largest mammals were killed off. These were mammals heavier than 44 kilograms, ranging from mammals the size of sheep to mammoths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""narrow"", ""type"": ""image"", ""filename"": ""QME-Extinctions.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There is strong evidence to suggest that these were largely driven by humans – we look at this in more detail later."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Africa was the least hard-hit, losing only 21% of its megafauna. Humans evolved in Africa, and hominins had already interacted with mammals for a long time. The same is also likely to be true across Eurasia, where 35% of megafauna were lost. But Australia, North America, and South America were particularly hard-hit; very soon after humans arrived, most large mammals were gone. Australia lost 88%; North America lost 83%; and South America, 72%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Far from being in balance with ecosystems, tiny populations of hunter-gatherers changed them forever. By 8,000 BCE – almost at the end of the QME – there were only around "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/world-population-1750-2015-and-un-projection-until-2100"", ""children"": [{""text"": ""5 million people"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Did humans cause the Quaternary megafauna extinction?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The driver of the QME has been debated for centuries. Climate and human impacts have been proposed as potential drivers."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One possibility is that "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""both"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" played some role in the downfall of the mammals. This could be through overhunting, the reshaping of landscapes through fire, or the introduction of invasive species."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are several reasons why we think our ancestors were at least partly responsible."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Extinction timings closely match the timing of human arrival."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" The timing of megafauna extinctions was not consistent across the world; instead, the timing of their demise coincided closely with the arrival of humans on each continent. The timing of human arrivals and extinction events is shown on the map below."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Humans reached Australia somewhere between 65 to 44,000 years ago."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Between 50 and 40,000 years ago, 82% of megafauna had been wiped out. It was tens of thousands of years before the extinctions in North and South America occurred. And several more before these occurred in Madagascar and the Caribbean islands. Elephant birds in Madagascar were still present eight millennia after the mammoth and mastodon were killed off in America. Extinction events followed in man’s footsteps."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""QME selectively impacted large mammals. "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""There have been many extinction events in Earth’s history. There have been five big mass extinction events and several smaller ones. These events don’t usually target specific groups of animals. Large ecological changes tend to impact everything from large to small mammals, reptiles, birds, and fish. During times of high climate variability over the past 66 million years (the ‘Cenozoic period’), neither small nor large mammals were more vulnerable to extinction."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The QME was different and unique in the fossil record: it selectively killed off large mammals. Now there are obvious reasons why larger mammals are at a greater risk of extinction from any cause: they are slower to reproduce, so declines or crashes in their populations take longer to recover from."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But there is also a strong bias to human pressures: humans selectively hunt the larger ones."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Islands were more heavily impacted than Africa. "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""As we saw previously, Africa was less heavily impacted than other continents during this period. We might expect this since hominids had been interacting with mammals for a long time before this. These interactions between species would have impacted mammal populations more gradually and to a lesser extent. They may have already reached some form of equilibrium. When humans arrived on other continents – such as Australia or the Americas – these interactions were new and represented a step-change in the dynamics of the ecosystem. Humans were efficient new predators."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There have now been many studies focused on the question of whether humans were a key driver of the QME. Many suggest that the answer is yes. Climatic changes might have driven an initial decline in large mammal populations – small population crashes – but human pressures are likely to have thwarted their recovery. Large mammals survived previous periods of climatic change, but the arrival of humans put pressure on already-depleted populations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Human impact on ecosystems dates back tens of thousands of years, despite the Anthropocene paradigm implying this is a recent phenomenon. We’ve not only been in direct competition with other mammals, but we’ve also reshaped the landscape beyond recognition."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Human-arrival-map.png"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""82da26090c9cb8bedfb965750eb003de47f74a56"": {""id"": ""82da26090c9cb8bedfb965750eb003de47f74a56"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Andermann, T., Faurby, S., Turvey, S. T., Antonelli, A., & Silvestro, D. (2020). 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"", ""spanType"": ""span-simple-text""}, {""url"": ""https://science.sciencemag.org/content/360/6386/310"", ""children"": [{""text"": ""Body size downgrading of mammals over the late Quaternary"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Science"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""360"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(6386), 310-313."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Did humans cause the Quaternary megafauna extinction?"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""10,000 to 50,000 years ago, hundreds of the largest mammals went extinct. It's likely that humans were the key driver of this."", ""subtitle"": ""10,000 to 50,000 years ago, hundreds of the largest mammals went extinct. It's likely that humans were the key driver of this."", ""sidebar-toc"": false, ""featured-image"": ""Quaternary-extinctions-featured-image.png""}",1,2024-03-09 18:05:37,2022-11-30 11:37:17,2024-03-11 11:29:39,listed,ALBJ4LtXF6KU10eTPAtxz4eK8D-pWgWwXoqYEDQKczY1TGO_SAMKpNPFE64QOOzqJUsRkR4ZjKfVWrvt5mDQvw,,"Humans have had such a profound impact on the planet’s ecosystems and climate that Earth might be defined by a new geological epoch: the Anthropocene (where “anthropo” means “human”). Some think this new epoch should start during the Industrial Revolution, and some at the advent of agriculture 10,000 to 15,000 years ago. This feeds into the popular notion that environmental destruction is a recent phenomenon. The lives of our ancestors are often romanticized. Many think they lived in balance with nature, unlike modern society where we fight against it. But when we look at the evidence of human impacts over millennia, it's hard to see how this was true. Our ancient ancestors contributed to the extinction of many of the world's largest mammals ('megafauna'). This was during an event known as the Quaternary megafauna extinction (QME). The extent of these extinctions across continents is shown in the chart. Between 52,000 and 9,000 BCE, more than 178 species of the world’s largest mammals were killed off. These were mammals heavier than 44 kilograms, ranging from mammals the size of sheep to mammoths. There is strong evidence to suggest that these were largely driven by humans – we look at this in more detail later. Africa was the least hard-hit, losing only 21% of its megafauna. Humans evolved in Africa, and hominins had already interacted with mammals for a long time. The same is also likely to be true across Eurasia, where 35% of megafauna were lost. But Australia, North America, and South America were particularly hard-hit; very soon after humans arrived, most large mammals were gone. Australia lost 88%; North America lost 83%; and South America, 72%. Far from being in balance with ecosystems, tiny populations of hunter-gatherers changed them forever. By 8,000 BCE – almost at the end of the QME – there were only around [5 million people](https://ourworldindata.org/grapher/world-population-1750-2015-and-un-projection-until-2100) in the world. # Did humans cause the Quaternary megafauna extinction? The driver of the QME has been debated for centuries. Climate and human impacts have been proposed as potential drivers. One possibility is that _both_ played some role in the downfall of the mammals. This could be through overhunting, the reshaping of landscapes through fire, or the introduction of invasive species. There are several reasons why we think our ancestors were at least partly responsible. **Extinction timings closely match the timing of human arrival.** The timing of megafauna extinctions was not consistent across the world; instead, the timing of their demise coincided closely with the arrival of humans on each continent. The timing of human arrivals and extinction events is shown on the map below. Humans reached Australia somewhere between 65 to 44,000 years ago.1 Between 50 and 40,000 years ago, 82% of megafauna had been wiped out. It was tens of thousands of years before the extinctions in North and South America occurred. And several more before these occurred in Madagascar and the Caribbean islands. Elephant birds in Madagascar were still present eight millennia after the mammoth and mastodon were killed off in America. Extinction events followed in man’s footsteps. **QME selectively impacted large mammals. **There have been many extinction events in Earth’s history. There have been five big mass extinction events and several smaller ones. These events don’t usually target specific groups of animals. Large ecological changes tend to impact everything from large to small mammals, reptiles, birds, and fish. During times of high climate variability over the past 66 million years (the ‘Cenozoic period’), neither small nor large mammals were more vulnerable to extinction.2 The QME was different and unique in the fossil record: it selectively killed off large mammals. Now there are obvious reasons why larger mammals are at a greater risk of extinction from any cause: they are slower to reproduce, so declines or crashes in their populations take longer to recover from. But there is also a strong bias to human pressures: humans selectively hunt the larger ones. **Islands were more heavily impacted than Africa. **As we saw previously, Africa was less heavily impacted than other continents during this period. We might expect this since hominids had been interacting with mammals for a long time before this. These interactions between species would have impacted mammal populations more gradually and to a lesser extent. They may have already reached some form of equilibrium. When humans arrived on other continents – such as Australia or the Americas – these interactions were new and represented a step-change in the dynamics of the ecosystem. Humans were efficient new predators. There have now been many studies focused on the question of whether humans were a key driver of the QME. Many suggest that the answer is yes. Climatic changes might have driven an initial decline in large mammal populations – small population crashes – but human pressures are likely to have thwarted their recovery. Large mammals survived previous periods of climatic change, but the arrival of humans put pressure on already-depleted populations. Human impact on ecosystems dates back tens of thousands of years, despite the Anthropocene paradigm implying this is a recent phenomenon. We’ve not only been in direct competition with other mammals, but we’ve also reshaped the landscape beyond recognition. Andermann, T., Faurby, S., Turvey, S. T., Antonelli, A., & Silvestro, D. (2020). [The past and future human impact on mammalian diversity](https://advances.sciencemag.org/content/6/36/eabb2313). _Science Advances_, _6_(36), eabb2313. Smith, F. A., Smith, R. E. E., Lyons, S. K., & Payne, J. L. (2018). [Body size downgrading of mammals over the late Quaternary](https://science.sciencemag.org/content/360/6386/310). _Science_, _360_(6386), 310-313. Smith, F. A., Smith, R. E. E., Lyons, S. K., & Payne, J. L. (2018). [Body size downgrading of mammals over the late Quaternary](https://science.sciencemag.org/content/360/6386/310). _Science_, _360_(6386), 310-313.",Did humans cause the Quaternary megafauna extinction? 1xsCTYaOy86WqxPNbBBkfWAmSVIxXithmC7gNhemU2rg,contributed-most-global-co2,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Since 1751 the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/cumulative-co2-emissions-region?stackMode=absolute"", ""children"": [{""text"": ""world has emitted"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" over 1.5 trillion tonnes of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" To reach our "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions#future-emission-scenarios"", ""children"": [{""text"": ""climate goal"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of limiting average temperature rise to 2°C, the world needs to urgently reduce emissions. One common argument is that those countries which have added most to the CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" in our atmosphere – contributing most to the problem today – should take on the greatest responsibility in tackling it."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We can compare each country’s total contribution to global emissions by looking at "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""cumulative"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""children"": [{""text"": ""CO"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-subscript""}, {""text"": "". We can calculate cumulative emissions by adding up each country’s annual CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" emissions over time. We did this calculation for each country and region over the period from 1751 through to 2017."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The distribution of cumulative emissions around the world is shown in the treemap. Treemaps are used to compare entities (such as countries or regions) in relation to others, and relative to the total. Here countries are presented as rectangles and colored by region. The size of each rectangle corresponds to the sum of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" emissions from a country between 1751 and 2017. Combined, all rectangles represent the global total."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Cumulative-CO2-treemap.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are some key points we can learn from this perspective:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""the United States has emitted more CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" than any other country to date: at around 400 billion tonnes since 1751, it is responsible for 25% of historical emissions;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""this is twice more than China – the world’s second largest national contributor;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""the 28 countries of the European Union (EU-28) – which are grouped together here as they typically negotiate and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://climateactiontracker.org/countries/eu/"", ""children"": [{""text"": ""set targets"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" on a collaborative basis – is also a large historical contributor at 22%;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""many of the large annual emitters today – such as India and Brazil – are not large contributors in a historical context;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Africa’s regional contribution – relative to its "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/world-population-by-world-regions-post-1820?stackMode=relative"", ""children"": [{""text"": ""population size"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – has been very small. This is the result of very low per capita emissions – both historically and currently."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All of this data is also explorable by country and over time in the interactive map. By clicking on any country you can see the country’s cumulative emissions over time, and compare it with other countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/cumulative-co-emissions?tab=map"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""How has each region’s share of global cumulative CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" emissions changed over time?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the visualizations above we focused on each country or region’s total cumulative emissions (1) in absolute terms; and (2) at a single point in time: as of 2017."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart we see the change in the share of global cumulative emissions by region over time – from 1751 through to 2017."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Up until 1950, more than half of historical CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" emissions were emitted by Europe. 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Using the timeline at the bottom of the chart you can see how contribution across the world has evolved since 1751. By clicking on a country you can see an individual country’s cumulative contribution over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-cumulative-co2"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The map for 2017 shows the large inequalities of contribution across the world that the first treemap visualization has shown. The USA has emitted most to date: more than a quarter of all historical CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "": twice that of China which is the second largest contributor. In contrast, most countries across Africa have been responsible for less than 0.01% of all emissions over the last 266 years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What becomes clear when we look at emissions across the world today is that the countries with the highest emissions over history are not always the biggest emitters today. The UK, for example, was responsible for only "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/annual-share-of-co2-emissions?tab=chart&country=GBR"", ""children"": [{""text"": ""1% of global emissions"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in 2017. Reductions here will have a relatively small impact on emissions at the global level – or at least fall far short of the scale of change we need. This creates tension with the argument that the largest contributors in the past should be those doing most to reduce emissions today. This is because a large fraction of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" remains in the atmosphere for hundreds of years once emitted."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This inequality is one of the main reasons which makes international agreement on who should take action so challenging."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""7d713b55b3441430af207006899d62e756ad98dc"": {""id"": ""7d713b55b3441430af207006899d62e756ad98dc"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Carbon dioxide (CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "") emissions from fossil fuel combustion were almost zero prior to 1750. 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Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f3f2475164afc15b77a3810ead3519694f4128f7"": {""id"": ""f3f2475164afc15b77a3810ead3519694f4128f7"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The underlying data sources for annual CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" emissions data come from the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://cdiac.ess-dive.lbl.gov/"", ""children"": [{""text"": ""Carbon Dioxide Analysis Center"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (CDIAC) and the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.globalcarbonproject.org/carbonbudget/index.htm"", ""children"": [{""text"": ""Global Carbon Project"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The cumulative figures were calculated by Our World in Data based on these annual estimate sources."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Who has contributed most to global CO2 emissions?"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""There's not only significant variability in how much CO2 countries emit across the world today. There are also large differences in how much each has emitted in the past. Who has contributed most to global CO2 since 1750?"", ""dateline"": ""October 1, 2019"", ""subtitle"": ""There's not only significant variability in how much CO2 countries emit across the world today. There are also large differences in how much each has emitted in the past. Who has contributed most to global CO2 since 1750?"", ""sidebar-toc"": false, ""featured-image"": ""Cumulative-CO2-treemap.png""}",1,2024-02-09 08:16:38,2019-10-01 15:00:19,2024-02-09 11:21:19,listed,ALBJ4LtZvrOfVUhFyAZIXx7_OB0uFuQCi9_TjzpUyneN5BxLM6S6Co8-Od8Dh6yQoxo3XaAd72r5gqXBwqEvgw,,"Since 1751 the [world has emitted](https://ourworldindata.org/grapher/cumulative-co2-emissions-region?stackMode=absolute) over 1.5 trillion tonnes of CO2.1 To reach our [climate goal](https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions#future-emission-scenarios) of limiting average temperature rise to 2°C, the world needs to urgently reduce emissions. One common argument is that those countries which have added most to the CO2 in our atmosphere – contributing most to the problem today – should take on the greatest responsibility in tackling it. We can compare each country’s total contribution to global emissions by looking at _cumulative__CO__2_. We can calculate cumulative emissions by adding up each country’s annual CO2 emissions over time. We did this calculation for each country and region over the period from 1751 through to 2017.2 The distribution of cumulative emissions around the world is shown in the treemap. Treemaps are used to compare entities (such as countries or regions) in relation to others, and relative to the total. Here countries are presented as rectangles and colored by region. The size of each rectangle corresponds to the sum of CO2 emissions from a country between 1751 and 2017. Combined, all rectangles represent the global total. There are some key points we can learn from this perspective: * the United States has emitted more CO2 than any other country to date: at around 400 billion tonnes since 1751, it is responsible for 25% of historical emissions; * this is twice more than China – the world’s second largest national contributor; * the 28 countries of the European Union (EU-28) – which are grouped together here as they typically negotiate and [set targets](https://climateactiontracker.org/countries/eu/) on a collaborative basis – is also a large historical contributor at 22%; * many of the large annual emitters today – such as India and Brazil – are not large contributors in a historical context; * Africa’s regional contribution – relative to its [population size](https://ourworldindata.org/grapher/world-population-by-world-regions-post-1820?stackMode=relative) – has been very small. This is the result of very low per capita emissions – both historically and currently. All of this data is also explorable by country and over time in the interactive map. By clicking on any country you can see the country’s cumulative emissions over time, and compare it with other countries. # How has each region’s share of global cumulative CO2 emissions changed over time? In the visualizations above we focused on each country or region’s total cumulative emissions (1) in absolute terms; and (2) at a single point in time: as of 2017. In the chart we see the change in the share of global cumulative emissions by region over time – from 1751 through to 2017. Up until 1950, more than half of historical CO2 emissions were emitted by Europe. The vast majority of European emissions back then were emitted by the United Kingdom; as the [data shows](https://ourworldindata.org/grapher/share-of-cumulative-co2?tab=chart&country=GBR), until 1882 more than half of the world’s cumulative emissions came from the UK alone. Over the century which followed, industrialization in the USA rapidly increased its contribution. It’s only over the past 50 years that growth in South America, Asia and Africa have increased these regions’ share of total contribution. # How has each country’s share of global cumulative CO2 emissions changed over time? In the final visualization you can explore the same cumulative CO2 emissions as you have seen above but now visualizes _by country_. Using the timeline at the bottom of the chart you can see how contribution across the world has evolved since 1751. By clicking on a country you can see an individual country’s cumulative contribution over time. The map for 2017 shows the large inequalities of contribution across the world that the first treemap visualization has shown. The USA has emitted most to date: more than a quarter of all historical CO2: twice that of China which is the second largest contributor. In contrast, most countries across Africa have been responsible for less than 0.01% of all emissions over the last 266 years. What becomes clear when we look at emissions across the world today is that the countries with the highest emissions over history are not always the biggest emitters today. The UK, for example, was responsible for only [1% of global emissions](https://ourworldindata.org/grapher/annual-share-of-co2-emissions?tab=chart&country=GBR) in 2017. Reductions here will have a relatively small impact on emissions at the global level – or at least fall far short of the scale of change we need. This creates tension with the argument that the largest contributors in the past should be those doing most to reduce emissions today. This is because a large fraction of CO2 remains in the atmosphere for hundreds of years once emitted.3 This inequality is one of the main reasons which makes international agreement on who should take action so challenging. Carbon dioxide (CO2) emissions from fossil fuel combustion were almost zero prior to 1750. The United Kingdom was the world’s first industrialized nation – and first fossil-fuel CO2 emitter. In 1751 its (and global) [emissions were](https://ourworldindata.org/grapher/annual-co2-emissions-per-country?tab=chart&year=1751&country=GBR) less than 10 million tonnes – 3600 times less than [global emissions today](https://ourworldindata.org/grapher/annual-co-emissions-by-region). We can conclude that emissions prior to 1750 were very low (and inconsequential to the numbers we compare today). You can find further information on how long historical emissions (dating back to 1751) are estimated **[here](https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions#how-do-we-measure-or-estimate-co2-emissions)**. The underlying data sources for annual CO2 emissions data come from the [Carbon Dioxide Analysis Center](https://cdiac.ess-dive.lbl.gov/) (CDIAC) and the [Global Carbon Project](https://www.globalcarbonproject.org/carbonbudget/index.htm). The cumulative figures were calculated by Our World in Data based on these annual estimate sources. IPCC, 2013: [Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change](https://www.ipcc.ch/report/ar5/wg1/) [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.",Who has contributed most to global CO2 emissions? 1xdTt6o9OWxJ9RJAQ1DiyfTuFZGPfGnHUFq5AA86sBOg,rise-us-maternal-mortality-rates-measurement,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Look at reported maternal mortality rates in the United States, and you’ll see an alarming rise since the early 2000s."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This rise has been widely covered in the media. See a 2023 article on Scientific American: “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.scientificamerican.com/article/why-maternal-mortality-rates-are-getting-worse-across-the-u-s/"", ""children"": [{""text"": ""Why Maternal Mortality Rates Are Getting Worse across the U.S."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” Or a report on National Public Radio (NPR): “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.npr.org/sections/health-shots/2023/07/04/1185904749/u-s-maternal-deaths-keep-rising-heres-who-is-most-at-risk"", ""children"": [{""text"": ""The number of people dying in the U.S. from pregnancy-related causes has more than doubled in the last 20 years."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” It has, understandably, been a big concern among the public."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But researchers have shown that this rise does not represent an actual increase in the number of women dying in childbirth. Rather, it is the result of a change in measurement that was gradually introduced in the US between 2003 and 2017."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This change wasn’t adopted at a national level in a single moment; that would have led to a single step-wise change in mortality rates. Instead, the measurement change was adopted state by state, which led to a "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""gradual"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" rise over 14 years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This measurement change has helped to identify more deaths that meet the criteria for maternal deaths, but has also led to some misclassification."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The process of "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/how-are-causes-of-death-registered-around-the-world"", ""children"": [{""text"": ""determining the cause of death"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" can be complicated, and in many countries, national statistics from death certificates tend to miss some maternal deaths. To tackle this, some countries have used additional systems to identify maternal deaths that would otherwise be unreported."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this article, I explain how a change in measurement in the United States led to an apparent rising trend in maternal deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""The recent rise in reported US maternal mortality looks alarming"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""id"": ""maternal-mortality"", ""children"": [{""text"": ""Maternal mortality"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" refers to the death of mothers from pregnancy, childbirth, abortion, or related causes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart below shows trends in reported maternal mortality rates between different countries. Rates are measured as the number of maternal deaths per 100,000 women in the population. It shows the statistics as reported to the World Health Organization."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can see that the maternal mortality rate has fallen across all of these countries since 1950."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But in the last two decades, the rate appears to have risen steeply in the United States. Between 2003 and 2017, it has more than doubled, from 0.4 to 0.8 deaths per 100,000 women."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In other countries such as France, Canada, and the United Kingdom the rates were stable or slightly falling."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""reported-maternal-mortality-rate-us-canada-france-uk.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""Maternal deaths had previously been underestimated"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""According to the "", ""spanType"": ""span-simple-text""}, {""id"": ""icd"", ""children"": [{""text"": ""International Classification of Diseases"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", which is the international system to classify causes of death, a maternal death is counted if pregnancy or related causes are listed as the “"", ""spanType"": ""span-simple-text""}, {""id"": ""underlying-cause-of-death"", ""children"": [{""text"": ""underlying cause of death"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": ""” on a death certificate."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This cause of death is filled into the field of the death certificate by doctors and nurses, based on the circumstances of death and medical records, according to their medical knowledge. The cause of death is then reported in the country’s "", ""spanType"": ""span-simple-text""}, {""id"": ""crvs"", ""children"": [{""text"": ""vital registry"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can read more about how causes of death are determined in my article:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/13dQWV1hDG1I0bf0HLvYGrgGxlW-7l1ci6rhL9Wf2XQc/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But research has found that data from death certificates often underestimates maternal deaths."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One reason is that some maternal deaths are missed and attributed to other causes. This can happen because pregnancy can worsen pre-existing conditions, such as "", ""spanType"": ""span-simple-text""}, {""id"": ""hiv_aids"", ""children"": [{""text"": ""HIV/AIDS"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""id"": ""cvd"", ""children"": [{""text"": ""cardiovascular diseases"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", and thereby indirectly lead to a woman’s death."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In these cases, it can be difficult for doctors to make a judgment call on whether the woman would have died if she had not been pregnant."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To complicate the situation further, sometimes medical records are lacking or inaccessible, and many deceased women don’t have linked hospital records or undergo an autopsy to confirm whether they were pregnant at the time of death."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There can also be social, cultural, or legal issues around reporting whether women were pregnant when they died."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To address these problems, which can lead to the underestimation of maternal deaths, the "", ""spanType"": ""span-simple-text""}, {""id"": ""icd"", ""children"": [{""text"": ""International Classification of Diseases (ICD)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" expanded its definition of maternal deaths and recommended that countries collect additional data on whether deceased women had been pregnant before their death."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the ninth edition, known as ICD-9 (published in 1979), the definition of maternal mortality was very narrow: it focused only on deaths "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""during childbirth or the postpartum period"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "", and considered any causes related to, or aggravated by, the pregnancy or its management."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The updated ICD-10 (published in 1994) expanded the definition. It considered all those deaths as maternal which happened "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""during pregnancy, childbirth, or within 42 days of the end of pregnancy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "", from any cause related to or aggravated by the pregnancy or its management, but not from accidental or incidental causes."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The ICD-10 also recommended that a “pregnancy checkbox” be included in national death certificates, which would help flag these deaths for further investigation to understand if they were caused by pregnancy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The checkbox asked if the deceased woman was pregnant or had been recently pregnant. You can see an example below, which is used in death certificates in the United States."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This checkbox was introduced to reduce underestimation and to capture maternal mortality more accurately."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""narrow"", ""type"": ""image"", ""caption"": [{""text"": ""The “pregnancy checkbox” section of death certificates in the United States. This section was added to death certificates in some states in 2003, and was then gradually adopted across all other US states. The figure is adapted from Catalano et al. (2020)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""checkbox-explanation.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""As the checkbox was gradually implemented in the United States, more maternal deaths were reported"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To follow the ICD-10 definition and make sure that maternal deaths weren’t going uncounted, the United States added the “pregnancy checkbox” to death certificates, starting in 2003."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The US used an automated system to code deaths as maternal deaths if the checkbox was ticked for women between the ages of 10 and 54, for deaths caused by medical conditions, regardless of other information on the death certificate."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 2003, four states had implemented the pregnancy checkbox — Idaho, Maryland, Montana, and New York state."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the following years, more and more states added the checkbox. It wasn’t until 2017 that every state included it on death certificates. You can see this in the chart below."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As more and more states included the pregnancy checkbox, more deaths were identified as related to pregnancy, and the reported maternal mortality rate increased."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""pregnancy-checkbox-maternal-mortality-usa.png"", ""hasOutline"": false, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To understand the impact of the measurement change, we can also examine what happened to maternal mortality "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""within"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" states."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is shown in the chart below, which plots the average maternal mortality ratio before and after the change."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart comes from reports from the National Center for Health Statistics and the National Vital Statistics System, which is part of the Centers for Disease Control and Prevention."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can see that once the checkbox was implemented, the reported maternal mortality ratio suddenly increased — on average, it "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""doubled"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" from 10 to 20 deaths per 100,000 births — and then remained stable."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""The impact of the pregnancy checkbox and misclassification on maternal mortality trends in the United States, 1999–2017."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""pregnancy-checkbox-average-change.png"", ""hasOutline"": false, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The researchers also estimated what the maternal mortality ratio from 2003 to 2017 would have looked like under two hypothetical scenarios: (a) if all states adopted the checkbox simultaneously, or (b) if none of them did."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In both scenarios, they estimate that there would have been no change in maternal mortality ratios between 2003 and 2017."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In other words, the rise in maternal mortality is largely explained by the staggered adoption of the checkbox."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The researchers also noted that the impact of the change in measurement was greatest among older women and non-Hispanic black women."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The checkbox increased the ability to detect pregnancy-related deaths that would have been missed otherwise, but in some cases, it also resulted in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""overcounting"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" deaths from other causes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In a quality assurance study of four US states, researchers found that around 21% of death certificates with the checkbox ticked were confirmed to be false positives — data from other health systems confirmed they had not been pregnant, and this was especially the case in girls aged under 15 and women aged over 45."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-15"", ""children"": [{""children"": [{""text"": ""15"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" One reason for these false positives is that the box was ticked accidentally in some cases."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-16"", ""children"": [{""children"": [{""text"": ""16"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To reduce these false positives, another change in measurement has been made in US statistics: from 2018 onwards, the checkbox is disregarded for women and girls aged under 10 or over 45."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-17"", ""children"": [{""children"": [{""text"": ""17"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Researchers also recommended further quality assurance processes — such as conducting follow-ups to verify ticked checkboxes and improving training for death certifiers — to be conducted in other states in the US "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""before"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" sending data to the "", ""spanType"": ""span-simple-text""}, {""id"": ""crvs"", ""children"": [{""text"": ""vital registry"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", to improve the accuracy of national data on maternal deaths."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-18"", ""children"": [{""children"": [{""text"": ""18"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""Maternal mortality is underreported in national statistics in many countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While the United States has used the checkbox to automatically code deaths as maternal if it is ticked"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-19"", ""children"": [{""children"": [{""text"": ""19"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "", this practice is not followed in several other countries."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-20"", ""children"": [{""children"": [{""text"": ""20"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There is strong evidence that maternal mortality, as defined in the "", ""spanType"": ""span-simple-text""}, {""id"": ""icd"", ""children"": [{""text"": ""ICD"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", is underreported in national statistics in many countries."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-21"", ""children"": [{""children"": [{""text"": ""21"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One reason is that some countries do not use data from the checkbox to identify potential maternal deaths, or do not routinely conduct additional investigations to identify unreported maternal deaths."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-20"", ""children"": [{""children"": [{""text"": ""20"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Some countries "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""have"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" implemented systems separate from their "", ""spanType"": ""span-simple-text""}, {""id"": ""crvs"", ""children"": [{""text"": ""vital registries"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" to investigate potential maternal deaths further."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-22"", ""children"": [{""children"": [{""text"": ""22"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" These systems include “enhanced surveillance”, which involves an additional system for more detailed monitoring, and “confidential inquiries”, which are private investigations into individual cases."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These investigations have been conducted infrequently, and the maternal deaths identified through these systems are not necessarily counted in vital registries for national statistics and given to the WHO."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-23"", ""children"": [{""children"": [{""text"": ""23"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Research finds that the number of maternal deaths from "", ""spanType"": ""span-simple-text""}, {""id"": ""crvs"", ""children"": [{""text"": ""vital registries"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" tend to be lower than equivalent definitions from these other surveillance systems."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-24"", ""children"": [{""children"": [{""text"": ""24"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In low- and middle-income countries — where death certificates and "", ""spanType"": ""span-simple-text""}, {""id"": ""crvs"", ""children"": [{""text"": ""vital registries"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" are often lacking — other sources of data are used to determine maternal deaths, including hospital records, and "", ""spanType"": ""span-simple-text""}, {""id"": ""verbal-autopsy"", ""children"": [{""text"": ""verbal autopsies"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Maternal mortality data from these sources can also include women who have died from incidental or accidental causes of death that are unrelated to their pregnancy, because data to identify the specific causes of death may be lacking."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-25"", ""children"": [{""children"": [{""text"": ""25"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""International statistics on maternal mortality are adjusted for underreporting, but uncertainties remain"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To address this problem — of underreported maternal deaths in "", ""spanType"": ""span-simple-text""}, {""id"": ""crvs"", ""children"": [{""text"": ""vital registries"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" — the United Nations Maternal Mortality Estimation Inter-agency Group (MMEIG) uses other data sources and expert knowledge to adjust for underreporting."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In countries that lack other surveillance systems for maternal deaths, the number of maternal deaths are adjusted upwards."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In countries that have enhanced surveillance data, different adjustment factors are used to adjust for incompleteness and misclassification."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-26"", ""children"": [{""children"": [{""text"": ""26"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Unfortunately, this adjustment can be imprecise because many countries lack comprehensive data on causes of death, or have not conducted national investigations into unreported maternal deaths, which could be used to understand the degree of underreporting in each country and improve adjustment factors."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-27"", ""children"": [{""children"": [{""text"": ""27"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""Conclusion"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To prevent avoidable maternal deaths, it’s crucial to have accurate data on deaths caused by pregnancy and related causes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Unfortunately, maternal deaths are often underreported in official statistics due to a range of reasons, such as missing medical records and poor training of death certifiers."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To help identify missed deaths, the United States introduced a “pregnancy 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""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In other high-income countries, there is strong evidence that maternal mortality is underreported in national statistics."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Some countries rely on additional systems to uncover unreported maternal deaths, but these tend to be conducted infrequently and are not necessarily considered in national statistics."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In contrast, data from low- and middle-income countries — which tend to lack death certificates, hospital records, and vital registries — are less precise."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""International organizations try to adjust for these problems of underreporting and misclassification, but without 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American Journal of Obstetrics and Gynecology, 222(3), 269.e1-269.e8. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/j.ajog.2019.10.005"", ""children"": [{""text"": ""https://doi.org/10.1016/j.ajog.2019.10.005"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""46c3260ad153c6cfdfe3e685cb7f1fabcff62bf3"": {""id"": ""46c3260ad153c6cfdfe3e685cb7f1fabcff62bf3"", ""index"": 23, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Diguisto, C., Saucedo, M., Kallianidis, A., Bloemenkamp, K., Bødker, B., Buoncristiano, M., Donati, S., Gissler, M., Johansen, M., Knight, M., Korbel, M., Kristufkova, A., Nyflot, L. T., & Deneux-Tharaux, C. (2022). Maternal mortality in eight European countries with enhanced surveillance systems: Descriptive population based study. BMJ, e070621. 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"", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1111/j.1471-0528.2012.03330.x"", ""children"": [{""text"": ""https://doi.org/10.1111/j.1471-0528.2012.03330.x"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Atrash, H., Alexander, S., & Berg, C. (1995). Maternal mortality in developed countries: Not just a concern of the past. Obstetrics & Gynecology, 86(4), 700–705. 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"", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/j.ajog.2017.04.042"", ""children"": [{""text"": ""https://doi.org/10.1016/j.ajog.2017.04.042"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""848c051fb45722952359c9c69226ceb86eb2152f"": {""id"": ""848c051fb45722952359c9c69226ceb86eb2152f"", ""index"": 24, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Gazeley, U., Reniers, G., Eilerts-Spinelli, H., Prieto, J. R., Jasseh, M., Khagayi, S., & Filippi, V. (2022). Women’s risk of death beyond 42 days post partum: A pooled analysis of longitudinal Health and Demographic Surveillance System data in sub-Saharan Africa. The Lancet Global Health, 10(11), e1582–e1589. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/S2214-109X(22)00339-4"", ""children"": [{""text"": ""https://doi.org/10.1016/S2214-109X(22)00339-4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""866035d77d002f6a7313fe631cd8272938985881"": {""id"": ""866035d77d002f6a7313fe631cd8272938985881"", ""index"": 16, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Hoyert, D. L., & Miniño, A. M. (2020). Maternal mortality in the United States: Changes in coding, publication, and data release, 2018. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://stacks.cdc.gov/view/cdc/84769"", ""children"": [{""text"": ""https://stacks.cdc.gov/view/cdc/84769"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""8cdf58bf9c9f72c61d34271ae7b45c23cd2f1f78"": {""id"": ""8cdf58bf9c9f72c61d34271ae7b45c23cd2f1f78"", ""index"": 18, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For women aged above 55, the coding instructions were to not rely only on the checkbox item, but use other information in the cause of death certificate about pregnancy or obstetric causes of death."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Hoyert, D. L., Uddin, S. F., & Miniño, A. M. (2020). Evaluation of the pregnancy status checkbox on the identification of maternal deaths. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://stacks.cdc.gov/view/cdc/84768/cdc_84768_DS1.pdf"", ""children"": [{""text"": ""https://stacks.cdc.gov/view/cdc/84768/cdc_84768_DS1.pdf"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""8d41121cd1daaffbdc8dc07b98202ba30cde2636"": {""id"": ""8d41121cd1daaffbdc8dc07b98202ba30cde2636"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Catalano, A., Davis, N. L., Petersen, E. E., Harrison, C., Kieltyka, L., You, M., Conrey, E. J., Ewing, A. C., Callaghan, W. M., & Goodman, D. A. (2020). Pregnant? Validity of the pregnancy checkbox on death certificates in four states, and characteristics associated with pregnancy checkbox errors. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""American Journal of Obstetrics and Gynecology"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""222"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(3), 269.e1-269.e8."", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/j.ajog.2019.10.005"", ""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""url"": ""https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7056489/"", ""children"": [{""text"": ""https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7056489/"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a3602cdfdb76881c29640cee6e2965b48a742e5c"": {""id"": ""a3602cdfdb76881c29640cee6e2965b48a742e5c"", ""index"": 25, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The 2023 UN MMEIG estimates have not adjusted for the change in the measurement of maternal mortality in the US."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is because the inclusion criteria of the UN MMEIG’s model uses data on false positive and false negative rates from a national-level inquiry into individual-level data, using multiple sources via a review process, where one of the sources needs to be the national Civil Registry and Vital Statistics (CRVS) system."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There has not yet been a national-level inquiry using data from the CRVS in the United States to investigate the false-positive and false-negative rates of maternal deaths across the country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Instead, inquiries into individual data in the US have been conducted in a selection of states so far, and the National Center for Health Statistics (NCHS) has used other approaches to understand the impact of the checkbox, and simulate the trends with and without the checkbox."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These approaches aren’t included in the UN MMEIG’s models currently."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""World Health Organization. (2023). Trends in maternal mortality 2000 to 2020. Estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://iris.who.int/bitstream/handle/10665/366225/9789240068759-eng.pdf?sequence=1"", ""children"": [{""text"": ""https://iris.who.int/bitstream/handle/10665/366225/9789240068759-eng.pdf?sequence=1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Ahmed, S. M. A., Cresswell, J. A., & Say, L. (2023). Incompleteness and misclassification of maternal death recording: A systematic review and meta-analysis. BMC Pregnancy and Childbirth, 23(1), 794. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1186/s12884-023-06077-4"", ""children"": [{""text"": ""https://doi.org/10.1186/s12884-023-06077-4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""aa7fc0f6aab551dae43c289f2a2810dec2c902c6"": {""id"": ""aa7fc0f6aab551dae43c289f2a2810dec2c902c6"", ""index"": 21, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Diguisto, C., Saucedo, M., Kallianidis, A., Bloemenkamp, K., Bødker, B., Buoncristiano, M., Donati, S., Gissler, M., Johansen, M., Knight, M., Korbel, M., Kristufkova, A., Nyflot, L. T., & Deneux-Tharaux, C. (2022). Maternal mortality in eight European countries with enhanced surveillance systems: Descriptive population based study. BMJ, e070621. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1136/bmj-2022-070621"", ""children"": [{""text"": ""https://doi.org/10.1136/bmj-2022-070621"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""bf7ea208ee21e523215536521dcdb81b41334317"": {""id"": ""bf7ea208ee21e523215536521dcdb81b41334317"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Atrash, H., Alexander, S., & Berg, C. (1995). Maternal mortality in developed countries: Not just a concern of the past. Obstetrics & Gynecology, 86(4), 700–705. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/0029-7844(95)00200-B"", ""children"": [{""text"": ""https://doi.org/10.1016/0029-7844(95)00200-B"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c88fef693430284687cf7501009c32f9494ca157"": {""id"": ""c88fef693430284687cf7501009c32f9494ca157"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Lumbiganon, P., Laopaiboon, M., Intarut, N., Vogel, J., Souza, J., Gülmezoglu, A., & Mori, R. (2014). Indirect causes of severe adverse maternal outcomes: A secondary analysis of the WHO Multicountry Survey on Maternal and Newborn Health. BJOG: An International Journal of Obstetrics & Gynaecology, 121(s1), 32–39. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1111/1471-0528.12647"", ""children"": [{""text"": ""https://doi.org/10.1111/1471-0528.12647"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d0c22570e11c1a05eff851409dbe20ef37212f31"": {""id"": ""d0c22570e11c1a05eff851409dbe20ef37212f31"", ""index"": 26, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""World Health Organization. (2022). Maternal mortality measurement: Guidance to improve national reporting. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.who.int/publications/i/item/9789240052376"", ""children"": [{""text"": ""https://www.who.int/publications/i/item/9789240052376"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d0cdb706673351a6d28e700d6e99f855891e2f2d"": {""id"": ""d0cdb706673351a6d28e700d6e99f855891e2f2d"", ""index"": 13, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Rossen, L. M., Womack, L. S., Hoyert, D. L., Anderson, R. N., & Uddin, S. F. (2020). The impact of the pregnancy checkbox and misclassification on maternal mortality trends in the United States, 1999–2017. National Center for Health Statistics. Vital Health Stat 3(44). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.cdc.gov/nchs/data/series/sr_03/sr03_044-508.pdf"", ""children"": [{""text"": ""https://www.cdc.gov/nchs/data/series/sr_03/sr03_044-508.pdf"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It’s important to note that racial disparities are visible regardless of the change in measurement, which is shown in Figure 14 in the report. In other words, with or without the checkbox, the maternal mortality rate was higher among black women than white women. At the same time, the measurement change is estimated to have had a larger impact on maternal mortality rates of black women than white women."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f4da9c0388bb4177fe7f9b8df91f456f5f26380a"": {""id"": ""f4da9c0388bb4177fe7f9b8df91f456f5f26380a"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This definition continues in the most recent manual:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""According to the ICD-11, maternal deaths are defined as the deaths of women while pregnant or within 42 days of termination of pregnancy, from pregnancy-related causes, but excluding accidental or incidental causes of death."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""fa6071aac3f44629ca6c19b398d9242257c29b8b"": {""id"": ""fa6071aac3f44629ca6c19b398d9242257c29b8b"", ""index"": 22, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""One exception is the United Kingdom’s Confidential Enquiry into Maternal Deaths (CEMD) — which is a long-running program that requires the reporting of maternal deaths from diverse sources such as health workers, coroners, family members, and media reports — and verifies maternal mortality data to enhances its quality and completeness."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Lin, C.-Y., Tsai, P.-Y., Wang, L.-Y., Chen, G., Kuo, P.-L., Lee, M.-C., & Lu, T.-H. (2019). Changes in the number and causes of maternal deaths after the introduction of pregnancy checkbox on the death certificate in Taiwan. Taiwanese Journal of Obstetrics and Gynecology, 58(5), 680–683. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/j.tjog.2019.07.017"", ""children"": [{""text"": ""https://doi.org/10.1016/j.tjog.2019.07.017"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Aflaki, K., & Ray, J. G. (2023). How other countries can improve Canada’s maternal mortality statistics. Obstetric Medicine, 16(4), 211–216. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1177/1753495X231178405"", ""children"": [{""text"": ""https://doi.org/10.1177/1753495X231178405"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Callaghan, J., Dudenhausen, J., Paulson, L., Hellmeyer, L., Vetter, K., Ziegert, M., Braun, T., & Koenigbauer, J. T. (2023). Analysis of maternal mortality in Berlin, Germany – discrepancy between reported maternal mortality and comprehensive death certificate exploration. Journal of Perinatal Medicine, 0(0). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1515/jpm-2023-0403"", ""children"": [{""text"": ""https://doi.org/10.1515/jpm-2023-0403"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Bouvier‐Colle, M., Mohangoo, A., Gissler, M., Novak‐Antolic, Z., Vutuc, C., Szamotulska, K., Zeitlin, J., & for The Euro‐Peristat Scientific Committee. (2012). What about the mothers? An analysis of maternal mortality and morbidity in perinatal health surveillance systems in Europe. BJOG: An International Journal of Obstetrics & Gynaecology, 119(7), 880–890. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1111/j.1471-0528.2012.03330.x"", ""children"": [{""text"": ""https://doi.org/10.1111/j.1471-0528.2012.03330.x"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""The rise in reported maternal mortality rates in the US is largely due to a change in measurement"", ""authors"": [""Saloni Dattani""], ""excerpt"": ""Maternal mortality rates appear to have risen in the last 20 years in the US. But this reflects a change in measurement rather than an actual rise in mortality."", ""subtitle"": ""Maternal mortality rates appear to have risen in the last 20 years in the US. But this reflects a change in measurement rather than an actual rise in mortality."", ""featured-image"": ""maternal-mortality-checkbox-thumbnail-edit.png""}",1,2024-03-13 17:17:04,2024-05-13 08:03:00,2024-05-09 11:56:02,listed,ALBJ4Lvwb_fqZJVG0npYw-039izMLv4NmxVDAIpCJPCb62CNukiwfiN-9vqa3eUH_ozZSSwyoM8sqD8DsVYVtg,,"Look at reported maternal mortality rates in the United States, and you’ll see an alarming rise since the early 2000s. This rise has been widely covered in the media. See a 2023 article on Scientific American: “[Why Maternal Mortality Rates Are Getting Worse across the U.S.](https://www.scientificamerican.com/article/why-maternal-mortality-rates-are-getting-worse-across-the-u-s/)” Or a report on National Public Radio (NPR): “[The number of people dying in the U.S. from pregnancy-related causes has more than doubled in the last 20 years.](https://www.npr.org/sections/health-shots/2023/07/04/1185904749/u-s-maternal-deaths-keep-rising-heres-who-is-most-at-risk)” It has, understandably, been a big concern among the public. But researchers have shown that this rise does not represent an actual increase in the number of women dying in childbirth. Rather, it is the result of a change in measurement that was gradually introduced in the US between 2003 and 2017. This change wasn’t adopted at a national level in a single moment; that would have led to a single step-wise change in mortality rates. Instead, the measurement change was adopted state by state, which led to a _gradual_ rise over 14 years. This measurement change has helped to identify more deaths that meet the criteria for maternal deaths, but has also led to some misclassification. The process of [determining the cause of death](https://ourworldindata.org/how-are-causes-of-death-registered-around-the-world) can be complicated, and in many countries, national statistics from death certificates tend to miss some maternal deaths. To tackle this, some countries have used additional systems to identify maternal deaths that would otherwise be unreported. In this article, I explain how a change in measurement in the United States led to an apparent rising trend in maternal deaths. # The recent rise in reported US maternal mortality looks alarming Maternal mortality refers to the death of mothers from pregnancy, childbirth, abortion, or related causes. The chart below shows trends in reported maternal mortality rates between different countries. Rates are measured as the number of maternal deaths per 100,000 women in the population. It shows the statistics as reported to the World Health Organization.1 You can see that the maternal mortality rate has fallen across all of these countries since 1950. But in the last two decades, the rate appears to have risen steeply in the United States. Between 2003 and 2017, it has more than doubled, from 0.4 to 0.8 deaths per 100,000 women. In other countries such as France, Canada, and the United Kingdom the rates were stable or slightly falling. # Maternal deaths had previously been underestimated According to the International Classification of Diseases, which is the international system to classify causes of death, a maternal death is counted if pregnancy or related causes are listed as the “underlying cause of death” on a death certificate. This cause of death is filled into the field of the death certificate by doctors and nurses, based on the circumstances of death and medical records, according to their medical knowledge. The cause of death is then reported in the country’s vital registry. You can read more about how causes of death are determined in my article: ### undefined undefined https://docs.google.com/document/d/13dQWV1hDG1I0bf0HLvYGrgGxlW-7l1ci6rhL9Wf2XQc/edit But research has found that data from death certificates often underestimates maternal deaths.2 One reason is that some maternal deaths are missed and attributed to other causes. This can happen because pregnancy can worsen pre-existing conditions, such as HIV/AIDS and cardiovascular diseases, and thereby indirectly lead to a woman’s death.3 In these cases, it can be difficult for doctors to make a judgment call on whether the woman would have died if she had not been pregnant.4 To complicate the situation further, sometimes medical records are lacking or inaccessible, and many deceased women don’t have linked hospital records or undergo an autopsy to confirm whether they were pregnant at the time of death.5 There can also be social, cultural, or legal issues around reporting whether women were pregnant when they died. To address these problems, which can lead to the underestimation of maternal deaths, the International Classification of Diseases (ICD) expanded its definition of maternal deaths and recommended that countries collect additional data on whether deceased women had been pregnant before their death. In the ninth edition, known as ICD-9 (published in 1979), the definition of maternal mortality was very narrow: it focused only on deaths **during childbirth or the postpartum period**, and considered any causes related to, or aggravated by, the pregnancy or its management. The updated ICD-10 (published in 1994) expanded the definition. It considered all those deaths as maternal which happened **during pregnancy, childbirth, or within 42 days of the end of pregnancy**, from any cause related to or aggravated by the pregnancy or its management, but not from accidental or incidental causes.6 The ICD-10 also recommended that a “pregnancy checkbox” be included in national death certificates, which would help flag these deaths for further investigation to understand if they were caused by pregnancy. The checkbox asked if the deceased woman was pregnant or had been recently pregnant. You can see an example below, which is used in death certificates in the United States. This checkbox was introduced to reduce underestimation and to capture maternal mortality more accurately. # As the checkbox was gradually implemented in the United States, more maternal deaths were reported To follow the ICD-10 definition and make sure that maternal deaths weren’t going uncounted, the United States added the “pregnancy checkbox” to death certificates, starting in 2003. The US used an automated system to code deaths as maternal deaths if the checkbox was ticked for women between the ages of 10 and 54, regardless of other information on the death certificate.8 In 2003, four states had implemented the pregnancy checkbox — Idaho, Maryland, Montana, and New York state.9 In the following years, more and more states added the checkbox. It wasn’t until 2017 that every state included it on death certificates. You can see this in the chart below. As more and more states included the pregnancy checkbox, more deaths were identified as related to pregnancy, and the reported maternal mortality rate increased.10 To understand the impact of the measurement change, we can also examine what happened to maternal mortality rates _within_ states. This is shown in the chart below, which plots the average maternal mortality rate before and after the change. The chart comes from reports from the National Center for Health Statistics and the National Vital Statistics System, which is part of the Centers for Disease Control and Prevention. You can see that once the checkbox was implemented, the reported maternal mortality ratio suddenly increased — on average, it _doubled_ from 10 to 20 deaths per 100,000 births — and then remained stable.11 The researchers also estimated what the maternal mortality ratio from 2003 to 2017 would have looked like under two hypothetical scenarios: (a) if all states adopted the checkbox simultaneously, or (b) if none of them did. In both scenarios, they estimate that there would have been no change in maternal mortality ratios between 2003 and 2017. In other words, the rise in maternal mortality is largely explained by the staggered adoption of the checkbox.13 The researchers also noted that the impact of the change in measurement was greatest among older women and non-Hispanic black women. The checkbox increased the ability to detect pregnancy-related deaths that would have been missed otherwise, but in some cases, it also resulted in _overcounting_ deaths from other causes. In a quality assurance study of four US states, researchers found that around 21% of death certificates with the checkbox ticked were confirmed to be false positives — data from other health systems confirmed they had not been pregnant, and this was especially the case in girls aged under 15 and women aged over 45. One reason for these false positives is that the box was ticked accidentally in some cases. To reduce these false positives, another change in measurement has been made in US statistics: from 2018 onwards, the checkbox is disregarded for women and girls aged under 10 or over 54.14 Researchers also recommended further quality assurance processes — such as conducting follow-ups to verify ticked checkboxes and improving training for death certifiers — to be conducted in other states in the US _before_ sending data to the vital registry, to improve the accuracy of national data on maternal deaths.15 # Maternal mortality is underreported in national statistics in many countries While the United States has used the checkbox to automatically code deaths as maternal if it is ticked8, this practice is not followed in several other countries.16 There is strong evidence that maternal mortality, as defined in the ICD, is underreported in national statistics in many countries.17 One reason is that some countries do not use data from the checkbox to identify potential maternal deaths, or do not routinely conduct additional investigations to identify unreported maternal deaths.16 Some countries _have_ implemented systems separate from their vital registries to investigate potential maternal deaths further.18 These systems include “enhanced surveillance”, which involves an additional system for more detailed monitoring, and “confidential inquiries”, which are private investigations into individual cases. These investigations have been conducted infrequently, and the maternal deaths identified through these systems are not necessarily counted in vital registries for national statistics and given to the WHO.19 Research finds that the number of maternal deaths from vital registries tend to be lower than equivalent definitions from these other surveillance systems.20 In low- and middle-income countries — where death certificates and vital registries are often lacking — other sources of data are used to determine maternal deaths, including hospital records, and verbal autopsies. Maternal mortality data from these sources can also include women who have died from incidental or accidental causes of death that are unrelated to their pregnancy, because data to identify the specific causes of death may be lacking.21 # International statistics on maternal mortality are adjusted for underreporting, but uncertainties remain To address this problem — of underreported maternal deaths in vital registries — the United Nations Maternal Mortality Estimation Inter-agency Group (MMEIG) uses other data sources and expert knowledge to adjust for underreporting. In countries that lack other surveillance systems for maternal deaths, the number of maternal deaths are adjusted upwards. In countries that have enhanced surveillance data, different adjustment factors are used to adjust for incompleteness and misclassification.22 Unfortunately, this adjustment can be imprecise because many countries lack comprehensive data on causes of death, or have not conducted national investigations into unreported maternal deaths, which could be used to understand the degree of underreporting in each country and improve adjustment factors.23 # Conclusion To prevent avoidable maternal deaths, it’s crucial to have accurate data on deaths caused by pregnancy and related causes. Unfortunately, maternal deaths are often underreported in official statistics due to a range of reasons, such as missing medical records and poor training of death certifiers. To help identify missed deaths, the United States introduced a “pregnancy checkbox” on death certificates, and deaths of women with this box ticked would be coded as maternal deaths in most age groups. While this helped identify maternal deaths that would have been missed, it also led to some misclassification and false positives from women who had not been pregnant or had died from other incidental causes. Because of this, the US changed its coding system in 2018 to disregard the checkbox for deaths of patients under 10 or over 54 years old. Researchers have also recommended that additional quality-assurance measures are used to verify potential maternal deaths before they are compiled in US national statistics. In other high-income countries, there is strong evidence that maternal mortality is underreported in national statistics. Some countries rely on additional systems to uncover unreported maternal deaths, but these tend to be conducted infrequently and are not necessarily considered in national statistics. In contrast, data from low- and middle-income countries — which tend to lack death certificates, hospital records, and vital registries — are less precise. International organizations try to adjust for these problems of underreporting and misclassification, but without better surveillance in each country, the adjustments can be imprecise. By improving data collection and surveillance of maternal deaths further, the world can have a better understanding of where and why mothers are dying, mobilize resources and policies to save lives, and reduce maternal mortality further. The World Health Organization’s Mortality Database publishes national statistics as they have been reported by countries, without further adjustment except to replace non-standard ICD codes, if they have been used, with standard ICD codes. Atrash, H., Alexander, S., & Berg, C. (1995). Maternal mortality in developed countries: Not just a concern of the past. Obstetrics & Gynecology, 86(4), 700–705. [https://doi.org/10.1016/0029-7844(95)00200-B](https://doi.org/10.1016/0029-7844(95)00200-B) Catalano, A., Davis, N. L., Petersen, E. E., Harrison, C., Kieltyka, L., You, M., Conrey, E. J., Ewing, A. C., Callaghan, W. M., & Goodman, D. A. (2020). Pregnant? Validity of the pregnancy checkbox on death certificates in four states, and characteristics associated with pregnancy checkbox errors. American Journal of Obstetrics and Gynecology, 222(3), 269.e1-269.e8. [https://pubmed.ncbi.nlm.nih.gov/31639369/](https://pubmed.ncbi.nlm.nih.gov/31639369/) Lumbiganon, P., Laopaiboon, M., Intarut, N., Vogel, J., Souza, J., Gülmezoglu, A., & Mori, R. (2014). Indirect causes of severe adverse maternal outcomes: A secondary analysis of the WHO Multicountry Survey on Maternal and Newborn Health. BJOG: An International Journal of Obstetrics & Gynaecology, 121(s1), 32–39. [https://doi.org/10.1111/1471-0528.12647](https://doi.org/10.1111/1471-0528.12647) Atrash, H., Alexander, S., & Berg, C. (1995). Maternal mortality in developed countries: Not just a concern of the past. Obstetrics & Gynecology, 86(4), 700–705. [https://doi.org/10.1016/0029-7844(95)00200-B](https://doi.org/10.1016/0029-7844(95)00200-B) Joseph, K. S., Boutin, A., Lisonkova, S., Muraca, G. M., Razaz, N., John, S., Mehrabadi, A., Sabr, Y., Ananth, C. V., & Schisterman, E. (2021). Maternal Mortality in the United States: Recent Trends, Current Status, and Future Considerations. Obstetrics & Gynecology, 137(5), 763–771. [https://doi.org/10.1097/AOG.0000000000004361](https://doi.org/10.1097/AOG.0000000000004361) This definition continues in the most recent manual: According to the ICD-11, maternal deaths are defined as the deaths of women while pregnant or within 42 days of termination of pregnancy, from pregnancy-related causes, but excluding accidental or incidental causes of death. Catalano, A., Davis, N. L., Petersen, E. E., Harrison, C., Kieltyka, L., You, M., Conrey, E. J., Ewing, A. C., Callaghan, W. M., & Goodman, D. A. (2020). Pregnant? Validity of the pregnancy checkbox on death certificates in four states, and characteristics associated with pregnancy checkbox errors. _American Journal of Obstetrics and Gynecology_, _222_(3), 269.e1-269.e8.[ ](https://doi.org/10.1016/j.ajog.2019.10.005)[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7056489/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7056489/) For women aged above 55, the coding instructions were to not rely only on the checkbox item, but use other information in the cause of death certificate about pregnancy or obstetric causes of death. Hoyert, D. L., Uddin, S. F., & Miniño, A. M. (2020). Evaluation of the pregnancy status checkbox on the identification of maternal deaths. [https://stacks.cdc.gov/view/cdc/84768/cdc_84768_DS1.pdf](https://stacks.cdc.gov/view/cdc/84768/cdc_84768_DS1.pdf) Hoyert, D. L., Uddin, S. F., & Miniño, A. M. (2020). _Evaluation of the pregnancy status checkbox on the identification of maternal deaths_.[ ](https://stacks.cdc.gov/view/cdc/84768/cdc_84768_DS1.pdf)[https://stacks.cdc.gov/view/cdc/84768/cdc_84768_DS1.pdf](https://stacks.cdc.gov/view/cdc/84768/cdc_84768_DS1.pdf) Joseph, K. S., Boutin, A., Lisonkova, S., Muraca, G. M., Razaz, N., John, S., Mehrabadi, A., Sabr, Y., Ananth, C. V., & Schisterman, E. (2021). Maternal Mortality in the United States: Recent Trends, Current Status, and Future Considerations. Obstetrics & Gynecology, 137(5), 763–771. [https://doi.org/10.1097/AOG.0000000000004361](https://doi.org/10.1097/AOG.0000000000004361) In this study, the researchers made estimates using the maternal mortality ratio. The maternal mortality ratio is slightly different from the maternal mortality rate, but both measure the frequency of maternal deaths. The maternal mortality rate is typically calculated as the number of deaths per 100,000 women, while the maternal mortality ratio is calculated as the number of deaths per 100,000 live births. Rossen, L. M., Womack, L. S., Hoyert, D. L., Anderson, R. N., & Uddin, S. F. (2020). The impact of the pregnancy checkbox and misclassification on maternal mortality trends in the United States, 1999–2017. [https://www.cdc.gov/nchs/data/series/sr_03/sr03_044-508.pdf](https://www.cdc.gov/nchs/data/series/sr_03/sr03_044-508.pdf) Rossen, L. M., Womack, L. S., Hoyert, D. L., Anderson, R. N., & Uddin, S. F. (2020). The impact of the pregnancy checkbox and misclassification on maternal mortality trends in the United States, 1999–2017. National Center for Health Statistics. Vital Health Stat 3(44). [https://www.cdc.gov/nchs/data/series/sr_03/sr03_044-508.pdf](https://www.cdc.gov/nchs/data/series/sr_03/sr03_044-508.pdf) Rossen, L. M., Womack, L. S., Hoyert, D. L., Anderson, R. N., & Uddin, S. F. (2020). The impact of the pregnancy checkbox and misclassification on maternal mortality trends in the United States, 1999–2017. National Center for Health Statistics. Vital Health Stat 3(44). [https://www.cdc.gov/nchs/data/series/sr_03/sr03_044-508.pdf](https://www.cdc.gov/nchs/data/series/sr_03/sr03_044-508.pdf) See also: Davis, N. L., Hoyert, D. L., Goodman, D. A., Hirai, A. H., & Callaghan, W. M. (2017). Contribution of maternal age and pregnancy checkbox on maternal mortality ratios in the United States, 1978–2012. American Journal of Obstetrics and Gynecology, 217(3), 352.e1-352.e7. [https://doi.org/10.1016/j.ajog.2017.04.042](https://doi.org/10.1016/j.ajog.2017.04.042) Hoyert, D. L., & Miniño, A. M. (2020). Maternal mortality in the United States: Changes in coding, publication, and data release, 2018. [https://stacks.cdc.gov/view/cdc/84769](https://stacks.cdc.gov/view/cdc/84769) Daymude, A. E. C., Catalano, A., & Goodman, D. (2019). Checking the pregnancy checkbox: Evaluation of a four‐state quality assurance pilot. Birth, 46(4), 648–655. [https://doi.org/10.1111/birt.12425](https://doi.org/10.1111/birt.12425) Berg, C. J. (2012). From Identification and Review to Action—Maternal Mortality Review in the United States. Seminars in Perinatology, 36(1), 7–13. [https://doi.org/10.1053/j.semperi.2011.09.003](https://doi.org/10.1053/j.semperi.2011.09.003) Lin, C.-Y., Tsai, P.-Y., Wang, L.-Y., Chen, G., Kuo, P.-L., Lee, M.-C., & Lu, T.-H. (2019). Changes in the number and causes of maternal deaths after the introduction of pregnancy checkbox on the death certificate in Taiwan. Taiwanese Journal of Obstetrics and Gynecology, 58(5), 680–683. [https://doi.org/10.1016/j.tjog.2019.07.017](https://doi.org/10.1016/j.tjog.2019.07.017) Aflaki, K., & Ray, J. G. (2023). How other countries can improve Canada’s maternal mortality statistics. Obstetric Medicine, 16(4), 211–216. [https://doi.org/10.1177/1753495X231178405](https://doi.org/10.1177/1753495X231178405) Callaghan, J., Dudenhausen, J., Paulson, L., Hellmeyer, L., Vetter, K., Ziegert, M., Braun, T., & Koenigbauer, J. T. (2023). Analysis of maternal mortality in Berlin, Germany – discrepancy between reported maternal mortality and comprehensive death certificate exploration. Journal of Perinatal Medicine, 0(0). [https://doi.org/10.1515/jpm-2023-0403](https://doi.org/10.1515/jpm-2023-0403) World Health Organization. (2022). Maternal mortality measurement: Guidance to improve national reporting. [https://iris.who.int/bitstream/handle/10665/360576/9789240052376-eng.pdf?sequence=1](https://iris.who.int/bitstream/handle/10665/360576/9789240052376-eng.pdf?sequence=1) Diguisto, C., Saucedo, M., Kallianidis, A., Bloemenkamp, K., Bødker, B., Buoncristiano, M., Donati, S., Gissler, M., Johansen, M., Knight, M., Korbel, M., Kristufkova, A., Nyflot, L. T., & Deneux-Tharaux, C. (2022). Maternal mortality in eight European countries with enhanced surveillance systems: Descriptive population based study. BMJ, e070621. [https://doi.org/10.1136/bmj-2022-070621](https://doi.org/10.1136/bmj-2022-070621) One exception is the United Kingdom’s Confidential Enquiry into Maternal Deaths (CEMD) — which is a long-running program that requires the reporting of maternal deaths from diverse sources such as health workers, coroners, family members, and media reports — and verifies maternal mortality data to enhances its quality and completeness. Lin, C.-Y., Tsai, P.-Y., Wang, L.-Y., Chen, G., Kuo, P.-L., Lee, M.-C., & Lu, T.-H. (2019). Changes in the number and causes of maternal deaths after the introduction of pregnancy checkbox on the death certificate in Taiwan. Taiwanese Journal of Obstetrics and Gynecology, 58(5), 680–683. [https://doi.org/10.1016/j.tjog.2019.07.017](https://doi.org/10.1016/j.tjog.2019.07.017) Aflaki, K., & Ray, J. G. (2023). How other countries can improve Canada’s maternal mortality statistics. Obstetric Medicine, 16(4), 211–216. [https://doi.org/10.1177/1753495X231178405](https://doi.org/10.1177/1753495X231178405) Callaghan, J., Dudenhausen, J., Paulson, L., Hellmeyer, L., Vetter, K., Ziegert, M., Braun, T., & Koenigbauer, J. T. (2023). Analysis of maternal mortality in Berlin, Germany – discrepancy between reported maternal mortality and comprehensive death certificate exploration. Journal of Perinatal Medicine, 0(0). [https://doi.org/10.1515/jpm-2023-0403](https://doi.org/10.1515/jpm-2023-0403) Bouvier‐Colle, M., Mohangoo, A., Gissler, M., Novak‐Antolic, Z., Vutuc, C., Szamotulska, K., Zeitlin, J., & for The Euro‐Peristat Scientific Committee. (2012). What about the mothers? An analysis of maternal mortality and morbidity in perinatal health surveillance systems in Europe. BJOG: An International Journal of Obstetrics & Gynaecology, 119(7), 880–890. [https://doi.org/10.1111/j.1471-0528.2012.03330.x](https://doi.org/10.1111/j.1471-0528.2012.03330.x) Diguisto, C., Saucedo, M., Kallianidis, A., Bloemenkamp, K., Bødker, B., Buoncristiano, M., Donati, S., Gissler, M., Johansen, M., Knight, M., Korbel, M., Kristufkova, A., Nyflot, L. T., & Deneux-Tharaux, C. (2022). Maternal mortality in eight European countries with enhanced surveillance systems: Descriptive population based study. BMJ, e070621. [https://doi.org/10.1136/bmj-2022-070621](https://doi.org/10.1136/bmj-2022-070621) Bouvier‐Colle, M., Mohangoo, A., Gissler, M., Novak‐Antolic, Z., Vutuc, C., Szamotulska, K., Zeitlin, J., & for The Euro‐Peristat Scientific Committee. (2012). What about the mothers? An analysis of maternal mortality and morbidity in perinatal health surveillance systems in Europe. BJOG: An International Journal of Obstetrics & Gynaecology, 119(7), 880–890. [https://doi.org/10.1111/j.1471-0528.2012.03330.x](https://doi.org/10.1111/j.1471-0528.2012.03330.x) Atrash, H., Alexander, S., & Berg, C. (1995). Maternal mortality in developed countries: Not just a concern of the past. Obstetrics & Gynecology, 86(4), 700–705. [https://doi.org/10.1016/0029-7844(95)00200-B](https://doi.org/10.1016/0029-7844(95)00200-B) Gazeley, U., Reniers, G., Eilerts-Spinelli, H., Prieto, J. R., Jasseh, M., Khagayi, S., & Filippi, V. (2022). Women’s risk of death beyond 42 days post partum: A pooled analysis of longitudinal Health and Demographic Surveillance System data in sub-Saharan Africa. The Lancet Global Health, 10(11), e1582–e1589. [https://doi.org/10.1016/S2214-109X(22)00339-4](https://doi.org/10.1016/S2214-109X(22)00339-4) The 2023 UN MMEIG estimates have not adjusted for the change in the measurement of maternal mortality in the US. This is because the inclusion criteria of the UN MMEIG’s model uses data on false positive and false negative rates from a national-level inquiry into individual-level data, using multiple sources via a review process, where one of the sources needs to be the national Civil Registry and Vital Statistics (CRVS) system. There has not yet been a national-level inquiry using data from the CRVS in the United States to investigate the false-positive and false-negative rates of maternal deaths across the country. Instead, inquiries into individual data in the US have been conducted in a selection of states so far, and the National Center for Health Statistics (NCHS) has used other approaches to understand the impact of the checkbox, and simulate the trends with and without the checkbox. These approaches aren’t included in the UN MMEIG’s models currently. World Health Organization. (2023). Trends in maternal mortality 2000 to 2020. Estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. [https://iris.who.int/bitstream/handle/10665/366225/9789240068759-eng.pdf?sequence=1](https://iris.who.int/bitstream/handle/10665/366225/9789240068759-eng.pdf?sequence=1) Ahmed, S. M. A., Cresswell, J. A., & Say, L. (2023). Incompleteness and misclassification of maternal death recording: A systematic review and meta-analysis. BMC Pregnancy and Childbirth, 23(1), 794. [https://doi.org/10.1186/s12884-023-06077-4](https://doi.org/10.1186/s12884-023-06077-4) World Health Organization. (2022). Maternal mortality measurement: Guidance to improve national reporting. [https://www.who.int/publications/i/item/9789240052376](https://www.who.int/publications/i/item/9789240052376)",The rise in reported maternal mortality rates in the US is largely due to a change in measurement 1xXL0L0uu5fYOQKjLNbvSrFcORvHPVNV1CX8_RYUd2wg,literacy,linear-topic-page,"{""toc"": [{""slug"": ""global-literacy-today"", ""text"": ""Global literacy today"", ""title"": ""Global literacy today"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""historical-change-in-literacy"", ""text"": ""Historical change in literacy"", ""title"": ""Historical change in literacy"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""global-literacy-has-grown-substantially-in-the-last-two-centuries"", ""text"": ""Global literacy has grown substantially in the last two centuries"", ""title"": ""Global literacy has grown substantially in the last two centuries"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""when-did-literacy-start-increasing-in-europe"", ""text"": ""When did literacy start increasing in Europe?"", ""title"": ""When did literacy start increasing in Europe?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""the-ambition-of-universal-literacy-in-europe-was-a-reform-born-of-the-enlightenment"", ""text"": ""The ambition of universal literacy in Europe was a reform born of the Enlightenment"", ""title"": ""The ambition of universal literacy in Europe was a reform born of the Enlightenment"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""in-the-us-the-expansion-of-literacy-helped-reduce-within-country-inequalities"", ""text"": ""In the US, the expansion of literacy helped reduce within-country inequalities"", ""title"": ""In the US, the expansion of literacy helped reduce within-country inequalities"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""literacy-and-access-to-education-have-increased-around-the-world"", ""text"": ""Literacy and access to education have increased around the world"", ""title"": ""Literacy and access to education have increased around the world"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""latin-america-has-made-huge-improvements-in-literacy-in-the-last-century"", ""text"": ""Latin America has made huge improvements in literacy in the last century"", ""title"": ""Latin America has made huge improvements in literacy in the last century"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""literacy-around-the-world-today"", ""text"": ""Literacy around the world today"", ""title"": ""Literacy around the world today"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""literacy-by-generation"", ""text"": ""Literacy by generation"", ""title"": ""Literacy by generation"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""literacy-rates-by-sex"", ""text"": ""Literacy rates by sex"", ""title"": ""Literacy rates by sex"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""numeracy"", ""text"": ""Numeracy"", ""title"": ""Numeracy"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""numeracy-skills-over-the-long-run"", ""text"": ""Numeracy skills over the long run"", ""title"": ""Numeracy skills over the long run"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""numeracy-skills-today"", ""text"": ""Numeracy skills today"", ""title"": ""Numeracy skills today"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""definitions-measurement"", ""text"": ""Definitions & Measurement"", ""title"": ""Definitions & Measurement"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""measurement-today"", ""text"": ""Measurement today"", ""title"": ""Measurement today"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""reconstructing-estimates-from-the-past"", ""text"": ""Reconstructing estimates from the past"", ""title"": ""Reconstructing estimates from the past"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""all-charts"", ""text"": ""Interactive charts on literacy"", ""title"": ""Interactive charts on literacy"", ""isSubheading"": false}, {""slug"": ""article-endnotes"", ""text"": ""Endnotes"", ""title"": ""Endnotes"", ""isSubheading"": false}, {""slug"": ""article-citation"", ""text"": ""Citation"", ""title"": ""Citation"", ""isSubheading"": false}, {""slug"": ""article-licence"", ""text"": ""Licence"", ""title"": ""Licence"", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Literacy is a key skill and a key measure of a population’s education. In this topic page, we discuss historical trends, as well as recent developments in literacy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""From a historical perspective, literacy levels for the world population have risen drastically in the last couple of centuries. While only one in ten people in the world could read and write in 1820, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/cross-country-literacy-rates?country=~OWID_WRL"", ""children"": [{""text"": ""today, the share has reversed, with only one in ten remaining illiterate"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Despite large improvements in the expansion of basic education and the continuous reduction of education inequalities, there are substantial challenges ahead. The poorest countries in the world, where basic education is most likely to be a constraint for development, still have very large segments of the population who are illiterate."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""url"": ""#all-charts"", ""children"": [{""text"": ""See all interactive charts on literacy ↓"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""text"": [{""text"": ""Related topics"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1FbKVWqPddBZy0NcHzDG-BSoCsN5tJnok3RXcLo6R9ls/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Other research and writing on literacy on Our World in Data:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/children-not-in-school"", ""children"": [{""text"": ""Access to basic education: Almost 60 million children of primary school age are not in school"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/financing-education"", ""children"": [{""text"": ""Education spending"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/how-is-literacy-measured"", ""children"": [{""text"": ""How is literacy measured?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/better-learning"", ""children"": [{""text"": ""Millions of children learn only very little. How can the world provide a better education to the next generation?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Global literacy today"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Of the world population older than 15 years, the majority are literate. This interactive map shows how literacy rates vary around the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In many countries, more than 95% have basic literacy skills. 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In some countries in sub-Saharan Africa, fewer than 1-in-3 adults (aged over 15 years) are able to both read and write."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/cross-country-literacy-rates?tab=map&country=East%20Asia%20%26%20Pacific+Sub-Saharan%20Africa+Europe%20%26%20Central%20Asia+Latin%20America%20%26%20Caribbean+Middle%20East%20%26%20North%20Africa+South%20Asia"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Historical change in literacy"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Global literacy has grown substantially in the last two centuries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While the earliest forms of written communication date back to about 3,500-3,000 BCE, for centuries literacy remained a very restricted technology closely associated with the exercise of power. It was only during the Middle Ages that book production started growing, and literacy among the general population slowly started becoming important in the Western World."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While the ambition of universal literacy in Europe was a fundamental reform born from the "", ""spanType"": ""span-simple-text""}, {""id"": ""enlightenment"", ""children"": [{""text"": ""Enlightenment"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", it took centuries for it to happen. It was only in the 19th and 20th centuries that rates of literacy approached universality in early-industrialized countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization presents estimates of world literacy from the early nineteenth century until the current day. As we can see, literacy rates grew constantly but rather slowly until the beginning of the twentieth century. The rate of growth really climbed after the middle of the 20th century, when the expansion of basic education became a global priority. You can read more about the expansion of education systems around the world on our topic page on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/financing-education#historical-perspective"", ""children"": [{""text"": ""Financing Education"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/literate-and-illiterate-world-population?stackMode=relative"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""When did literacy start increasing in Europe?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization shows the spread of literacy in Europe since the 15th century, based on estimates from Buringh and Van Zanden (2009)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As can be seen, the rising levels of education in Europe foreshadowed the emergence of modern societies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Particularly fast improvements in literacy took place across Northwest Europe in the period 1600-1800. As we discuss below, widespread literacy is considered a legacy of the Age of Enlightenment."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/cross-country-literacy-rates?country=RUS~GBR~NLD~OWID_WRL~IND~SWE~NOR~DNK~FRA~DEU"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The ambition of universal literacy in Europe was a reform born of the Enlightenment"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The next chart shows historical estimates of literacy in England over the last five centuries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The historical estimates are based on the percentage of men and women who could sign documents, a very basic definition of literacy that is often used in historical research on education."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The first observations refer to men and women in the diocese of Norwich, which lies to the Northeast of London. Here, the majority of men (61%) were unable to write their names in the late 16th century; for women, it was much lower."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""By 1840, two-thirds of men and about half of women were literate in England. The expansion of education led to a reduction in education gender inequality. Towards the end of the 19th century, the share had increased to almost three-quarters for both genders."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As the center of the Industrial Revolution and one of the first countries that established democratic institutions, England was, in important aspects, the center of the development of modernity."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The data shows that improvements in literacy preceded the Industrial Revolution, and in many ways, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/life-expectancy?country=GBR"", ""children"": [{""text"": ""the rise of living standards"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" became only possible thanks to an increasingly better-educated public. Economic growth is possible when we better understand how to produce the things we need and translate these insights into technological improvements that allow us to produce them more efficiently. Both the development of new technologies (innovation) and their use in production relied on a much better-educated population."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/grapher/mean-years-of-schooling-long-run?tab=chart&country=GBR"", ""children"": [{""text"": ""Widespread school education"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and even basic skills like literacy are very recent achievements that were enabled and, at the same time, required by the progress achieved in recent generations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Literacy-in-the-UK-since-1580-1.png"", ""parseErrors"": []}, {""text"": [{""text"": ""In the US, the expansion of literacy helped reduce within-country inequalities"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The expansion of literacy in early-industrialized countries helped reduce within-country inequalities. In the preceding visualization, we showed that England virtually closed literacy gender gaps by 1900. Here, we provide evidence of literacy gaps across races in the US."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization shows illiteracy rates by race for the period 1870-1979. As we can see, in order to reach near-universal levels of literacy, the US had to close the race gap. This was eventually achieved around 1980."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Percentage of persons 14 years old and over in the US who were illiterate by race, 1870-1979 – Our World in Data, with data from NCES"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""percentage-literate-by-race-over-time.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Literacy and access to education have increased around the world"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The next visualization shows, in two panels, a side-by-side comparison of long-term trends in school attendance and literacy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We can see that in 1870, only one in four people in the world attended school, and this meant that only one in five were able to read. Global inequalities in access to education were very large."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Today, in contrast, the global estimates of literacy and school attendance are above 80%, and the inequality between world regions – while still existing – is much lower."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We can see that two centuries ago, only a small elite of the world population had the ability to read and write – the best estimate is that 12% of the world population was literate. Over the course of the 19th century, global literacy more than doubled. And over the course of the 20th century the world achieved rapid progress in education. More than 4 out of 5 people are now able to read. Younger generations are better educated than ever before."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Rising-Education-Around-the-World-School-and-Literacy.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Latin America has made huge improvements in literacy in the last century"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As pointed out above, Europe pioneered the expansion of basic education – but global literacy rates only started really climbing in the second half of the 20th century, when the expansion of basic education became a global priority. Here, we present evidence of important recent achievements in Latin America, where literacy has dramatically increased in the past century."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As can be seen, many nations have gained 40-50 percentage points in literacy during this period."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Despite these improvements, however, there is still a wide disparity between nations. Here, you can see that, at the turn of the 21st century, half of the population in poor countries such as Haiti remains illiterate. This motivates the next visualization, where we discuss cross-country heterogeneity in more detail."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/cross-country-literacy-rates?time=1960..latest&country=ARG~BOL~BRA~COL~PRY~URY~PER"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Literacy around the world today"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Literacy by generation"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To assess the extent to which progress can be expected in the years to come, it is convenient to break down literacy estimates by age groups. The following map, using data from UNESCO, shows such estimates for most countries in the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As can be seen, in the majority of nations, there is a large difference in literacy rates across generations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These large differences across generations point to a global trend: the high literacy rate among the youth indicates that as time passes, the literacy rate for the overall population will continue to increase."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""World maps of the literacy rate by age group"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Literacy-Rate-by-Generation-World-Map.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Northern Africa and the Middle East have drastically improved literacy in just one generation"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We highlighted above the fact that most low and middle-income countries feature large differences in literacy rates across generations. The visualization shows specifically how remarkably large these differences are in Northern Africa and the Middle East. Using UNESCO data, these maps show that in many countries in these regions, only less than a third of the older generation is literate – while in contrast, more than 90% of the younger generation is literate."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Literacy-Rate-by-Age-in-Middle-East-and-Northern-Africa.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The scatter plot emphasizes the point already made. As you can see, younger generations are more likely to be literate than older generations around the world. In some countries, the gaps are dramatic. For example, countries in the bottom right of the chart where the youth literacy rate may be over 90% but the elderly literacy rate is below 40%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart you can use the slider at the bottom to check how these generational gaps have changed in recent decades. You can see that "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/literacy-rates-of-younger-vs-older-population?endpointsOnly=1&time=1970..latest&country=BFA~CAF~CMR~EGY~SWZ~MLI~RWA~BEN~BDI~GNB~COM~MOZ~ZAF~STP~MAR~ZWE~COG~LBR~LBY~TUN~DZA~MWI~SYC~CIV~SEN~TZA~CPV~MUS~ZMB~BWA~NAM~NGA~UGA~TCD~GAB~ETH~GIN~GNQ~GMB~GHA~KEN~LSO~MDG~MRT~SDN~TGO~AGO~COD~NER~ERI~SLE~SSD"", ""children"": [{""text"": ""throughout Africa, the changes have been mainly horizontal"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (i.e., gaps have been widening as there have been radical recent improvements specifically benefiting the younger population). This is in contrast to richer regions, such as Europe, where the expansion of education started earlier, and as a consequence, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/literacy-rates-of-younger-vs-older-population?endpointsOnly=1&time=1970..2015&country=PRT~POL~HUN~GRC~ITA~MNE~ESP~MLT~BLR~EST~LVA~LTU~MDA~RUS~BIH~HRV~SVN~CYP~ROU~MKD~ALB~BGR~UKR~SRB"", ""children"": [{""text"": ""changes have been mainly vertical"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/literacy-rates-of-the-the-younger-population-15-24-years-versus-literacy-rates-of-the-older-population-65"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Literacy rates by sex"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The visualization shows the literacy for rates for young men and women. In countries above the diagonal dashed line, literacy rates are higher for young men than for young women; this is the case for many poorer countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/grapher/literacy-rate-of-young-men-and-women-line"", ""children"": [{""text"": ""This chart"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" shows the literacy rate by sex over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/grapher/the-gender-parity-index-gpi-for-the-youth-literacy-rate"", ""children"": [{""text"": ""This visualization"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" shows the ratio of the literacy rate between young women and men around the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/literacy-rate-of-young-men-and-women"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Numeracy"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Numeracy skills over the long run"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Numeracy is the ability to understand and work with numbers. The visualization shows how this ability became more common in populations around the world based on a very basic definition of numeracy, the ability to state one's own age correctly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-the-population-with-basic-numeracy-skills-by-birth-decade"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Numeracy skills today"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Compared to the data on literacy we have less information on numeracy skills in the world today. Some information comes from PIAAC, the OECD's survey of the skills of adults."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The scatter plot shows how adults in OECD countries scored in the literacy and numeracy dimension. We see that the two aspects are closely correlated; those countries that have high literacy also have high numeracy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""PIAAC is only available for the recent past, but it can still give us some insights into how numeracy skills in the world have changed. If we compare the numeracy scores of the young cohort with the older cohort "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/numeracy-skills-of-adults-young-adults-and-adults"", ""children"": [{""text"": ""in a scatterplot"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", we find that in most countries, numeracy skills have recently increased."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/numeracy-vs-literacy-skills-of-adults"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Definitions & Measurement"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Measurement today"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Common methods and data sources"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, we present a breakdown of UNESCO literacy estimates, showing the main methodologies used and how these have changed over time. (To explore changes across time, use the slider underneath the map.)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The breakdown covers four categories: self-reported literacy declared directly by individuals, self-reported literacy declared by the head of the household, tested literacy from proficiency examinations, and indirect estimation or extrapolation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In most cases, the categories covering 'self-reports' correspond to estimates of literacy that rely on answers provided to a simple yes/no question asking people if they can read and write."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The category 'indirect estimates ' corresponds mainly to estimates that rely on indirect evidence from educational attainment, usually based on the highest degree of completed education."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/uploads/2018/03/Literacy-measurement-OWID-metadata.xlsx"", ""children"": [{""text"": ""this table"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", you will find details regarding all literacy definitions and sources, country by country, and how we categorized them for the purpose of this chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart tells us that:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""There is substantial cross-country variation, with recent estimates covering all four measurement methods."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There is variation within countries across time (e.g., Mexico switches between self-reports and extrapolation)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/mode-of-reporting-literacy-rates"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another way to dissect the same data is to classify literacy estimates according to the type of measurement instrument used to collect the relevant data. In the next chart, we explore this, splitting estimates into three categories: sampling, including data from literacy tests, and household surveys; census data; and other instruments (e.g., administrative data on school enrollment)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Here, we can see that most countries use sampling instruments (coded as 'Survey' on the map), although in the past census data was more common. Literacy surveys have the potential of being more accurate – when the sampling is done correctly – because they allow for more specific and detailed measurement than short and generic questions in population censuses."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/literacy-rate-source-type"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Data quality: Challenges and limitations"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As mentioned above, recent data on literacy is often based on a single question included in national population censuses, or household surveys presented to respondents above a certain age, where literacy skills are self-reported. The question is often phrased as \""Can you read and write?\"". These self-reports of literacy skills have several limitations:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""Simple questions such as \""Can you read and write?\"" frame literacy as a skill you either possess or do not when, in reality, literacy is a multi-dimensional skill that exists on a continuum."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Self-reports are subjective in that the question depends on what each individual understands by \""reading\"" and \""writing\"". The form of a word may be familiar enough for a respondent to recall its sound or meaning without actually ‘reading’ it. Similarly, when writing out one’s name to convey written ability, this can be accomplished by ‘drawing’ a familiar shape rather than writing in an effort to produce a written text with meaning."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In many cases, surveys ask only one individual to report literacy on behalf of the entire household. This indirect reporting potentially introduces further noise, in particular when it comes to estimating literacy among women and children since these groups are less often considered 'heads of household' in the surveys."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Similarly, inferring literacy from data on educational attainment is also problematic since schooling does not produce literacy in the same way everywhere: Proficiency tests show that in many low-income countries, a "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/students-in-grade-2-who-cant-read-a-single-word-ca-2015"", ""children"": [{""text"": ""large fraction"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of second-grade primary school students cannot read a single word of a short text, and for very few people in these countries going to school for four or five years "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.cgdev.org/blog/measuring-quality-girls-education-across-developing-world"", ""children"": [{""text"": ""guarantees basic literacy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Even at a conceptual level, there is a lack of consensus – national definitions of literacy that are based on educational attainment vary substantially from country to country. For example, in Greece, people are considered literate if they have finished six years of primary education, while in Paraguay, you qualify as literate if you have completed two years of primary school."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""Reconstructing estimates from the past"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Trends over time"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The correlation between educational attainment and literacy holds across countries and over time. The next chart shows this by plotting changes in literacy rates and average years of schooling."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Each country in this chart is represented by a line, where the beginning and end points correspond to the first and last available observation of these two variables over the period of available data, which varies from country to country. Before 1990, almost all observations correspond to census data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we can see by the arrows pointing to the top-right corner, literacy rates tend to be much higher in countries where people tend to have more years of education. As average years of education go up in a country, literacy rates also increase."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""/grapher/literacy-rates-vs-average-years-of-schooling?time=earliest..latest"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Countries with high literacy rates also tend to have higher results in the basic literacy test included in the DHS surveys (this test requires survey respondents to read a sentence shown to them). As we can see in the chart, but these two variables are closely related."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/literacy-rate-adult-total-dhs-surveys-vs-unesco"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Other historical sources"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Other historical sources used to estimate literacy is to calculate the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/historical-literacy-in-england-by-sex"", ""children"": [{""text"": ""share of people who could sign official documents"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (e.g., court documents, marriage certificates, etc.)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As the researcher Jeremiah Dittmar "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.jeremiahdittmar.com/files/Print-Welfare-Paper-5.27.2011.pdf"", ""children"": [{""text"": ""explains"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", this approach only gives a lower bound of the estimates because the number of people who could read was higher than the number who could write."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Indeed, other methods have been proposed in order to rely on historical estimates of people who could read. For example, researchers Eltjo Buringh and Jan Luiten van Zanden deduce "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/estimated-historical-literacy-rates?country=GBR+BEL+FRA+DEU+IRL+ITA+NLD+POL+ESP+SWE+Western%20Europe"", ""children"": [{""text"": ""literacy rates from estimated per capita book consumption"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". "", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" As Buringh and Van Zanden show, their estimates based on book consumption are different but still fairly close to alternative estimates based on signed documents."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""top"": [], ""type"": ""all-charts"", ""heading"": ""Interactive charts on literacy"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""0282ad00d3f09f6678b0a521bc1157c083d3c990"": {""id"": ""0282ad00d3f09f6678b0a521bc1157c083d3c990"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Buringh, E., & Van Zanden, J. L. (2009). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=e2d27ff15e4bf635829316f6d934e80e696ef3d7"", ""children"": [{""text"": ""Charting the “Rise of the West”: Manuscripts and Printed Books in Europe, A long-term perspective from the sixth through eighteenth centuries"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The Journal of Economic History, 69(02), 409-445."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""287c09af415e038ab1991032395da3ea881fd166"": {""id"": ""287c09af415e038ab1991032395da3ea881fd166"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""You will find more details about this in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.unesco.org/gem-report/en/literacy-life"", ""children"": [{""text"": ""Chapter 6 of the Education for All Global Monitoring Report (2006)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""43aacc369248cb44daae7ed9fa80b58ed479702d"": {""id"": ""43aacc369248cb44daae7ed9fa80b58ed479702d"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The graph is based on Clark (2008), who, in turn, relies on the sources indicated in the chart. Gregory Clark (2008). A farewell to alms: a brief economic history of the world. Princeton University Press."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""4691a11e1e6b5f0f781e72b64c3495270331d22d"": {""id"": ""4691a11e1e6b5f0f781e72b64c3495270331d22d"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Since estimates of signed documents tend to rely on small samples (e.g., parish documents from specific towns), researchers often rely on additional assumptions to extrapolate estimates to the national level. For example, Bob Allen provides estimates of the evolution of literacy in Europe between 1500 and 1800 using data on urbanization rates. For more details, see Allen, R. C. (2003). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20190122001259/http://www.econ.queensu.ca/files/other/allen03.pdf"", ""children"": [{""text"": ""Progress and poverty in early modern Europe"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The Economic History Review, 56(3), 403-443."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a008a94dcc7daf2184b9188f05bd7d79f7c4da75"": {""id"": ""a008a94dcc7daf2184b9188f05bd7d79f7c4da75"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The data is taken from the "", ""spanType"": ""span-simple-text""}, {""url"": ""http://stats.uis.unesco.org/unesco/TableViewer/tableView.aspx?ReportId=210"", ""children"": [{""text"": ""UNESCO statistics"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The data refers to both genders and to the latest available data in the time between 2000 and 2012."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b827d83f0b3b7ff66d0d9fdf85ff3a510827a760"": {""id"": ""b827d83f0b3b7ff66d0d9fdf85ff3a510827a760"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""They use a demand equation that links book consumption to a number of factors, including literacy and book prices. For more details, see Buringh, E. and Van Zanden, J.L., 2009. Charting the “Rise of the West”: Manuscripts and Printed Books in Europe, a long-term Perspective from the Sixth through Eighteenth Centuries. The Journal of Economic History, 69(2), pp.409-445."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e25aa5c768a7fc14614aa4760ecd041701fc41bd"": {""id"": ""e25aa5c768a7fc14614aa4760ecd041701fc41bd"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""An overview of the academic literature on the historical origins and spread of literacy can be found in Easton, P. (2014). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://unesdoc.unesco.org/ark:/48223/pf0000225258"", ""children"": [{""text"": ""Sustaining Literacy in Africa: Developing a Literate Environment"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". United Nations Educational, Scientific and Cultural Organization. Paris, France."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ef54e0889c9c293f178b6ef7ae982d9c203f4cc9"": {""id"": ""ef54e0889c9c293f178b6ef7ae982d9c203f4cc9"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""url"": ""http://nces.ed.gov/naal/lit_history.asp"", ""children"": [{""text"": ""National Center for Education Statistics"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". From the original source, we have excluded some years to have equal time differences on the x-axis (and interpolated the values for 1950), but the data is shown at the linked source."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""linear-topic-page"", ""title"": ""Literacy"", ""authors"": [""Max Roser"", ""Esteban Ortiz-Ospina""], ""excerpt"": ""Being able to read and write opens up the world of education and knowledge.\nWhen and why did more people become literate? How can progress continue?"", ""dateline"": ""This article was first published in 2013. The last revisions were done in March 2024."", ""subtitle"": ""Being able to read and write opens up the world of education and knowledge.\nWhen and why did more people become literate? How can progress continue?"", ""sidebar-toc"": true, ""featured-image"": ""literacy-thumbnail.png""}",1,2023-11-10 14:55:56,2018-09-20 13:59:11,2024-03-06 12:03:15,unlisted,ALBJ4Lvk0BxIwW6ifuNKBxbcrflNZqkaTW06BIsSB9iMY4cB05Z5AewE2caRb30c07HWiTSxM-WyJZRxNtKh7A,,"Literacy is a key skill and a key measure of a population’s education. In this topic page, we discuss historical trends, as well as recent developments in literacy. From a historical perspective, literacy levels for the world population have risen drastically in the last couple of centuries. While only one in ten people in the world could read and write in 1820, [today, the share has reversed, with only one in ten remaining illiterate](https://ourworldindata.org/grapher/cross-country-literacy-rates?country=~OWID_WRL). Despite large improvements in the expansion of basic education and the continuous reduction of education inequalities, there are substantial challenges ahead. The poorest countries in the world, where basic education is most likely to be a constraint for development, still have very large segments of the population who are illiterate. **[See all interactive charts on literacy ↓](#all-charts)** ### Related topics ### undefined undefined https://docs.google.com/document/d/1FbKVWqPddBZy0NcHzDG-BSoCsN5tJnok3RXcLo6R9ls/edit Other research and writing on literacy on Our World in Data: * [Access to basic education: Almost 60 million children of primary school age are not in school](https://ourworldindata.org/children-not-in-school) * [Education spending](https://ourworldindata.org/financing-education) * [How is literacy measured?](https://ourworldindata.org/how-is-literacy-measured) * [Millions of children learn only very little. How can the world provide a better education to the next generation?](https://ourworldindata.org/better-learning) # Global literacy today Of the world population older than 15 years, the majority are literate. This interactive map shows how literacy rates vary around the world. In many countries, more than 95% have basic literacy skills. Literacy skills of the majority of the population are a modern achievement as we show below. Globally, however, large inequalities remain, notably between some sub-Saharan Africa and the rest of the world. In some countries in sub-Saharan Africa, fewer than 1-in-3 adults (aged over 15 years) are able to both read and write. # Historical change in literacy ## Global literacy has grown substantially in the last two centuries While the earliest forms of written communication date back to about 3,500-3,000 BCE, for centuries literacy remained a very restricted technology closely associated with the exercise of power. It was only during the Middle Ages that book production started growing, and literacy among the general population slowly started becoming important in the Western World.1 While the ambition of universal literacy in Europe was a fundamental reform born from the Enlightenment, it took centuries for it to happen. It was only in the 19th and 20th centuries that rates of literacy approached universality in early-industrialized countries. The following visualization presents estimates of world literacy from the early nineteenth century until the current day. As we can see, literacy rates grew constantly but rather slowly until the beginning of the twentieth century. The rate of growth really climbed after the middle of the 20th century, when the expansion of basic education became a global priority. You can read more about the expansion of education systems around the world on our topic page on [Financing Education](https://ourworldindata.org/financing-education#historical-perspective). ## When did literacy start increasing in Europe? The following visualization shows the spread of literacy in Europe since the 15th century, based on estimates from Buringh and Van Zanden (2009).2 As can be seen, the rising levels of education in Europe foreshadowed the emergence of modern societies. Particularly fast improvements in literacy took place across Northwest Europe in the period 1600-1800. As we discuss below, widespread literacy is considered a legacy of the Age of Enlightenment. ## The ambition of universal literacy in Europe was a reform born of the Enlightenment The next chart shows historical estimates of literacy in England over the last five centuries. The historical estimates are based on the percentage of men and women who could sign documents, a very basic definition of literacy that is often used in historical research on education.3 The first observations refer to men and women in the diocese of Norwich, which lies to the Northeast of London. Here, the majority of men (61%) were unable to write their names in the late 16th century; for women, it was much lower. By 1840, two-thirds of men and about half of women were literate in England. The expansion of education led to a reduction in education gender inequality. Towards the end of the 19th century, the share had increased to almost three-quarters for both genders. As the center of the Industrial Revolution and one of the first countries that established democratic institutions, England was, in important aspects, the center of the development of modernity. The data shows that improvements in literacy preceded the Industrial Revolution, and in many ways, [the rise of living standards](https://ourworldindata.org/grapher/life-expectancy?country=GBR) became only possible thanks to an increasingly better-educated public. Economic growth is possible when we better understand how to produce the things we need and translate these insights into technological improvements that allow us to produce them more efficiently. Both the development of new technologies (innovation) and their use in production relied on a much better-educated population. [Widespread school education](https://ourworldindata.org/grapher/mean-years-of-schooling-long-run?tab=chart&country=GBR) and even basic skills like literacy are very recent achievements that were enabled and, at the same time, required by the progress achieved in recent generations. ## In the US, the expansion of literacy helped reduce within-country inequalities The expansion of literacy in early-industrialized countries helped reduce within-country inequalities. In the preceding visualization, we showed that England virtually closed literacy gender gaps by 1900. Here, we provide evidence of literacy gaps across races in the US. The following visualization shows illiteracy rates by race for the period 1870-1979. As we can see, in order to reach near-universal levels of literacy, the US had to close the race gap. This was eventually achieved around 1980. ## Literacy and access to education have increased around the world The next visualization shows, in two panels, a side-by-side comparison of long-term trends in school attendance and literacy. We can see that in 1870, only one in four people in the world attended school, and this meant that only one in five were able to read. Global inequalities in access to education were very large. Today, in contrast, the global estimates of literacy and school attendance are above 80%, and the inequality between world regions – while still existing – is much lower. We can see that two centuries ago, only a small elite of the world population had the ability to read and write – the best estimate is that 12% of the world population was literate. Over the course of the 19th century, global literacy more than doubled. And over the course of the 20th century the world achieved rapid progress in education. More than 4 out of 5 people are now able to read. Younger generations are better educated than ever before. ## Latin America has made huge improvements in literacy in the last century As pointed out above, Europe pioneered the expansion of basic education – but global literacy rates only started really climbing in the second half of the 20th century, when the expansion of basic education became a global priority. Here, we present evidence of important recent achievements in Latin America, where literacy has dramatically increased in the past century. As can be seen, many nations have gained 40-50 percentage points in literacy during this period. Despite these improvements, however, there is still a wide disparity between nations. Here, you can see that, at the turn of the 21st century, half of the population in poor countries such as Haiti remains illiterate. This motivates the next visualization, where we discuss cross-country heterogeneity in more detail. # Literacy around the world today ## Literacy by generation To assess the extent to which progress can be expected in the years to come, it is convenient to break down literacy estimates by age groups. The following map, using data from UNESCO, shows such estimates for most countries in the world. As can be seen, in the majority of nations, there is a large difference in literacy rates across generations. These large differences across generations point to a global trend: the high literacy rate among the youth indicates that as time passes, the literacy rate for the overall population will continue to increase. ### Northern Africa and the Middle East have drastically improved literacy in just one generation We highlighted above the fact that most low and middle-income countries feature large differences in literacy rates across generations. The visualization shows specifically how remarkably large these differences are in Northern Africa and the Middle East. Using UNESCO data, these maps show that in many countries in these regions, only less than a third of the older generation is literate – while in contrast, more than 90% of the younger generation is literate. The scatter plot emphasizes the point already made. As you can see, younger generations are more likely to be literate than older generations around the world. In some countries, the gaps are dramatic. For example, countries in the bottom right of the chart where the youth literacy rate may be over 90% but the elderly literacy rate is below 40%. In the chart you can use the slider at the bottom to check how these generational gaps have changed in recent decades. You can see that [throughout Africa, the changes have been mainly horizontal](https://ourworldindata.org/grapher/literacy-rates-of-younger-vs-older-population?endpointsOnly=1&time=1970..latest&country=BFA~CAF~CMR~EGY~SWZ~MLI~RWA~BEN~BDI~GNB~COM~MOZ~ZAF~STP~MAR~ZWE~COG~LBR~LBY~TUN~DZA~MWI~SYC~CIV~SEN~TZA~CPV~MUS~ZMB~BWA~NAM~NGA~UGA~TCD~GAB~ETH~GIN~GNQ~GMB~GHA~KEN~LSO~MDG~MRT~SDN~TGO~AGO~COD~NER~ERI~SLE~SSD) (i.e., gaps have been widening as there have been radical recent improvements specifically benefiting the younger population). This is in contrast to richer regions, such as Europe, where the expansion of education started earlier, and as a consequence, [changes have been mainly vertical](https://ourworldindata.org/grapher/literacy-rates-of-younger-vs-older-population?endpointsOnly=1&time=1970..2015&country=PRT~POL~HUN~GRC~ITA~MNE~ESP~MLT~BLR~EST~LVA~LTU~MDA~RUS~BIH~HRV~SVN~CYP~ROU~MKD~ALB~BGR~UKR~SRB). ## Literacy rates by sex The visualization shows the literacy for rates for young men and women. In countries above the diagonal dashed line, literacy rates are higher for young men than for young women; this is the case for many poorer countries. [This chart](https://ourworldindata.org/grapher/literacy-rate-of-young-men-and-women-line) shows the literacy rate by sex over time. [This visualization](https://ourworldindata.org/grapher/the-gender-parity-index-gpi-for-the-youth-literacy-rate) shows the ratio of the literacy rate between young women and men around the world. # Numeracy ## Numeracy skills over the long run Numeracy is the ability to understand and work with numbers. The visualization shows how this ability became more common in populations around the world based on a very basic definition of numeracy, the ability to state one's own age correctly. ## Numeracy skills today Compared to the data on literacy we have less information on numeracy skills in the world today. Some information comes from PIAAC, the OECD's survey of the skills of adults. The scatter plot shows how adults in OECD countries scored in the literacy and numeracy dimension. We see that the two aspects are closely correlated; those countries that have high literacy also have high numeracy. PIAAC is only available for the recent past, but it can still give us some insights into how numeracy skills in the world have changed. If we compare the numeracy scores of the young cohort with the older cohort [in a scatterplot](https://ourworldindata.org/grapher/numeracy-skills-of-adults-young-adults-and-adults), we find that in most countries, numeracy skills have recently increased. # Definitions & Measurement ## Measurement today ### Common methods and data sources In the chart, we present a breakdown of UNESCO literacy estimates, showing the main methodologies used and how these have changed over time. (To explore changes across time, use the slider underneath the map.) The breakdown covers four categories: self-reported literacy declared directly by individuals, self-reported literacy declared by the head of the household, tested literacy from proficiency examinations, and indirect estimation or extrapolation. In most cases, the categories covering 'self-reports' correspond to estimates of literacy that rely on answers provided to a simple yes/no question asking people if they can read and write. The category 'indirect estimates ' corresponds mainly to estimates that rely on indirect evidence from educational attainment, usually based on the highest degree of completed education. In [this table](https://ourworldindata.org/uploads/2018/03/Literacy-measurement-OWID-metadata.xlsx), you will find details regarding all literacy definitions and sources, country by country, and how we categorized them for the purpose of this chart. This chart tells us that: * There is substantial cross-country variation, with recent estimates covering all four measurement methods. * There is variation within countries across time (e.g., Mexico switches between self-reports and extrapolation). Another way to dissect the same data is to classify literacy estimates according to the type of measurement instrument used to collect the relevant data. In the next chart, we explore this, splitting estimates into three categories: sampling, including data from literacy tests, and household surveys; census data; and other instruments (e.g., administrative data on school enrollment). Here, we can see that most countries use sampling instruments (coded as 'Survey' on the map), although in the past census data was more common. Literacy surveys have the potential of being more accurate – when the sampling is done correctly – because they allow for more specific and detailed measurement than short and generic questions in population censuses. ### Data quality: Challenges and limitations As mentioned above, recent data on literacy is often based on a single question included in national population censuses, or household surveys presented to respondents above a certain age, where literacy skills are self-reported. The question is often phrased as ""Can you read and write?"". These self-reports of literacy skills have several limitations: * Simple questions such as ""Can you read and write?"" frame literacy as a skill you either possess or do not when, in reality, literacy is a multi-dimensional skill that exists on a continuum. * Self-reports are subjective in that the question depends on what each individual understands by ""reading"" and ""writing"". The form of a word may be familiar enough for a respondent to recall its sound or meaning without actually ‘reading’ it. Similarly, when writing out one’s name to convey written ability, this can be accomplished by ‘drawing’ a familiar shape rather than writing in an effort to produce a written text with meaning. * In many cases, surveys ask only one individual to report literacy on behalf of the entire household. This indirect reporting potentially introduces further noise, in particular when it comes to estimating literacy among women and children since these groups are less often considered 'heads of household' in the surveys. Similarly, inferring literacy from data on educational attainment is also problematic since schooling does not produce literacy in the same way everywhere: Proficiency tests show that in many low-income countries, a [large fraction](https://ourworldindata.org/grapher/students-in-grade-2-who-cant-read-a-single-word-ca-2015) of second-grade primary school students cannot read a single word of a short text, and for very few people in these countries going to school for four or five years [guarantees basic literacy](https://www.cgdev.org/blog/measuring-quality-girls-education-across-developing-world). Even at a conceptual level, there is a lack of consensus – national definitions of literacy that are based on educational attainment vary substantially from country to country. For example, in Greece, people are considered literate if they have finished six years of primary education, while in Paraguay, you qualify as literate if you have completed two years of primary school.6 ## Reconstructing estimates from the past ### Trends over time The correlation between educational attainment and literacy holds across countries and over time. The next chart shows this by plotting changes in literacy rates and average years of schooling. Each country in this chart is represented by a line, where the beginning and end points correspond to the first and last available observation of these two variables over the period of available data, which varies from country to country. Before 1990, almost all observations correspond to census data. As we can see by the arrows pointing to the top-right corner, literacy rates tend to be much higher in countries where people tend to have more years of education. As average years of education go up in a country, literacy rates also increase. Countries with high literacy rates also tend to have higher results in the basic literacy test included in the DHS surveys (this test requires survey respondents to read a sentence shown to them). As we can see in the chart, but these two variables are closely related. ### Other historical sources Other historical sources used to estimate literacy is to calculate the [share of people who could sign official documents](https://ourworldindata.org/grapher/historical-literacy-in-england-by-sex) (e.g., court documents, marriage certificates, etc.).7 As the researcher Jeremiah Dittmar [explains](http://www.jeremiahdittmar.com/files/Print-Welfare-Paper-5.27.2011.pdf), this approach only gives a lower bound of the estimates because the number of people who could read was higher than the number who could write. Indeed, other methods have been proposed in order to rely on historical estimates of people who could read. For example, researchers Eltjo Buringh and Jan Luiten van Zanden deduce [literacy rates from estimated per capita book consumption](https://ourworldindata.org/grapher/estimated-historical-literacy-rates?country=GBR+BEL+FRA+DEU+IRL+ITA+NLD+POL+ESP+SWE+Western%20Europe). 8 As Buringh and Van Zanden show, their estimates based on book consumption are different but still fairly close to alternative estimates based on signed documents. An overview of the academic literature on the historical origins and spread of literacy can be found in Easton, P. (2014). [Sustaining Literacy in Africa: Developing a Literate Environment](https://unesdoc.unesco.org/ark:/48223/pf0000225258). United Nations Educational, Scientific and Cultural Organization. Paris, France. Buringh, E., & Van Zanden, J. L. (2009). [Charting the “Rise of the West”: Manuscripts and Printed Books in Europe, A long-term perspective from the sixth through eighteenth centuries](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=e2d27ff15e4bf635829316f6d934e80e696ef3d7). The Journal of Economic History, 69(02), 409-445. The graph is based on Clark (2008), who, in turn, relies on the sources indicated in the chart. Gregory Clark (2008). A farewell to alms: a brief economic history of the world. Princeton University Press. [National Center for Education Statistics](http://nces.ed.gov/naal/lit_history.asp). From the original source, we have excluded some years to have equal time differences on the x-axis (and interpolated the values for 1950), but the data is shown at the linked source. The data is taken from the [UNESCO statistics](http://stats.uis.unesco.org/unesco/TableViewer/tableView.aspx?ReportId=210). The data refers to both genders and to the latest available data in the time between 2000 and 2012. You will find more details about this in [Chapter 6 of the Education for All Global Monitoring Report (2006)](https://www.unesco.org/gem-report/en/literacy-life). Since estimates of signed documents tend to rely on small samples (e.g., parish documents from specific towns), researchers often rely on additional assumptions to extrapolate estimates to the national level. For example, Bob Allen provides estimates of the evolution of literacy in Europe between 1500 and 1800 using data on urbanization rates. For more details, see Allen, R. C. (2003). [Progress and poverty in early modern Europe](https://web.archive.org/web/20190122001259/http://www.econ.queensu.ca/files/other/allen03.pdf). The Economic History Review, 56(3), 403-443. They use a demand equation that links book consumption to a number of factors, including literacy and book prices. For more details, see Buringh, E. and Van Zanden, J.L., 2009. Charting the “Rise of the West”: Manuscripts and Printed Books in Europe, a long-term Perspective from the Sixth through Eighteenth Centuries. The Journal of Economic History, 69(2), pp.409-445.",Literacy 1xTrEatdbSxXE7TGB1wuH99PIj0fZNRSCNRiZ1-TmbNs,weather-forecasts,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Weather forecasts are often seen as just a nice thing to have. Useful when planning a Sunday barbecue, or when we want to know if we’ll need an umbrella for the day. But in many ways weather forecasts are absolutely crucial: they can be a matter of life and death."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Accurate forecasts can save lives by giving early warnings of storms, heat waves, and disasters. Farmers use them for agricultural management, which can make the difference between a lost harvest or a harvest of plenty. Grid operators rely on accurate forecasts of temperatures for heating and cooling demand, and how much energy they’ll get from wind and solar farms. Pilots and sailors need them to carry people across oceans safely. Accurate information about future weather is often absolutely vital."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this article, I look at improvements over time and the global inequalities that need to be closed to protect lives and livelihoods around the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Weather forecasts have improved a lot"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Weather forecasting has come a long way. In 650 B.C. the Babylonians would try to predict weather patterns based on cloud patterns and movements. Three centuries later, Aristotle wrote "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Meteorologica"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", discussing how phenomena such as rain, hail, hurricanes, and lightning formed. Much of it turned out to be wrong, but it represents one of the first attempts to explain how the weather works in detail."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It wasn’t until 1859 that the UK’s Meteorological Service (the Met Office) "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.metoffice.gov.uk/about-us/who/our-history"", ""children"": [{""text"": ""issued its first"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" weather forecast for shipping. Two years later, it broadcasted its first public weather forecast. While meteorological measurements improved over time, the massive step-change in predictions came with the use of computerized numerical modeling. This didn’t start until a century later, in the 1960s."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Forecasts have improved a lot since then. We can see this across a range of measurements, and different national meteorological organizations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Met Office "", ""spanType"": ""span-simple-text""}, {""url"": ""https://blog.metoffice.gov.uk/2023/11/15/why-an-exact-date-weather-forecast-headline-isnt-what-it-seems/#:~:text=Despite%20the%20certainty%20suggested%20in,come%20with%20in%2Dbuilt%20uncertainty."", ""children"": [{""text"": ""says"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" its four-day forecasts are now as accurate as its one-day forecasts were 30 years ago."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Predictions have gotten much better in the United States, too. We can see this in some of the most important forecasts: the prediction of hurricanes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The National Hurricane Center "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.nhc.noaa.gov/verification/verify5.shtml"", ""children"": [{""text"": ""publishes data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" on the “track error” of hurricanes and cyclones — the error in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""where"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" the hurricane hits. This is shown in the chart below, from the 1960s onwards."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Each line represents the error of forecasts for different time periods in advance. For example, 12 hours before it hits, all the way up to 120 hours (or 5 days) before."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We can see that this track error — especially for longer-term forecasts — has decreased a lot over time. In the 1970s, a 48-hour forecast had an error between 200 and 400 nautical miles; today this is around 50 nautical miles."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/hurricane-track-error"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We can show the same data another way. In the chart below, each line represents the average error for each decade. On the horizontal axis we have the forecast period, again extending from 0 to 120 hours."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The 72-hour error in the 1960s and 70s was over 400 nautical miles. Today, it’s less than 80 miles."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Meteorologists can now make pretty accurate predictions of where a hurricane will hit three or four days in advance, which lets cities and communities prepare while preventing unnecessary evacuations that might have been implemented in the past."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""hurricane-forecasting-error.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""url"": ""https://charts.ecmwf.int/"", ""children"": [{""text"": ""European Centre for Medium-Range Weather Forecasts"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (ECMWF) produces global numerical weather models. While national weather agencies use much higher-resolution processing to get local forecasts, these global models provide a crucial input into these systems."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The ECMWF publishes analyses of its errors over time. This is shown in the chart below."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It shows the difference between the forecast and the actual weather outcome for forecasts 3, 5, 7, and 10 days in advance. The metric used here is the “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://climate.copernicus.eu/esotc/2022/atmospheric-circulation"", ""children"": [{""text"": ""500 hPa geopotential height"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""”, a commonly used meteorological measure of air pressure (which dictates weather patterns)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The solid line is for the Northern Hemisphere, and the dashed line is for the Southern."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Three-day forecasts — shown in blue — have been pretty accurate since the 1980s, and have still gotten a lot better over time. Today the accuracy is around 97%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The biggest improvements we’ve seen are for longer timeframes. By the early 2000s, 5-day forecasts were “highly accurate” and 7-day forecasts are reaching that threshold today. 10-day forecasts aren’t quite there yet but are getting better."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""improved-weather-forecasting.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Why have weather forecasts improved?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A few key developments "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.science.org/doi/10.1126/science.aav7274"", ""children"": [{""text"": ""explain these improvements"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The first big change is that the data has improved. More extensive and higher-resolution observations can be used as inputs into the weather models. This is because we have more and better satellite data, and because land-based stations are covering many more areas around the globe, and at a higher density. The precision of these instruments has improved, too."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These observations are then fed into numerical prediction models to forecast the weather. That brings us to the next two developments. The computers on which these models are run have gotten much faster. Faster speeds are crucial: the Met Office now chunks the world into grids of smaller and smaller squares. While they once modeled the world in 90-kilometer-wide squares, they are now down to a grid of 1.5-kilometer squares. That means many more calculations need to be run to get this high-resolution map. The methods to turn the observations into model outputs have also improved. We’ve gone from very simple visions of the world to methods that can capture the complexity of these systems in detail."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The final crucial factor is how these forecasts are communicated. Not long ago, you could only get daily updates in the daily newspaper. With the rise of radio and TV, you could get a few notices per day. Now, we can get minute-by-minute updates online or on our smartphones."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Low-income countries have much worse forecasts, and often no early warning systems"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At home in Scotland, I can open an app on my phone and get a pretty accurate 5-day forecast within seconds. Unfortunately, this quality of information isn’t available to everyone. There are large differences in weather forecasts across the world, with a large gap between rich and poor."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As the researchers Manuel Linsenmeier and Jeffrey Shrader report in a "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ideas.repec.org/p/osf/socarx/7e2jf.html"", ""children"": [{""text"": ""recent paper"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", a 7-day forecast in a rich country can be more accurate than a one-day forecast in some low-income ones."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While national forecasts have improved over time across all income levels, the quality gap today is almost as wide as it was in the 1980s."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are a few reasons for this. First, far fewer land-based instruments and radiosondes measure meteorological data in poorer countries. Second, the frequency of reporting is much lower."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is unsurprising when we look at the amount of money spent on weather and climate information. In "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.science.org/doi/10.1126/sciadv.1602632"", ""children"": [{""text"": ""a paper"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" published in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Science"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", Lucian Georgeson, Mark Maslin, and Martyn Poessinouw looked at differences in spending across income groups."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This includes private and public spending on commercial products that fall within the definition of “weather and climate information services”."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is shown as the spending "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""per person"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", and the spending as a share of gross domestic product (GDP) in the chart below."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Low-income countries spend 15 to 20 times less per person than high-income countries. But given the size of their economies, they actually spend "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""more"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" as a share of GDP."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/weather-forecast-spending"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This gap is a problem. 60% of workers in low-income countries are "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-the-labor-force-employed-in-agriculture?tab=chart&country=Low+income~High+income"", ""children"": [{""text"": ""employed in agriculture"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", arguably the most weather-dependent sector. Most "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/africa-yields-problem"", ""children"": [{""text"": ""are small-scale farmers"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", who are often extremely poor."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Having accurate weather forecasts can help farmers make better decisions. They can get information on the best time to plant their crops. They know in advance when irrigation will be most needed, or when fertilizers might be at risk of being washed away. They can receive alerts about pest and disease outbreaks so they can either protect their crops when an attack is coming or save pesticides when the risk is low. That means they can use precious resources most efficiently if they have access to accurate weather forecasts. Good weather forecasts are most crucial for the poorest people in the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""They’re also crucial for protecting against cyclones, heat waves, flooding, and storm surges. Having accurate forecasts several days in advance allows cities and communities to prepare. Housing can be protected, and emergency services can be on standby to help with the recovery."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But accurate forecasts alone don’t solve the problem: they’re only useful if they are disseminated to people so they can respond. Many of the deadliest disasters over the last few decades "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.sciencedirect.com/science/article/pii/S2212096322000687"", ""children"": [{""text"": ""were accurately forecasted"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" ahead of time. The common failure was poor communication."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Improving forecasts is the foundation. But these also need to be incorporated into effective early warning systems. The World Meteorological Organization "", ""spanType"": ""span-simple-text""}, {""url"": ""https://documents1.worldbank.org/curated/en/099050123155016375/pdf/P1765160197f400b80947e0af8c48049151.pdf"", ""children"": [{""text"": ""estimates that"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" around one-third of the world — predominantly the poorest countries — do not have them."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Improving forecasts — especially in low-income countries — is underrated"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""After the big progress in recent decades, we take good weather forecasting and dissemination for granted in large parts of the world. Making this available to everyone would make a difference."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This will be even more important as climate change increases the risks of weather-related disasters. It is ultimately the poorest, who are the more vulnerable, who will suffer the worst consequences. Better forecasts are key to good climate change adaptation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Proper investment and financial support will be essential to close the gaps."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There also emerging technologies that could accelerate this. A "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.nature.com/articles/s41586-023-06185-3"", ""children"": [{""text"": ""recent paper"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" published in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Nature "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""documented a new "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/artificial-intelligence"", ""children"": [{""text"": ""artificial intelligence (AI)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" system — Pangu-Weather — that can perform forecasts as accurately (or better) than leading meteorological agencies up to 10,000 times faster."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It was trained on 39 years of historical data. The speed of these forecasts would make them much cheaper to run and could provide much better results for countries with limited budgets."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Faster and more efficient technologies can also fill the gaps where land-based weather stations aren’t available. Sensor-carrying drones can run surveys over specific areas to build higher-resolution maps. With lower-cost and more efficient ways of turning that into forecasts, mobile technologies can disseminate this information quickly. Some companies are already sending messages to farmers in low-income countries to advise them on the best time to plant their crops."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This innovation is crucial to making countries more resilient to weather today. But it’s also essential in a world where weather is likely to get more extreme."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""Many thanks to Max Roser and Edouard Mathieu for their valuable feedback and comments on this article."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Acknowledgements"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""05108c11b8200cdc32d85e77f14c890168caa6b8"": {""id"": ""05108c11b8200cdc32d85e77f14c890168caa6b8"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""These charts are sourced from the following sources: Alley, R. B., Emanuel, K. A., & Zhang, F. (2019). Advances in weather prediction. Science, 363(6425), 342-344."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Economist (2023). The high-tech race to improve weather forecasting."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""6a1a179a99f0ecd3d9fc08cd78b268cefe31e3a7"": {""id"": ""6a1a179a99f0ecd3d9fc08cd78b268cefe31e3a7"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Alley, R. B., Emanuel, K. A., & Zhang, F. (2019). Advances in weather prediction. Science, 363(6425), 342-344."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""8a602e04f06567546458d0554c76a1f066adcb34"": {""id"": ""8a602e04f06567546458d0554c76a1f066adcb34"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""De Perez, E. C., Berse, K. B., Depante, L. A. C., Easton-Calabria, E., Evidente, E. P. R., Ezike, T., ... & Van Sant, C. (2022). Learning from the past in moving to the future: Invest in communication and response to weather early warnings to reduce death and damage. Climate Risk Management, 38, 100461."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""91032e71f50a4c6894dafdd6f0d09aec6ab0c612"": {""id"": ""91032e71f50a4c6894dafdd6f0d09aec6ab0c612"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Linsenmeier, Manuel & Shrader, Jeffrey G., 2023. \""Global inequalities in weather forecasts,\"" SocArXiv 7e2jf, Center for Open Science."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You might wonder how this can be true when the ECMWF forecasts have so quickly improved in both the Northern and Southern Hemisphere. 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Science Advances, 3(5), e1602632."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Weather forecasts have become much more accurate; we now need to make them available to everyone"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""A four-day forecast today is as accurate as a one-day forecast 30 years ago."", ""dateline"": ""March 12, 2024"", ""subtitle"": ""A four-day forecast today is as accurate as a one-day forecast 30 years ago."", ""featured-image"": ""weather-forecasting-featured-image.png""}",1,2024-02-14 12:38:07,2024-03-12 10:34:37,2024-03-15 17:51:44,listed,ALBJ4Lt2AdFj0YAh191f4eEeminL4CMufqEJ1zCc8QRsMwEgD_usz4yCX3UnMrJ1Wq3EDIP_5hfsN8UV63MHew,,"Weather forecasts are often seen as just a nice thing to have. Useful when planning a Sunday barbecue, or when we want to know if we’ll need an umbrella for the day. But in many ways weather forecasts are absolutely crucial: they can be a matter of life and death. Accurate forecasts can save lives by giving early warnings of storms, heat waves, and disasters. Farmers use them for agricultural management, which can make the difference between a lost harvest or a harvest of plenty. Grid operators rely on accurate forecasts of temperatures for heating and cooling demand, and how much energy they’ll get from wind and solar farms. Pilots and sailors need them to carry people across oceans safely. Accurate information about future weather is often absolutely vital. In this article, I look at improvements over time and the global inequalities that need to be closed to protect lives and livelihoods around the world. # Weather forecasts have improved a lot Weather forecasting has come a long way. In 650 B.C. the Babylonians would try to predict weather patterns based on cloud patterns and movements. Three centuries later, Aristotle wrote _Meteorologica_, discussing how phenomena such as rain, hail, hurricanes, and lightning formed. Much of it turned out to be wrong, but it represents one of the first attempts to explain how the weather works in detail. It wasn’t until 1859 that the UK’s Meteorological Service (the Met Office) [issued its first](https://www.metoffice.gov.uk/about-us/who/our-history) weather forecast for shipping. Two years later, it broadcasted its first public weather forecast. While meteorological measurements improved over time, the massive step-change in predictions came with the use of computerized numerical modeling. This didn’t start until a century later, in the 1960s. Forecasts have improved a lot since then. We can see this across a range of measurements, and different national meteorological organizations. The Met Office [says](https://blog.metoffice.gov.uk/2023/11/15/why-an-exact-date-weather-forecast-headline-isnt-what-it-seems/#:~:text=Despite%20the%20certainty%20suggested%20in,come%20with%20in%2Dbuilt%20uncertainty.) its four-day forecasts are now as accurate as its one-day forecasts were 30 years ago. Predictions have gotten much better in the United States, too. We can see this in some of the most important forecasts: the prediction of hurricanes. The National Hurricane Center [publishes data](https://www.nhc.noaa.gov/verification/verify5.shtml) on the “track error” of hurricanes and cyclones — the error in _where_ the hurricane hits. This is shown in the chart below, from the 1960s onwards. Each line represents the error of forecasts for different time periods in advance. For example, 12 hours before it hits, all the way up to 120 hours (or 5 days) before. We can see that this track error — especially for longer-term forecasts — has decreased a lot over time. In the 1970s, a 48-hour forecast had an error between 200 and 400 nautical miles; today this is around 50 nautical miles. We can show the same data another way. In the chart below, each line represents the average error for each decade. On the horizontal axis we have the forecast period, again extending from 0 to 120 hours. The 72-hour error in the 1960s and 70s was over 400 nautical miles. Today, it’s less than 80 miles. Meteorologists can now make pretty accurate predictions of where a hurricane will hit three or four days in advance, which lets cities and communities prepare while preventing unnecessary evacuations that might have been implemented in the past. The [European Centre for Medium-Range Weather Forecasts](https://charts.ecmwf.int/) (ECMWF) produces global numerical weather models. While national weather agencies use much higher-resolution processing to get local forecasts, these global models provide a crucial input into these systems. The ECMWF publishes analyses of its errors over time. This is shown in the chart below.1 It shows the difference between the forecast and the actual weather outcome for forecasts 3, 5, 7, and 10 days in advance. The metric used here is the “[500 hPa geopotential height](https://climate.copernicus.eu/esotc/2022/atmospheric-circulation)”, a commonly used meteorological measure of air pressure (which dictates weather patterns). The solid line is for the Northern Hemisphere, and the dashed line is for the Southern. Three-day forecasts — shown in blue — have been pretty accurate since the 1980s, and have still gotten a lot better over time. Today the accuracy is around 97%. The biggest improvements we’ve seen are for longer timeframes. By the early 2000s, 5-day forecasts were “highly accurate” and 7-day forecasts are reaching that threshold today. 10-day forecasts aren’t quite there yet but are getting better. # Why have weather forecasts improved? A few key developments [explain these improvements](https://www.science.org/doi/10.1126/science.aav7274).2 The first big change is that the data has improved. More extensive and higher-resolution observations can be used as inputs into the weather models. This is because we have more and better satellite data, and because land-based stations are covering many more areas around the globe, and at a higher density. The precision of these instruments has improved, too. These observations are then fed into numerical prediction models to forecast the weather. That brings us to the next two developments. The computers on which these models are run have gotten much faster. Faster speeds are crucial: the Met Office now chunks the world into grids of smaller and smaller squares. While they once modeled the world in 90-kilometer-wide squares, they are now down to a grid of 1.5-kilometer squares. That means many more calculations need to be run to get this high-resolution map. The methods to turn the observations into model outputs have also improved. We’ve gone from very simple visions of the world to methods that can capture the complexity of these systems in detail. The final crucial factor is how these forecasts are communicated. Not long ago, you could only get daily updates in the daily newspaper. With the rise of radio and TV, you could get a few notices per day. Now, we can get minute-by-minute updates online or on our smartphones. # Low-income countries have much worse forecasts, and often no early warning systems At home in Scotland, I can open an app on my phone and get a pretty accurate 5-day forecast within seconds. Unfortunately, this quality of information isn’t available to everyone. There are large differences in weather forecasts across the world, with a large gap between rich and poor. As the researchers Manuel Linsenmeier and Jeffrey Shrader report in a [recent paper](https://ideas.repec.org/p/osf/socarx/7e2jf.html), a 7-day forecast in a rich country can be more accurate than a one-day forecast in some low-income ones.3 While national forecasts have improved over time across all income levels, the quality gap today is almost as wide as it was in the 1980s. There are a few reasons for this. First, far fewer land-based instruments and radiosondes measure meteorological data in poorer countries. Second, the frequency of reporting is much lower. This is unsurprising when we look at the amount of money spent on weather and climate information. In [a paper](https://www.science.org/doi/10.1126/sciadv.1602632) published in _Science_, Lucian Georgeson, Mark Maslin, and Martyn Poessinouw looked at differences in spending across income groups.4 This includes private and public spending on commercial products that fall within the definition of “weather and climate information services”. This is shown as the spending _per person_, and the spending as a share of gross domestic product (GDP) in the chart below. Low-income countries spend 15 to 20 times less per person than high-income countries. But given the size of their economies, they actually spend _more_ as a share of GDP. This gap is a problem. 60% of workers in low-income countries are [employed in agriculture](https://ourworldindata.org/grapher/share-of-the-labor-force-employed-in-agriculture?tab=chart&country=Low+income~High+income), arguably the most weather-dependent sector. Most [are small-scale farmers](https://ourworldindata.org/africa-yields-problem), who are often extremely poor. Having accurate weather forecasts can help farmers make better decisions. They can get information on the best time to plant their crops. They know in advance when irrigation will be most needed, or when fertilizers might be at risk of being washed away. They can receive alerts about pest and disease outbreaks so they can either protect their crops when an attack is coming or save pesticides when the risk is low. That means they can use precious resources most efficiently if they have access to accurate weather forecasts. Good weather forecasts are most crucial for the poorest people in the world. They’re also crucial for protecting against cyclones, heat waves, flooding, and storm surges. Having accurate forecasts several days in advance allows cities and communities to prepare. Housing can be protected, and emergency services can be on standby to help with the recovery. But accurate forecasts alone don’t solve the problem: they’re only useful if they are disseminated to people so they can respond. Many of the deadliest disasters over the last few decades [were accurately forecasted](https://www.sciencedirect.com/science/article/pii/S2212096322000687) ahead of time. The common failure was poor communication.5 Improving forecasts is the foundation. But these also need to be incorporated into effective early warning systems. The World Meteorological Organization [estimates that](https://documents1.worldbank.org/curated/en/099050123155016375/pdf/P1765160197f400b80947e0af8c48049151.pdf) around one-third of the world — predominantly the poorest countries — do not have them. # Improving forecasts — especially in low-income countries — is underrated After the big progress in recent decades, we take good weather forecasting and dissemination for granted in large parts of the world. Making this available to everyone would make a difference. This will be even more important as climate change increases the risks of weather-related disasters. It is ultimately the poorest, who are the more vulnerable, who will suffer the worst consequences. Better forecasts are key to good climate change adaptation. Proper investment and financial support will be essential to close the gaps. There also emerging technologies that could accelerate this. A [recent paper](https://www.nature.com/articles/s41586-023-06185-3) published in _Nature _documented a new [artificial intelligence (AI)](https://ourworldindata.org/artificial-intelligence) system — Pangu-Weather — that can perform forecasts as accurately (or better) than leading meteorological agencies up to 10,000 times faster.6 It was trained on 39 years of historical data. The speed of these forecasts would make them much cheaper to run and could provide much better results for countries with limited budgets. Faster and more efficient technologies can also fill the gaps where land-based weather stations aren’t available. Sensor-carrying drones can run surveys over specific areas to build higher-resolution maps. With lower-cost and more efficient ways of turning that into forecasts, mobile technologies can disseminate this information quickly. Some companies are already sending messages to farmers in low-income countries to advise them on the best time to plant their crops. This innovation is crucial to making countries more resilient to weather today. But it’s also essential in a world where weather is likely to get more extreme. These charts are sourced from the following sources: Alley, R. B., Emanuel, K. A., & Zhang, F. (2019). Advances in weather prediction. Science, 363(6425), 342-344. The Economist (2023). The high-tech race to improve weather forecasting. Alley, R. B., Emanuel, K. A., & Zhang, F. (2019). Advances in weather prediction. Science, 363(6425), 342-344. Linsenmeier, Manuel & Shrader, Jeffrey G., 2023. ""Global inequalities in weather forecasts,"" SocArXiv 7e2jf, Center for Open Science. You might wonder how this can be true when the ECMWF forecasts have so quickly improved in both the Northern and Southern Hemisphere. This is because most countries need to develop their own forecasts to get more high-resolution predictions. Georgeson, L., Maslin, M., & Poessinouw, M. (2017). Global disparity in the supply of commercial weather and climate information services. Science Advances, 3(5), e1602632. De Perez, E. C., Berse, K. B., Depante, L. A. C., Easton-Calabria, E., Evidente, E. P. R., Ezike, T., ... & Van Sant, C. (2022). Learning from the past in moving to the future: Invest in communication and response to weather early warnings to reduce death and damage. Climate Risk Management, 38, 100461. Bi, K., Xie, L., Zhang, H. et al. Accurate medium-range global weather forecasting with 3D neural networks. Nature 619, 533–538 (2023).",Weather forecasts have become much more accurate; we now need to make them available to everyone 1xO9wHbwM5LKlGFbHyet3CLmiqnI3ZOHS8V3P_81Itf4,regimes-of-the-world-data,article,"{""toc"": [{""slug"": ""how-does-ro-w-work-to-make-its-assessments-valid"", ""text"": ""How does RoW work to make its assessments valid?"", ""title"": ""How does RoW work to make its assessments valid?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-does-ro-w-work-to-make-its-assessments-precise-and-reliable"", ""text"": ""How does RoW work to make its assessments precise and reliable?"", ""title"": ""How does RoW work to make its assessments precise and reliable?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-does-ro-w-work-to-make-its-assessments-comparable"", ""text"": ""How does RoW work to make its assessments comparable?"", ""title"": ""How does RoW work to make its assessments comparable?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-are-the-remaining-differences-in-the-data-dealt-with"", ""text"": ""How are the remaining differences in the data dealt with?"", ""title"": ""How are the remaining differences in the data dealt with?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-is-the-data-made-accessible-and-transparent"", ""text"": ""How is the data made accessible and transparent?"", ""title"": ""How is the data made accessible and transparent?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""keep-reading-on-our-world-in-data"", ""text"": ""Keep reading on Our World in Data"", ""title"": ""Keep reading on Our World in Data"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""acknowledgments"", ""text"": ""Acknowledgments"", ""title"": ""Acknowledgments"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Measuring the state of democracy across the world helps us understand the extent to which people have political rights and freedoms."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But measuring democracy comes with many challenges. People do not always agree on what characteristics define a democracy. These characteristics — such as whether an election was free and fair — are difficult to define and assess. The judgment of experts is to some degree subjective. They may disagree about a specific characteristic or how something as complex as a political system can be reduced into a single measure."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How do researchers address these challenges and measure democracy?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What is the Regimes of the World data?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In some of our work on democracy, we rely on the Regimes of the World (RoW) data by political scientists Anna Lührmann, Marcus Tannenberg, and Staffan Lindberg"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "", published by the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.v-dem.net/vdemds.html"", ""children"": [{""text"": ""Varieties of Democracy (V-Dem) project"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The project is managed by the V-Dem Institute, based at the University of Gothenburg in Sweden. It spans seven more regional centers around the world and is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.v-dem.net/about/v-dem-project/"", ""children"": [{""text"": ""run"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" by five principal investigators, dozens of project and regional managers, and more than 100 country coordinators."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""V-Dem is funded through grants and donations by government agencies and private foundations, such as the Swedish Research Council, the European Commission, and the Marcus and Marianne Wallenberg Foundation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How does RoW characterize democracy?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Regimes of the World distinguishes four types of political systems: closed autocracies, electoral autocracies, electoral democracies, and liberal democracies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Closed autocracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": citizens do not have the right to choose either the chief executive of the government or the legislature through multi-party elections"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Electoral autocracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": citizens have the right to choose the chief executive and the legislature through multi-party elections; but they lack some freedoms, such as the freedoms of association or expression that make the elections meaningful, free, and fair"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Electoral democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": citizens have the right to choose the chief executive and the legislature in meaningful, free and fair, and multi-party elections"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Liberal democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": electoral democracy and citizens enjoy individual and minority rights, are equal before the law, and the actions of the executive are constrained by the legislative and the courts"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can find data on the more specific characteristics and derived measures in our "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/democracy?country=ARG~AUS~BWA~CHN~OWID_WRL&Dataset=Varieties+of+Democracy&Metric=Electoral+democracy&Sub-metric=Main+index"", ""children"": [{""text"": ""Democracy Data Explorer"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How is democracy scored?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Regimes of the World treats democracy as a binary, by classifying a country as either a democracy or not."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This scoring thereby differs from other approaches such as Varieties of Democracy’s "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/vdem-electoral-democracy-data"", ""children"": [{""text"": ""electoral democracy index"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/democracies-measurement"", ""children"": [{""text"": ""other projects"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", which classify countries as a spectrum, with some being scored as more democratic than others."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/political-regime"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""What years and countries are covered?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As of version 13 of the dataset, V-Dem covers 202 countries, going back in time as far as 1789. Many countries have been covered since 1900, including before they became independent from their colonial powers."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""RoW covers countries and years since 1900. But we expand the years and countries covered and refine the coding rules, as detailed below."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How is democracy measured?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""How does RoW work to make its assessments valid?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To measure what it wants to capture, RoW uses data from the Varieties of Democracy project, which assesses the characteristics of democracy mostly through evaluations by experts."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These anonymous experts are primarily academics and members of the media and civil society. They are also often nationals or residents of the country they assess, and therefore know its political system well and can evaluate aspects that are difficult to observe."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""V-Dem’s own team of researchers supplements the expert evaluations. They code some easier-to-observe rules and laws of the political system, such as whether the legislature has a lower and upper house."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How does RoW work to make its assessments precise and reliable?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""V-Dem uses several experts per country, year, and topic, to make its assessments less subjective. In total, around 3,500 country experts fill out surveys for V-Dem every year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While there are fewer experts for small countries and for the time before 1900, they rely typically on 25 experts per country and 5 experts per topic."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How does RoW work to make its assessments comparable?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""V-Dem also works to make their coders’ assessments comparable across countries and time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The surveys ask the experts to answer very specific questions on completely explained scales about sub-characteristics of political systems — such as the presence or absence of election fraud — instead of making them rely on their broad impressions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The surveys are available in English, Arabic, French, Portuguese, Russian, and Spanish to reduce misunderstandings."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Experts further evaluate hypothetical countries, many coded several countries, and they denote their own uncertainty and personal demographic information."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""V-Dem then uses this information to investigate expert biases, which they have found to be limited: they only find that experts from a country tend to be stricter in their assessments. "", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""How are the remaining differences in the data dealt with?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""V-Dem uses a statistical model to address any remaining differences between coders."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The model combines the experts’ ratings of actual countries and hypothetical countries, as well as the experts’ stated uncertainties and personal demographics to produce best, upper-, and lower-bound estimates of many characteristics."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""V-Dem provides these different estimates for all of its main and supplementary indices, including the Electoral Democracy Index and the subindices for free and fair elections, freedom of association, and freedom of expression."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""With the different estimates, V-Dem explicitly acknowledges that its coders can be uncertain or make errors in their measurement."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In addition to its main classification, RoW provides an expanded version that identifies countries that may fit better into the next-higher or -lower main categories. You can find the data in our "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/democracy?country=ARG~AUS~BWA~CHN~OWID_WRL&Dataset=Regimes+of+the+World&Metric=%C2%ADPolitical+regime%2C+including+ambiguous+categories&Sub-metric=Main+classification"", ""children"": [{""text"": ""Democracy Data Explorer"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The overall classification is the result of evaluating whether necessary characteristics are present or not. If the experts consider a country’s elections to have been both multi-party and free and fair, and the country as having had minimal features of an electoral democracy in general"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "", RoW classifies it as a democracy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A country is classified as a liberal democracy if the experts consider the country’s laws to have been transparent; the men and women there as having had access to the justice system; and the country as having had broad features of a liberal democracy overall."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" If it does not meet one of these conditions, the country is classified as an electoral democracy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A country is classified as an autocracy if it does not meet the above criteria of meaningful, free and fair, multi-party elections. It is classified as an electoral autocracy if the experts consider the elections for the legislature and chief executive — the most powerful politician — to have been multi-party. It is classified as a closed autocracy if either the legislature or chief executive has not been chosen in multi-party elections."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How is the data made accessible and transparent?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""V-Dem, which publishes the RoW data, releases "", ""spanType"": ""span-simple-text""}, {""url"": ""https://v-dem.net/data/the-v-dem-dataset/"", ""children"": [{""text"": ""its data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" publicly and makes it straightforward to download and use."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It publishes the overall scores, the underlying subindices, and several hundred specific questions by country-year, country-date, and coder."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""V-Dem also releases descriptions of how RoW measures democracy, as well as the questions and coding procedures that guide the experts and researchers."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How do we change the data?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In our work, we expand the years covered by RoW further."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While RoW covers the years since 1900, we use V-Dem's historical data from 1789 to 1899 to expand the classification’s coverage back in time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To expand the time coverage of today’s countries and include more of the period when they were still non-sovereign territories, we identified the historical entity they were a part of and used that regime’s data whenever available."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Finally, we make some additional minor changes to the coding rules."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Our code and data are available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://github.com/owid/notebooks/tree/main/BastianHerre/democracy"", ""children"": [{""text"": ""on GitHub"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and record our revisions in detail."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How often and when is the data updated?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""V-Dem releases a new version of the data each year in March."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We at Our World in Data aim to update our data within a few weeks of the release."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What are the data’s shortcomings?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are shortcomings in the way Regimes of the World characterizes and measures democracy."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The classification only captures that these political rights were broad, not that they were universal. This means that not all people living in a democracy necessarily enjoy its political rights: this includes children, but often also historically marginalized groups such as women."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The classification also focuses on electoral and liberal understandings of democracy and does not account for other characterizations, such as democracies as egalitarian political systems, in which political power is equally distributed to allow everyone to participate. This means that some of the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/economic-inequality-gini-index"", ""children"": [{""text"": ""most economically-unequal countries"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in the world, such as Brazil and South Africa, are classified as broadly democratic in recent years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""RoW also does not cover some countries with very small populations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Furthermore, because the classification groups all political systems into four broad types, it is not very granular. This means that it does not pick up small changes in political institutions, or conversely that the classification sometimes categorizes countries with similar institutions differently. This includes some recategorizations of countries across years where their political institutions barely changed, but crossed a somewhat arbitrary threshold."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The assessment of the RoW classification remains to some extent subjective. It is built on difficult evaluations by experts that rely less on easier-to-observe characteristics, such as whether regular elections are held."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Finally, the index’s aggregation remains to some extent arbitrary. It is unclear why specific indicators were chosen, such as whether citizens had access to the justice system, and not (also) whether they were free from government repression."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What are the data’s strengths?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Despite these shortcomings, the classification tells us a lot about how democratic the world was in the past and today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Its characterizations of democracy as an electoral political system, in which citizens get to participate in free and fair elections, and a liberal political system, in which citizens are protected from others and the state, are commonly recognized as the two basic principles of democracy and shared by "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/democracies-measurement"", ""children"": [{""text"": ""most of the leading approaches of measuring democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Because it treats democracy as a binary, the classification can make the many differences in political institutions we observe across countries and over time much easier to understand. It allows us to combine the many countries with similar political institutions, while still distinguishing countries whose institutions differ in meaningful ways."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This allows us to observe whether one country is democratic or not, or whether a country has become a democracy or stopped being one over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The index also covers many countries and years. Except for microstates, it covers all countries in the world. Many countries are covered since 1900 — even while they were colonized by another country — and some of them as far back as 1789."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Finally, RoW and V-Dem take many steps to make their assessments valid, precise, comparable across countries and time, and transparent. RoW relies on many country and subject experts answering detailed surveys to measure aspects of political systems that are often difficult to observe, and acknowledges the remaining uncertainty in their assessments in its expanded classification."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What is our summary assessment?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Whether the Regimes of the World classification is a useful measure of democracy will depend on the questions we want to answer."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The classification will not give us a satisfying answer if we are interested in the political rights of historically marginalized groups specifically; in non-electoral or non-liberal understandings of democracy; in the political systems of microstates; and interested in small differences in the political systems of countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In these cases, we will have to rely on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/democracies-measurement"", ""children"": [{""text"": ""other measures"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But if we value a sophisticated measure based on the knowledge of many country experts and are interested in big differences in political regimes, within and across countries, and far into the past, we can learn a lot from this data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is for these latter purposes we use the measure in some of our reporting on democracy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Keep reading on "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Our World in Data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1sC5hYCG5Tbnt02QzaxHFeGhliSWbwUuyttXKP8TS9xc/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""text"": [{""text"": ""Acknowledgments"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I thank Marcus Tannenberg and Johannes von Römer for providing data and code, and Edouard Mathieu, Hannah Ritchie and Max Roser for their very helpful comments and ideas about how to improve this article."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""0e6e859b9e399f12af48aed3634ff14395f49ce1"": {""id"": ""0e6e859b9e399f12af48aed3634ff14395f49ce1"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For more details, see: Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan Lindberg, Jan Teorell, Kyle Marquardt, Juraj Medzihorsky, Daniel Pemstein, Nazifa Alizada, Lisa Gastaldi, Garry Hindle, Josefine Pernes, Johannes von Römer, Eitan Tzelgov, Yi-ting Wang, and Steven Wilson. 2021. V-Dem Methodology v11.1. Varieties of Democracy (V-Dem) Project: page 24."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""187e41deda5445cc8af582cfa7980c6a1312d6e4"": {""id"": ""187e41deda5445cc8af582cfa7980c6a1312d6e4"", ""index"": 13, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For instance, we may consider Mexico throughout the 2000s, with its legislature and executive chosen in multi-party elections, and scores on the polyarchy index between 0.634 and 0.701 in these years, as virtually equally democratic. At the same time, we would still acknowledge that its political system was substantially less democratic in the 1980s, when its legislature and executive were also chosen in multi-party elections, but other freedoms were restricted to such an extent (with scores on the polyarchy index between 0.302 and 0.377) that those elections did not hold the government accountable."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""1b7f08dc6ac4f25eaeae4d305408298ee1b8830e"": {""id"": ""1b7f08dc6ac4f25eaeae4d305408298ee1b8830e"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Specifically, it uses a Bayesian Item-Response Theory estimation strategy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Marquardt, Kyle, and Daniel Pemstein. 2018. IRT Models for Expert-Coded Panel Data. Political Analysis 26(4): 431-456."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""59fe2f1087c1fff104e369455f9a58693d09779b"": {""id"": ""59fe2f1087c1fff104e369455f9a58693d09779b"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The overall extent of electoral democracy is based on V-Dem’s "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/vdem-electoral-democracy-data"", ""children"": [{""text"": ""electoral democracy index"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""5dae3493a955fd69a0e0ec01d069b6527ffc64e3"": {""id"": ""5dae3493a955fd69a0e0ec01d069b6527ffc64e3"", ""index"": 12, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For instance, RoW classifies Zambia as an electoral autocracy from 1994-1999, with scores on V-Dem’s polyarchy index of 0.499 and then 0.496. From 2000-2004, the country was reclassified as an electoral democracy, even though it barely moved over the RoW threshold of 0.5, with scores of first 0.502 and then 0.506."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""704fbe0e3a18357d2d53061d3dfe66c656c60bfc"": {""id"": ""704fbe0e3a18357d2d53061d3dfe66c656c60bfc"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Lührmann, Anna, Marcus Tannnberg, and Staffan Lindberg. 2018. Regimes of the World (RoW): Opening New Avenues for the Comparative Study of Political Regimes. Politics and Governance 6(1): 60-77."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7799c8aaa02c24a5d7e63608b8695eee683f6024"": {""id"": ""7799c8aaa02c24a5d7e63608b8695eee683f6024"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For example, V-Dem only provides regime data since Bangladesh’s independence in 1971. There is, however, regime data for Pakistan and the colony of India, both of which the current territory of Bangladesh was a part. We, therefore, use the regime data of Pakistan for Bangladesh from 1947 to 1970, and the regime data of India from 1789 to 1946. We did so for all countries with a past or current population of more than one million."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7b1da23aaef19d9f5d1ff06feb9edbf7ad4814ef"": {""id"": ""7b1da23aaef19d9f5d1ff06feb9edbf7ad4814ef"", ""index"": 11, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""One example is Switzerland, which has been classified as a liberal democracy since 1849, even though its government forbade women to vote and stand in elections "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.parlament.ch/en/%C3%BCber-das-parlament/political-women/conquest-of-equal-rights/women-suffrage"", ""children"": [{""text"": ""until 1971, more than a hundred years later"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9ecd8dcf52433b58e7203893d585854385f1c093"": {""id"": ""9ecd8dcf52433b58e7203893d585854385f1c093"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Expressed precisely, V-Dem’s measurement model produces a probability distribution over the country-year scores. The best estimate is the distribution’s median, while the upper and lower bound estimates demarcate the interval in which the model places 68 percent of the probability mass."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a785dc4f8490a34bf891beabd7823794fa7acd84"": {""id"": ""a785dc4f8490a34bf891beabd7823794fa7acd84"", ""index"": 10, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This and the following section draw on several very helpful other articles summarizing and reviewing some of the leading democracy datasets:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Boese, Vanessa. 2019. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Boese%2C+Vanessa.+2019.+How+%28not%29+to+measure+democracy.+International+Area+Studies+Review+22%282%29%3A+95-127&btnG="", ""children"": [{""text"": ""How (not) to measure democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". International Area Studies Review 22(2): 95-127."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Coppedge, Michael, John Gerring, Staffan I. Lindberg, Svend-Erik Skaaning, and Jan Teorell. 2017. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Coppedge%2C+Michael%2C+John+Gerring%2C+Staffan+I.+Lindberg%2C+Svend-Erik+Skaaning%2C+and+Jan+Teorell.+2017.+V-Dem+Comparisons+and+Contrasts+with+Other+Measurement+Projects.+V-Dem+Working+Paper+45.&btnG="", ""children"": [{""text"": ""V-Dem Comparisons and Contrasts with Other Measurement Projects"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". V-Dem Working Paper 45."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Møller, Jørgen and Svend-Erik Skaaning. 2021. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=M%C3%B8ller%2C+J%C3%B8rgen+and+Svend-Erik+Skaaning.+2021.+Varieties+of+Measurement%3A+A+Comparative+Assessment+of+Relatively+New+Democracy+Ratings+based+on+Original+Data.+V-Dem+Working+Paper+123.&btnG="", ""children"": [{""text"": ""Varieties of Measurement: A Comparative Assessment of Relatively New Democracy Ratings based on Original Data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". V-Dem Working Paper 123."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Skaaning, Svend-Erik. 2018. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Skaaning%2C+Svend-Erik.+2018.+Different+Types+of+Data+and+the+Validity+of+Democracy+Measures.+Politics+and+Governance+6%281%29%3A+105-116.&btnG="", ""children"": [{""text"": ""Different Types of Data and the Validity of Democracy Measures"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Politics and Governance 6(1): 105-116."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ba4e2452e99cc684238b536e965756f8543fa4c2"": {""id"": ""ba4e2452e99cc684238b536e965756f8543fa4c2"", ""index"": 9, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The two most consequential changes we make relate to RoW’s identification of whether a country’s chief executive is elected. One way RoW considers a chief executive to have been elected — even if they are not directly elected or appointed by the legislature — is if they are the head of state, they depend on the approval of the legislature, and there were multi-party elections for the executive. This last part is likely a coding error because to be consistent with RoW's other definitions, this should depend on multi-party legislative, not executive, elections. Only if the legislature has been chosen in multi-party elections does it make an otherwise unelected chief executive—who must be approved by that legislature—dependent on multi-party elections. We correct this error. Furthermore, RoW considers a chief executive to have been elected if the country had chosen both its legislature and executive in multi-party elections. But this considers some chief executives as elected even if they came to power through force after elections were previously held. Examples include the coup d’états led by Fulgencio Batista in Cuba in 1952 and by Muhammadu Buhari in Nigeria in 1983. We instead consider such chief executives as unelected."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""bb9bbac22114875dbfbef0f748783c48a02a73f9"": {""id"": ""bb9bbac22114875dbfbef0f748783c48a02a73f9"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The overall extent of liberal democracy is based on V-Dem’s liberal component index, which combines expert assessments of citizens’ equality before the law and their individual liberties, as well as legislative and judicial constraints on the executive."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d39b6c728ec3cfc6a5e32603fe71ada3ed5fb124"": {""id"": ""d39b6c728ec3cfc6a5e32603fe71ada3ed5fb124"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""“We have run extensive tests on how well such individual-level factors predict country-ratings but have found that the only factor consistently associated with country-ratings is country of origin (with “domestic” experts being harsher in their judgments).”"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan Lindberg, Jan Teorell, Kyle Marquardt, Juraj Medzihorsky, Daniel Pemstein, Nazifa Alizada, Lisa Gastaldi, Garry Hindle, Josefine Pernes, Johannes von Römer, Eitan Tzelgov, Yi-ting Wang, and Steven Wilson. 2021. V-Dem Methodology v11.1. Varieties of Democracy (V-Dem) Project: page 24."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""def7f0b8e1c0f0b857371fa1a8a4729a634e0b1f"": {""id"": ""def7f0b8e1c0f0b857371fa1a8a4729a634e0b1f"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Cecilia Borella, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Oskar Rydén, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2024. \"""", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Coppedge%2C+Michael%2C+John+Gerring%2C+Carl+Henrik+Knutsen%2C+Staffan+I.+Lindberg%2C+Jan+Teorell%2C+David+Altman%2C+Fabio+Angiolillo%2C+Michael+Bernhard%2C+Cecilia+Borella%2C+Agnes+Cornell%2C+M.+Steven+Fish%2C+Linnea+Fox%2C+Lisa+Gastaldi%2C+Haakon+Gjerl%C3%B8w%2C+Adam+Glynn%2C+Ana+Good+God%2C+Sandra+Grahn%2C+Allen+Hicken%2C+Katrin+Kinzelbach%2C+Joshua+Krusell%2C+Kyle+L.+Marquardt%2C+Kelly+McMann%2C+Valeriya+Mechkova%2C+Juraj+Medzihorsky%2C+Natalia+Natsika%2C+Anja+Neundorf%2C+Pamela+Paxton%2C+Daniel+Pemstein%2C+Josefine+Pernes%2C+Oskar+Ryd%C3%A9n%2C+Johannes+von+R%C3%B6mer%2C+Brigitte+Seim%2C+Rachel+Sigman%2C+Svend-Erik+Skaaning%2C+Jeffrey+Staton%2C+Aksel+Sundstr%C3%B6m%2C+Eitan+Tzelgov%2C+Yi-ting+Wang%2C+Tore+Wig%2C+Steven+Wilson+and+Daniel+Ziblatt.+2024.+%22V-Dem+Country-Year+Dataset+v14%22+Varieties+of+Democracy+%28V-Dem%29+Project.+https%3A%2F%2Fdoi.org%2F10.23696%2Fmcwt-fr58&btnG="", ""children"": [{""text"": ""V-Dem Country-Year Dataset v14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""\"" Varieties of Democracy (V-Dem) Project. https://doi.org/10.23696/mcwt-fr58"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""The ‘Regimes of the World’ data: how do researchers measure democracy?"", ""authors"": [""Bastian Herre""], ""excerpt"": ""There are many ways to measure democracy. Here is how the Regimes of the World classification does it, one of the leading sources of global democracy data."", ""dateline"": ""originally published on December 2, 2021 (last updated in April 2024)"", ""subtitle"": ""There are many ways to measure democracy. Here is how the Regimes of the World classification does it, one of the leading sources of global democracy data."", ""featured-image"": ""the-regimes-of-the-world-data-how-do-researchers-measure-democracy-featured-image.png""}",1,2023-07-21 11:15:11,2021-12-02 13:47:16,2023-12-28 16:31:13,unlisted,ALBJ4LsxJvUJmrN5-WZbuqAWXl0rZCy--iDzwej0oX_lcAcAglb9TDAiZSIGPzdxRkAX7jNFCHdNRGg7WGARKQ,,"Measuring the state of democracy across the world helps us understand the extent to which people have political rights and freedoms. But measuring democracy comes with many challenges. People do not always agree on what characteristics define a democracy. These characteristics — such as whether an election was free and fair — are difficult to define and assess. The judgment of experts is to some degree subjective. They may disagree about a specific characteristic or how something as complex as a political system can be reduced into a single measure. How do researchers address these challenges and measure democracy? # What is the Regimes of the World data? In some of our work on democracy, we rely on the Regimes of the World (RoW) data by political scientists Anna Lührmann, Marcus Tannenberg, and Staffan Lindberg1, published by the [Varieties of Democracy (V-Dem) project](https://www.v-dem.net/vdemds.html).2 The project is managed by the V-Dem Institute, based at the University of Gothenburg in Sweden. It spans seven more regional centers around the world and is [run](https://www.v-dem.net/about/v-dem-project/) by five principal investigators, dozens of project and regional managers, and more than 100 country coordinators. V-Dem is funded through grants and donations by government agencies and private foundations, such as the Swedish Research Council, the European Commission, and the Marcus and Marianne Wallenberg Foundation. # How does RoW characterize democracy? Regimes of the World distinguishes four types of political systems: closed autocracies, electoral autocracies, electoral democracies, and liberal democracies. * **Closed autocracy**: citizens do not have the right to choose either the chief executive of the government or the legislature through multi-party elections * **Electoral autocracy**: citizens have the right to choose the chief executive and the legislature through multi-party elections; but they lack some freedoms, such as the freedoms of association or expression that make the elections meaningful, free, and fair * **Electoral democracy**: citizens have the right to choose the chief executive and the legislature in meaningful, free and fair, and multi-party elections * **Liberal democracy**: electoral democracy and citizens enjoy individual and minority rights, are equal before the law, and the actions of the executive are constrained by the legislative and the courts You can find data on the more specific characteristics and derived measures in our [Democracy Data Explorer](https://ourworldindata.org/explorers/democracy?country=ARG~AUS~BWA~CHN~OWID_WRL&Dataset=Varieties+of+Democracy&Metric=Electoral+democracy&Sub-metric=Main+index). # How is democracy scored? Regimes of the World treats democracy as a binary, by classifying a country as either a democracy or not. This scoring thereby differs from other approaches such as Varieties of Democracy’s [electoral democracy index](https://ourworldindata.org/vdem-electoral-democracy-data) and [other projects](https://ourworldindata.org/democracies-measurement), which classify countries as a spectrum, with some being scored as more democratic than others. # What years and countries are covered? As of version 13 of the dataset, V-Dem covers 202 countries, going back in time as far as 1789. Many countries have been covered since 1900, including before they became independent from their colonial powers. RoW covers countries and years since 1900. But we expand the years and countries covered and refine the coding rules, as detailed below. # How is democracy measured? ## How does RoW work to make its assessments valid? To measure what it wants to capture, RoW uses data from the Varieties of Democracy project, which assesses the characteristics of democracy mostly through evaluations by experts.3 These anonymous experts are primarily academics and members of the media and civil society. They are also often nationals or residents of the country they assess, and therefore know its political system well and can evaluate aspects that are difficult to observe. V-Dem’s own team of researchers supplements the expert evaluations. They code some easier-to-observe rules and laws of the political system, such as whether the legislature has a lower and upper house. ## How does RoW work to make its assessments precise and reliable? V-Dem uses several experts per country, year, and topic, to make its assessments less subjective. In total, around 3,500 country experts fill out surveys for V-Dem every year. While there are fewer experts for small countries and for the time before 1900, they rely typically on 25 experts per country and 5 experts per topic. ## How does RoW work to make its assessments comparable? V-Dem also works to make their coders’ assessments comparable across countries and time. The surveys ask the experts to answer very specific questions on completely explained scales about sub-characteristics of political systems — such as the presence or absence of election fraud — instead of making them rely on their broad impressions. The surveys are available in English, Arabic, French, Portuguese, Russian, and Spanish to reduce misunderstandings. Experts further evaluate hypothetical countries, many coded several countries, and they denote their own uncertainty and personal demographic information. V-Dem then uses this information to investigate expert biases, which they have found to be limited: they only find that experts from a country tend to be stricter in their assessments. 4 ## How are the remaining differences in the data dealt with? V-Dem uses a statistical model to address any remaining differences between coders.5 The model combines the experts’ ratings of actual countries and hypothetical countries, as well as the experts’ stated uncertainties and personal demographics to produce best, upper-, and lower-bound estimates of many characteristics.6 V-Dem provides these different estimates for all of its main and supplementary indices, including the Electoral Democracy Index and the subindices for free and fair elections, freedom of association, and freedom of expression. With the different estimates, V-Dem explicitly acknowledges that its coders can be uncertain or make errors in their measurement. In addition to its main classification, RoW provides an expanded version that identifies countries that may fit better into the next-higher or -lower main categories. You can find the data in our [Democracy Data Explorer](https://ourworldindata.org/explorers/democracy?country=ARG~AUS~BWA~CHN~OWID_WRL&Dataset=Regimes+of+the+World&Metric=%C2%ADPolitical+regime%2C+including+ambiguous+categories&Sub-metric=Main+classification). The overall classification is the result of evaluating whether necessary characteristics are present or not. If the experts consider a country’s elections to have been both multi-party and free and fair, and the country as having had minimal features of an electoral democracy in general7, RoW classifies it as a democracy. A country is classified as a liberal democracy if the experts consider the country’s laws to have been transparent; the men and women there as having had access to the justice system; and the country as having had broad features of a liberal democracy overall.8 If it does not meet one of these conditions, the country is classified as an electoral democracy. A country is classified as an autocracy if it does not meet the above criteria of meaningful, free and fair, multi-party elections. It is classified as an electoral autocracy if the experts consider the elections for the legislature and chief executive — the most powerful politician — to have been multi-party. It is classified as a closed autocracy if either the legislature or chief executive has not been chosen in multi-party elections. ## How is the data made accessible and transparent? V-Dem, which publishes the RoW data, releases [its data](https://v-dem.net/data/the-v-dem-dataset/) publicly and makes it straightforward to download and use. It publishes the overall scores, the underlying subindices, and several hundred specific questions by country-year, country-date, and coder. V-Dem also releases descriptions of how RoW measures democracy, as well as the questions and coding procedures that guide the experts and researchers. # How do we change the data? In our work, we expand the years covered by RoW further. While RoW covers the years since 1900, we use V-Dem's historical data from 1789 to 1899 to expand the classification’s coverage back in time. To expand the time coverage of today’s countries and include more of the period when they were still non-sovereign territories, we identified the historical entity they were a part of and used that regime’s data whenever available.9 Finally, we make some additional minor changes to the coding rules.10 Our code and data are available [on GitHub](https://github.com/owid/notebooks/tree/main/BastianHerre/democracy) and record our revisions in detail. # How often and when is the data updated? V-Dem releases a new version of the data each year in March. We at Our World in Data aim to update our data within a few weeks of the release. # What are the data’s shortcomings? There are shortcomings in the way Regimes of the World characterizes and measures democracy.11 The classification only captures that these political rights were broad, not that they were universal. This means that not all people living in a democracy necessarily enjoy its political rights: this includes children, but often also historically marginalized groups such as women.12 The classification also focuses on electoral and liberal understandings of democracy and does not account for other characterizations, such as democracies as egalitarian political systems, in which political power is equally distributed to allow everyone to participate. This means that some of the [most economically-unequal countries](https://ourworldindata.org/grapher/economic-inequality-gini-index) in the world, such as Brazil and South Africa, are classified as broadly democratic in recent years. RoW also does not cover some countries with very small populations. Furthermore, because the classification groups all political systems into four broad types, it is not very granular. This means that it does not pick up small changes in political institutions, or conversely that the classification sometimes categorizes countries with similar institutions differently. This includes some recategorizations of countries across years where their political institutions barely changed, but crossed a somewhat arbitrary threshold.13 The assessment of the RoW classification remains to some extent subjective. It is built on difficult evaluations by experts that rely less on easier-to-observe characteristics, such as whether regular elections are held. Finally, the index’s aggregation remains to some extent arbitrary. It is unclear why specific indicators were chosen, such as whether citizens had access to the justice system, and not (also) whether they were free from government repression. # What are the data’s strengths? Despite these shortcomings, the classification tells us a lot about how democratic the world was in the past and today. Its characterizations of democracy as an electoral political system, in which citizens get to participate in free and fair elections, and a liberal political system, in which citizens are protected from others and the state, are commonly recognized as the two basic principles of democracy and shared by [most of the leading approaches of measuring democracy](https://ourworldindata.org/democracies-measurement). Because it treats democracy as a binary, the classification can make the many differences in political institutions we observe across countries and over time much easier to understand. It allows us to combine the many countries with similar political institutions, while still distinguishing countries whose institutions differ in meaningful ways.14 This allows us to observe whether one country is democratic or not, or whether a country has become a democracy or stopped being one over time. The index also covers many countries and years. Except for microstates, it covers all countries in the world. Many countries are covered since 1900 — even while they were colonized by another country — and some of them as far back as 1789. Finally, RoW and V-Dem take many steps to make their assessments valid, precise, comparable across countries and time, and transparent. RoW relies on many country and subject experts answering detailed surveys to measure aspects of political systems that are often difficult to observe, and acknowledges the remaining uncertainty in their assessments in its expanded classification. # What is our summary assessment? Whether the Regimes of the World classification is a useful measure of democracy will depend on the questions we want to answer. The classification will not give us a satisfying answer if we are interested in the political rights of historically marginalized groups specifically; in non-electoral or non-liberal understandings of democracy; in the political systems of microstates; and interested in small differences in the political systems of countries. In these cases, we will have to rely on [other measures](https://ourworldindata.org/democracies-measurement). But if we value a sophisticated measure based on the knowledge of many country experts and are interested in big differences in political regimes, within and across countries, and far into the past, we can learn a lot from this data. It is for these latter purposes we use the measure in some of our reporting on democracy. ## Keep reading on _Our World in Data_ ### undefined undefined https://docs.google.com/document/d/1sC5hYCG5Tbnt02QzaxHFeGhliSWbwUuyttXKP8TS9xc/edit ## Acknowledgments I thank Marcus Tannenberg and Johannes von Römer for providing data and code, and Edouard Mathieu, Hannah Ritchie and Max Roser for their very helpful comments and ideas about how to improve this article. For more details, see: Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan Lindberg, Jan Teorell, Kyle Marquardt, Juraj Medzihorsky, Daniel Pemstein, Nazifa Alizada, Lisa Gastaldi, Garry Hindle, Josefine Pernes, Johannes von Römer, Eitan Tzelgov, Yi-ting Wang, and Steven Wilson. 2021. V-Dem Methodology v11.1. Varieties of Democracy (V-Dem) Project: page 24. For instance, we may consider Mexico throughout the 2000s, with its legislature and executive chosen in multi-party elections, and scores on the polyarchy index between 0.634 and 0.701 in these years, as virtually equally democratic. At the same time, we would still acknowledge that its political system was substantially less democratic in the 1980s, when its legislature and executive were also chosen in multi-party elections, but other freedoms were restricted to such an extent (with scores on the polyarchy index between 0.302 and 0.377) that those elections did not hold the government accountable. Specifically, it uses a Bayesian Item-Response Theory estimation strategy. Marquardt, Kyle, and Daniel Pemstein. 2018. IRT Models for Expert-Coded Panel Data. Political Analysis 26(4): 431-456. The overall extent of electoral democracy is based on V-Dem’s [electoral democracy index](https://ourworldindata.org/vdem-electoral-democracy-data). For instance, RoW classifies Zambia as an electoral autocracy from 1994-1999, with scores on V-Dem’s polyarchy index of 0.499 and then 0.496. From 2000-2004, the country was reclassified as an electoral democracy, even though it barely moved over the RoW threshold of 0.5, with scores of first 0.502 and then 0.506. Lührmann, Anna, Marcus Tannnberg, and Staffan Lindberg. 2018. Regimes of the World (RoW): Opening New Avenues for the Comparative Study of Political Regimes. Politics and Governance 6(1): 60-77. Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Oskar Rydén, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson and Daniel Ziblatt. 2023. [V-Dem [Country-Year/Country-Date] Dataset v13.](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Coppedge%2C+Michael%2C+John+Gerring%2C+Carl+Henrik+Knutsen%2C+Staffan+I.+Lindberg%2C+Jan+Teorell%2C+David+Altman%2C+Michael+Bernhard%2C+Agnes+Cornell%2C+M.+Steven+Fish%2C+Lisa+Gastaldi%2C+Haakon+Gjerl%C3%B8w%2C+Adam+Glynn%2C+Ana+Good+God%2C+Sandra+Grahn%2C+Allen+Hicken%2C+Katrin+Kinzelbach%2C+Joshua+Krusell%2C+Kyle+L.+Marquardt%2C+Kelly+McMann%2C+Valeriya+Mechkova%2C+Juraj+Medzihorsky%2C+Natalia+Natsika%2C+Anja+Neundorf%2C+Pamela+Paxton%2C+Daniel+Pemstein%2C+Josefine+Pernes%2C+Oskar+Ryd%C3%A9n%2C+Johannes+von+R%C3%B6mer%2C+Brigitte+Seim%2C+Rachel+Sigman%2C+Svend-Erik+Skaaning%2C+Jeffrey+Staton%2C+Aksel+Sundstr%C3%B6m%2C+Eitan+Tzelgov%2C+Yi-ting+Wang%2C+Tore+Wig%2C+Steven+Wilson+and+Daniel+Ziblatt.+2023.+V-Dem+%5BCountry-Year%2FCountry-Date%5D+Dataset+v13.+Varieties+of+Democracy+%28V-Dem%29+Project.&btnG=) Varieties of Democracy (V-Dem) Project. Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2023. [The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data.](https://v-dem.net/media/publications/Working_Paper_21_z5BldB1.pdf) V-Dem Working Paper No. 21. University of Gothenburg: Varieties of Democracy Institute. For example, V-Dem only provides regime data since Bangladesh’s independence in 1971. There is, however, regime data for Pakistan and the colony of India, both of which the current territory of Bangladesh was a part. We, therefore, use the regime data of Pakistan for Bangladesh from 1947 to 1970, and the regime data of India from 1789 to 1946. We did so for all countries with a past or current population of more than one million. One example is Switzerland, which has been classified as a liberal democracy since 1849, even though its government forbade women to vote and stand in elections [until 1971, more than a hundred years later](https://www.parlament.ch/en/%C3%BCber-das-parlament/political-women/conquest-of-equal-rights/women-suffrage). Expressed precisely, V-Dem’s measurement model produces a probability distribution over the country-year scores. The best estimate is the distribution’s median, while the upper and lower bound estimates demarcate the interval in which the model places 68 percent of the probability mass. This and the following section draw on several very helpful other articles summarizing and reviewing some of the leading democracy datasets: Boese, Vanessa. 2019. [How (not) to measure democracy](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Boese%2C+Vanessa.+2019.+How+%28not%29+to+measure+democracy.+International+Area+Studies+Review+22%282%29%3A+95-127&btnG=). International Area Studies Review 22(2): 95-127. Coppedge, Michael, John Gerring, Staffan I. Lindberg, Svend-Erik Skaaning, and Jan Teorell. 2017. [V-Dem Comparisons and Contrasts with Other Measurement Projects](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Coppedge%2C+Michael%2C+John+Gerring%2C+Staffan+I.+Lindberg%2C+Svend-Erik+Skaaning%2C+and+Jan+Teorell.+2017.+V-Dem+Comparisons+and+Contrasts+with+Other+Measurement+Projects.+V-Dem+Working+Paper+45.&btnG=). V-Dem Working Paper 45. Møller, Jørgen and Svend-Erik Skaaning. 2021. [Varieties of Measurement: A Comparative Assessment of Relatively New Democracy Ratings based on Original Data](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=M%C3%B8ller%2C+J%C3%B8rgen+and+Svend-Erik+Skaaning.+2021.+Varieties+of+Measurement%3A+A+Comparative+Assessment+of+Relatively+New+Democracy+Ratings+based+on+Original+Data.+V-Dem+Working+Paper+123.&btnG=). V-Dem Working Paper 123. Skaaning, Svend-Erik. 2018. [Different Types of Data and the Validity of Democracy Measures](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Skaaning%2C+Svend-Erik.+2018.+Different+Types+of+Data+and+the+Validity+of+Democracy+Measures.+Politics+and+Governance+6%281%29%3A+105-116.&btnG=). Politics and Governance 6(1): 105-116. The two most consequential changes we make relate to RoW’s identification of whether a country’s chief executive is elected. One way RoW considers a chief executive to have been elected — even if they are not directly elected or appointed by the legislature — is if they are the head of state, they depend on the approval of the legislature, and there were multi-party elections for the executive. This last part is likely a coding error because to be consistent with RoW's other definitions, this should depend on multi-party legislative, not executive, elections. Only if the legislature has been chosen in multi-party elections does it make an otherwise unelected chief executive—who must be approved by that legislature—dependent on multi-party elections. We correct this error. Furthermore, RoW considers a chief executive to have been elected if the country had chosen both its legislature and executive in multi-party elections. But this considers some chief executives as elected even if they came to power through force after elections were previously held. Examples include the coup d’états led by Fulgencio Batista in Cuba in 1952 and by Muhammadu Buhari in Nigeria in 1983. We instead consider such chief executives as unelected. The overall extent of liberal democracy is based on V-Dem’s liberal component index, which combines expert assessments of citizens’ equality before the law and their individual liberties, as well as legislative and judicial constraints on the executive. “We have run extensive tests on how well such individual-level factors predict country-ratings but have found that the only factor consistently associated with country-ratings is country of origin (with “domestic” experts being harsher in their judgments).” Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan Lindberg, Jan Teorell, Kyle Marquardt, Juraj Medzihorsky, Daniel Pemstein, Nazifa Alizada, Lisa Gastaldi, Garry Hindle, Josefine Pernes, Johannes von Römer, Eitan Tzelgov, Yi-ting Wang, and Steven Wilson. 2021. V-Dem Methodology v11.1. Varieties of Democracy (V-Dem) Project: page 24.",The ‘Regimes of the World’ data: how do researchers measure democracy? 1xK2lZ4tIq9rdwyjD7F4WB9kmhiyK5l1qsH1GjoSN1UI,covid-sweden-death-reporting,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""There are two ways that COVID-19 deaths can be presented over time: by the date of death, or the date on which the death was reported. Neither of these methods is necessarily better than the other—but one should be aware of the difference as it can affect comparisons across countries and over time if these methods are not consistent."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The official data for deaths in Sweden is presented by date of death by "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.folkhalsomyndigheten.se/smittskydd-beredskap/utbrott/aktuella-utbrott/covid-19/statistik-och-analyser/bekraftade-fall-i-sverige/"", ""children"": [{""text"": ""Folkhälsomyndigheten"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", the Swedish Public Health Agency. This matters because it can take many days until all deaths for a particular day are reported in Sweden."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In practice this means that Sweden might today only report 10 deaths for yesterday, but once reporting is complete the death count for that same day might increase to 40."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The death counts for the last 2 weeks in Sweden should therefore always be interpreted as an incomplete count of what occurred in this period."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/sweden-official-covid-deaths?tab=chart&stackMode=absolute&time=earliest..latest®ion=World"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The mortality data presented by the Swedish Public Health Agency evolves over time"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This undercount in recent days means that deaths often appear to be falling; but when this is later completed, data shows that more deaths were occurring during that period. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""This means that for the last 2 weeks of data, death counts in Sweden must only be interpreted as an incomplete measure of mortality."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As an example, this chart shows what confirmed deaths looked like for the period from October 20 to October 29, when the data was first published on October 30 (red series), and once many more death certificates had been added on November 12 (blue series)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/daily-new-confirmed-covid-19-deaths-in-sweden-oct-2020?tab=chart&stackMode=absolute&country=Data%20on%20Nov.%209~Data%20on%20Oct.%2030®ion=World"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One day after October 29, it looked as if deaths had peaked on October 27 and then started to fall, but in reality that’s not what happened over this period. What actually happened is shown by the blue series: deaths increased steadily."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This also means that each day, the Swedish government will add new deaths for multiple days in the past—mostly on recent days, sometimes for a longer period—if they have been reported with a long delay."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is important to know these differences when studying the official data from Sweden, and even more when comparing it with other countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How confirmed deaths are presented in our data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Our source for COVID-19 cases and deaths, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://github.com/CSSEGISandData/COVID-19/"", ""children"": [{""text"": ""the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", updates its figures for Sweden based on the date of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""report"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". More precisely, every morning Johns Hopkins University collects each country's cumulative total number of cases and deaths since the start of the pandemic, and subtracts the previous day's total from it."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This results in a daily figure that corresponds to the number of cases or deaths reported in the last 24 hours—regardless of when these deaths actually happened. This means that if the reported death toll for a country was 20 for a given day, it will remain 20 indefinitely."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Because the Swedish Public Health Agency doesn't report on weekends and national holidays, the resulting series is more irregular, but it doesn't show the same systematic fall for the last 2 weeks as the data presented by date of death, as for every other country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/explorers/coronavirus-data-explorer?zoomToSelection=true&time=latest&country=~SWE&pickerSort=desc&pickerMetric=total_cases&hideControls=true&Metric=Excess+mortality+%28estimates%29&Interval=Cumulative&Relative+to+Population=true&Color+by+test+positivity=false"", ""type"": ""chart"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""article"", ""title"": ""Why do COVID-19 deaths in Sweden's official data always appear to decrease?"", ""authors"": [""Edouard Mathieu""], ""excerpt"": ""There are two ways that COVID-19 deaths can be presented over time: by the date of death, or the date on which the death is reported. The data for Sweden is shown by date of death – this means the most recent points should be treated as incomplete."", ""dateline"": ""November 13, 2020"", ""subtitle"": ""There are two ways that COVID-19 deaths can be presented over time: by the date of death, or the date on which the death is reported. The data for Sweden is shown by date of death – this means the most recent points should be treated as incomplete."", ""sidebar-toc"": false, ""featured-image"": """"}",1,2024-02-21 18:55:24,2020-11-13 09:49:01,2024-02-21 19:04:04,listed,ALBJ4LvhWrxnOX8nQVXcuxFmrBWwnPQKuM_t7cIYkldLOIjBDzeCRL0h2gjSiufQmDjHC48InZl6t7A1yB2R7Q,,"There are two ways that COVID-19 deaths can be presented over time: by the date of death, or the date on which the death was reported. Neither of these methods is necessarily better than the other—but one should be aware of the difference as it can affect comparisons across countries and over time if these methods are not consistent. The official data for deaths in Sweden is presented by date of death by [Folkhälsomyndigheten](https://www.folkhalsomyndigheten.se/smittskydd-beredskap/utbrott/aktuella-utbrott/covid-19/statistik-och-analyser/bekraftade-fall-i-sverige/), the Swedish Public Health Agency. This matters because it can take many days until all deaths for a particular day are reported in Sweden. In practice this means that Sweden might today only report 10 deaths for yesterday, but once reporting is complete the death count for that same day might increase to 40. The death counts for the last 2 weeks in Sweden should therefore always be interpreted as an incomplete count of what occurred in this period. # The mortality data presented by the Swedish Public Health Agency evolves over time This undercount in recent days means that deaths often appear to be falling; but when this is later completed, data shows that more deaths were occurring during that period. **This means that for the last 2 weeks of data, death counts in Sweden must only be interpreted as an incomplete measure of mortality.** As an example, this chart shows what confirmed deaths looked like for the period from October 20 to October 29, when the data was first published on October 30 (red series), and once many more death certificates had been added on November 12 (blue series). One day after October 29, it looked as if deaths had peaked on October 27 and then started to fall, but in reality that’s not what happened over this period. What actually happened is shown by the blue series: deaths increased steadily. This also means that each day, the Swedish government will add new deaths for multiple days in the past—mostly on recent days, sometimes for a longer period—if they have been reported with a long delay. It is important to know these differences when studying the official data from Sweden, and even more when comparing it with other countries. # How confirmed deaths are presented in our data Our source for COVID-19 cases and deaths, [the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University](https://github.com/CSSEGISandData/COVID-19/), updates its figures for Sweden based on the date of _report_. More precisely, every morning Johns Hopkins University collects each country's cumulative total number of cases and deaths since the start of the pandemic, and subtracts the previous day's total from it. This results in a daily figure that corresponds to the number of cases or deaths reported in the last 24 hours—regardless of when these deaths actually happened. This means that if the reported death toll for a country was 20 for a given day, it will remain 20 indefinitely. Because the Swedish Public Health Agency doesn't report on weekends and national holidays, the resulting series is more irregular, but it doesn't show the same systematic fall for the last 2 weeks as the data presented by date of death, as for every other country. ",Why do COVID-19 deaths in Sweden's official data always appear to decrease? 1wzlYx1IUYEJNooOrMlw8frYUKrYmw-bvJ1bCftzd4fA,obesity,linear-topic-page,"{""toc"": [{""slug"": ""what-is-obesity"", ""text"": ""What is obesity?"", ""title"": ""What is obesity?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""obesity-is-commonly-measured-using-the-body-mass-index-bmi-scale"", ""text"": ""Obesity is commonly measured using the Body Mass Index (BMI) scale"", ""title"": ""Obesity is commonly measured using the Body Mass Index (BMI) scale"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""obesity-is-one-of-the-leading-risk-factors-for-early-death"", ""text"": ""Obesity is one of the leading risk factors for early death"", ""title"": ""Obesity is one of the leading risk factors for early death"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""obesity-is-responsible-for-millions-of-premature-deaths-each-year"", ""text"": ""Obesity is responsible for millions of premature deaths each year"", ""title"": ""Obesity is responsible for millions of premature deaths each year"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""the-global-distribution-of-health-impacts-from-obesity"", ""text"": ""The global distribution of health impacts from obesity"", ""title"": ""The global distribution of health impacts from obesity"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-share-of-global-deaths-are-the-result-of-obesity"", ""text"": ""What share of global deaths are the result of obesity?"", ""title"": ""What share of global deaths are the result of obesity?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""there-is-a-large-difference-in-death-rates-from-obesity-across-the-world"", ""text"": ""There is a large difference in death rates from obesity across the world"", ""title"": ""There is a large difference in death rates from obesity across the world"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""what-share-of-adults-are-obese"", ""text"": ""What share of adults are obese?"", ""title"": ""What share of adults are obese?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""obesity-varies-widely-worldwide-and-has-become-more-common"", ""text"": ""Obesity varies widely worldwide and has become more common"", ""title"": ""Obesity varies widely worldwide and has become more common"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""what-share-of-adults-are-overweight"", ""text"": ""What share of adults are overweight?"", ""title"": ""What share of adults are overweight?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""body-mass-index-bmi"", ""text"": ""Body Mass Index (BMI)"", ""title"": ""Body Mass Index (BMI)"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""mean-bmi-in-adult-women"", ""text"": ""Mean BMI in adult women"", ""title"": ""Mean BMI in adult women"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""mean-bmi-in-adult-men"", ""text"": ""Mean BMI in adult men"", ""title"": ""Mean BMI in adult men"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""childhood-obesity"", ""text"": ""Childhood obesity"", ""title"": ""Childhood obesity"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""share-of-children-that-are-overweight"", ""text"": ""Share of children that are overweight"", ""title"": ""Share of children that are overweight"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""what-are-the-drivers-of-obesity"", ""text"": ""What are the drivers of obesity?"", ""title"": ""What are the drivers of obesity?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""daily-supply-of-calories"", ""text"": ""Daily supply of calories"", ""title"": ""Daily supply of calories"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""is-bmi-an-appropriate-measure-of-weight-related-health"", ""text"": ""Is BMI an appropriate measure of weight-related health?"", ""title"": ""Is BMI an appropriate measure of weight-related health?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""all-charts"", ""text"": ""Interactive charts on Obesity"", ""title"": ""Interactive charts on Obesity"", ""isSubheading"": false}, {""slug"": ""article-endnotes"", ""text"": ""Endnotes"", ""title"": ""Endnotes"", ""isSubheading"": false}, {""slug"": ""article-citation"", ""text"": ""Citation"", ""title"": ""Citation"", ""isSubheading"": false}, {""slug"": ""article-licence"", ""text"": ""Licence"", ""title"": ""Licence"", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Obesity is a major risk factor for a range of diseases, including heart disease, stroke, diabetes, and various types of cancer."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is most commonly measured using the body mass index (BMI) scale."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On this page, you will find global data and research on obesity — its prevalence, drivers, health consequences, and trends over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""url"": ""#all-charts"", ""children"": [{""text"": ""See all interactive charts on Obesity ↓"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Related topics:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/14eHWSP57fDnIsbKVIkpEFf2nFlXP_egrcdIDp881QfM/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/hunger-and-undernourishment"", ""type"": ""prominent-link"", ""title"": ""Hunger and Undernourishment"", ""description"": ""How does undernourishment vary across the world? How has it changed over time?"", ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/13s3a_bgPT8_VRu50K1pf9Nc7zhu-IyxqYzUDR95NCRw/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Other research and writing on obesity on Our World in Data:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/obesity-definition"", ""children"": [{""text"": ""What is obesity and how is it measured?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""What is obesity?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Obesity is commonly measured using the 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are used to define whether an individual is considered to be underweight, healthy, overweight, or obese."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In adults, the WHO defines these categories using the cut-off points: an individual with a BMI between 25 and 30 is considered \""overweight\"", while a BMI greater than 30 is defined as \""obese\""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" However, different cut-off points are used for other groups, such as children and pregnant women."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Read more in our article:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1lcBQGkqIduZ_A-BLLfW5ejD-YK2mCI3CtDrim0Hjjp4/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""text"": [{""text"": ""Obesity is one of the leading risk factors for early death"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Obesity is responsible for millions of premature deaths each year"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Obesity is one of the world's largest health problems — one that has shifted from being a problem in rich countries to a health challenge around the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Global Burden of Disease"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" is a major global study on the causes and "", ""spanType"": ""span-simple-text""}, {""id"": ""risk-factor"", ""children"": [{""text"": ""risk factors"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" for death and disease."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The authors of the study have estimated the annual number of deaths attributed to a wide range of risk factors, as you can see below."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Obesity — defined as having a high body mass index — is a risk factor for several of the world's leading causes of death, including heart disease, stroke, diabetes, and various types of cancer."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As you can see, it’s estimated that around 5 million people died prematurely in 2019 as a result of obesity, which makes it one of the leading causes of death worldwide."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Read more in our article:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1iH_m2GlsBuif80sDwfg0fNGZmpf9X0-TFM5oHQr9fPA/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/number-of-deaths-by-risk-factor"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The global distribution of health impacts from obesity"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""What share of global deaths are the result of obesity?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Globally, it’s estimated that almost 10% in 2019 resulted from the consequences of obesity — this was almost double the share in 1990."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This share varies significantly across the world. In the map here we see the share of deaths attributed to obesity across countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Across many middle-income countries — such as in Eastern Europe, Central Asia, North Africa, and Latin America — more than 15% of deaths were attributed to obesity in 2019."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This results from both a high prevalence of obesity, as well as poorer overall health and healthcare systems compared to high-income countries with similarly high levels of obesity."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 2019, in most high-income countries, the share of deaths attributed to obesity was in the range of 8 to 10%. In many middle-income countries, this share was almost twice as high."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In contrast, across several low-income countries — especially across Sub-Saharan Africa — it’s estimated that obesity accounts for under 5% of deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-deaths-obesity"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""There is a large difference in death rates from obesity across the world"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Death rates from obesity can also help us understand differences in the impact of obesity between countries and over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the map here you can see differences in death rates from obesity across the world, per 100,000 people in the population."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Death rates tend to be higher in Eastern Europe, Central Asia, North Africa, and Latin America."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In contrast, they tend to be much lower in Western Europe, Australia, and East Asia."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When we look at the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/death-rate-vs-share-obesity"", ""children"": [{""text"": ""relationship between"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" death rates and the prevalence of obesity we find a positive one: death rates tend to be higher in countries where more people are obese."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But what we also notice is that for a given prevalence of obesity, death rates can vary by a factor of four. While around a quarter of people in Russia and Norway are obese, death rates in Russia are much higher."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It's not "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""only"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" the prevalence of obesity that plays a role but also other factors — such as underlying health, other confounding risk factors (such as "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/alcohol-consumption"", ""children"": [{""text"": ""alcohol"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/drug-use"", ""children"": [{""text"": ""drugs"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/smoking"", ""children"": [{""text"": ""smoking"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and other lifestyle factors), and healthcare systems."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/death-rate-from-obesity"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""What share of adults are obese?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Obesity varies widely worldwide and has become more common"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart here, we see the share of adults (aged 18 years and older) who are obese across regions. These estimates are based on survey data and statistical modeling by the World Health Organization (WHO)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Overall we see a pattern roughly in line with prosperity: the prevalence of obesity tends to be higher in richer countries across Europe, North America, and Oceania. Obesity rates tend to be much lower across South Asia and Sub-Saharan Africa."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""More than a third of adults in the United States were obese in 2016. In countries such as India and Nigeria, that share was far lower."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart also shows the share of adults who are obese has grown over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-adults-defined-as-obese?tab=chart"", ""type"": ""chart"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/obesity-in-men-vs-obesity-in-women"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""text"": [{""text"": ""What share of adults are overweight?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Globally, it’s estimated that around two-fifths of adults were overweight or obese in 2016."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The classification of \""overweight\"" "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/obesity-definition"", ""children"": [{""text"": ""is also defined"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" based on the body-mass index — it refers to BMI values between 25 and 30."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the map here we see the share of adults who are overweight or obese across countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As you can see, the share of people who are overweight tends to be higher in richer countries and lower in poorer countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In many high-income countries such as the United States, it’s estimated that over 60% of adults are overweight or obese."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In contrast, across South Asia and Sub-Saharan Africa, it’s estimated that around one in five adults are overweight or obese."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-females-defined-as-overweight"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-adults-who-are-overweight"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Body Mass Index (BMI)"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Mean BMI in adult women"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the map, here we see the distribution of average (mean) BMI in adult women across the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 2016, the global average (mean) BMI in women was around 25, which is the cut-off for overweight. The average BMI tends to be higher in North and South America and North Africa, and lower in sub-Saharan Africa and Asia."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/mean-body-mass-index-bmi-in-adult-women"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Mean BMI in adult men"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the map here we see the distribution of mean BMI for adult men across the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 2016, the global average (mean) BMI in men was estimated to be around 25, which is the cut-off for overweight. The average BMI tends to be higher in North and South America and North Africa, and lower in sub-Saharan Africa and Asia."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/mean-body-mass-index-bmi-in-adult-males"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Childhood obesity"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Share of children that are overweight"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Obesity and overweight in children are also measured based on body mass index (BMI)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, BMI scores are "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/obesity-definition"", ""children"": [{""text"": ""interpreted differently"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" for children and adolescents."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Weight categories are defined by comparing weight to the WHO Growth Standards — a child is defined as overweight if their weight-for-height is more than two standard deviations from the median of the WHO Child Growth Standards."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/children-who-are-overweight-sdgs"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""What are the drivers of obesity?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At a basic level, weight gain — eventually leading to being overweight or obesity — is determined by a balance of energy."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When we consume more energy — typically measured in calories — than the energy expended to maintain life and carry out daily activities, we gain weight. This is called an “energy surplus”. When we consume less energy than we expend, we lose weight — this is an “energy deficit”."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This means there are two potential drivers of the increase in obesity rates in recent decades — either we eat more (an increase in calorie intake) or we expend less energy in our daily life through lower activity levels. Both elements are likely to play a role in the rise in obesity."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To tackle obesity, interventions that address both energy intake and expenditure are important."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""Daily supply of calories"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Over the past century — but particularly over the past 50 years — the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/food-per-person"", ""children"": [{""text"": ""supply of calories"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" has increased across the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the 1960s, the global "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/daily-per-capita-caloric-supply?tab=chart&country=Asia~Europe~North+America~South+America~Africa"", ""children"": [{""text"": ""average supply of calories"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (that is, the availability of calories for people to eat) was around 2,200 kcal per person per day. By 2013, this had increased to 2800kcal."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Across most countries, energy consumption has therefore increased. Without an increase in energy expenditure, weight gain and obesity tend to rise."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart here, we see the relationship between the share of men who are overweight or obese versus the daily average supply of kilocalories per person."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Overall there is a strong positive relationship: countries with higher rates of overweight tend to have a higher supply of calories."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you press \""play\"" on the interactive timeline you can see how this has changed for each country over time. 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Available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://knoema.com/WHOGDOBMIMay/who-global-database-on-body-mass-index-bmi"", ""children"": [{""text"": ""online"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e487b65181dfc5ba1e23737a25dd93b5b1f75435"": {""id"": ""e487b65181dfc5ba1e23737a25dd93b5b1f75435"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For example, an adult who weighs 70kg and whose height is 1.75m will have a BMI of 22.9. This is calculated as 70kg / 1.75"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}, {""text"": "" = 70 / 3.06 = 22.9"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""linear-topic-page"", ""title"": ""Obesity"", ""authors"": [""Hannah Ritchie"", ""Max Roser""], ""excerpt"": ""When did obesity increase? How do rates vary across the world? What is the health impact?"", ""dateline"": ""This page was first published in August 2017 and last revised in January 2024."", ""subtitle"": ""When did obesity increase? How do rates vary across the world? What is the health impact?"", ""sidebar-toc"": true, ""featured-image"": ""obesity-thumbnail.png""}",1,2023-11-10 14:20:48,2017-08-17 13:21:34,2024-02-29 11:06:20,unlisted,ALBJ4LvACZOZGizGMjUoqGu3aDk0hUYbqvtiqLWJVhWcHVfXV-iQozXk-U_VYH2oTzlgwA462QiaxUVocgfwAA,,"Obesity is a major risk factor for a range of diseases, including heart disease, stroke, diabetes, and various types of cancer. It is most commonly measured using the body mass index (BMI) scale. On this page, you will find global data and research on obesity — its prevalence, drivers, health consequences, and trends over time. **[See all interactive charts on Obesity ↓](#all-charts)** **Related topics:** ### undefined undefined https://docs.google.com/document/d/14eHWSP57fDnIsbKVIkpEFf2nFlXP_egrcdIDp881QfM/edit ### Hunger and Undernourishment How does undernourishment vary across the world? How has it changed over time? https://ourworldindata.org/hunger-and-undernourishment ### undefined undefined https://docs.google.com/document/d/13s3a_bgPT8_VRu50K1pf9Nc7zhu-IyxqYzUDR95NCRw/edit **Other research and writing on obesity on Our World in Data:** * [What is obesity and how is it measured?](https://ourworldindata.org/obesity-definition) # What is obesity? ## Obesity is commonly measured using the Body Mass Index (BMI) scale Obesity can be measured in different ways, but the most common is the Body Mass Index (BMI) scale, which is calculated based on a person’s height and weight. BMI is defined as their weight in kilograms divided by the square of their height in meters (kg/m2).1 BMI values are used to define whether an individual is considered to be underweight, healthy, overweight, or obese. In adults, the WHO defines these categories using the cut-off points: an individual with a BMI between 25 and 30 is considered ""overweight"", while a BMI greater than 30 is defined as ""obese"".2 However, different cut-off points are used for other groups, such as children and pregnant women. Read more in our article: ### undefined undefined https://docs.google.com/document/d/1lcBQGkqIduZ_A-BLLfW5ejD-YK2mCI3CtDrim0Hjjp4/edit # Obesity is one of the leading risk factors for early death ## Obesity is responsible for millions of premature deaths each year Obesity is one of the world's largest health problems — one that has shifted from being a problem in rich countries to a health challenge around the world. The _Global Burden of Disease_ is a major global study on the causes and risk factors for death and disease.3 The authors of the study have estimated the annual number of deaths attributed to a wide range of risk factors, as you can see below. Obesity — defined as having a high body mass index — is a risk factor for several of the world's leading causes of death, including heart disease, stroke, diabetes, and various types of cancer.4 As you can see, it’s estimated that around 5 million people died prematurely in 2019 as a result of obesity, which makes it one of the leading causes of death worldwide. Read more in our article: ### undefined undefined https://docs.google.com/document/d/1iH_m2GlsBuif80sDwfg0fNGZmpf9X0-TFM5oHQr9fPA/edit # The global distribution of health impacts from obesity ## What share of global deaths are the result of obesity? Globally, it’s estimated that almost 10% in 2019 resulted from the consequences of obesity — this was almost double the share in 1990. This share varies significantly across the world. In the map here we see the share of deaths attributed to obesity across countries. Across many middle-income countries — such as in Eastern Europe, Central Asia, North Africa, and Latin America — more than 15% of deaths were attributed to obesity in 2019. This results from both a high prevalence of obesity, as well as poorer overall health and healthcare systems compared to high-income countries with similarly high levels of obesity. In 2019, in most high-income countries, the share of deaths attributed to obesity was in the range of 8 to 10%. In many middle-income countries, this share was almost twice as high. In contrast, across several low-income countries — especially across Sub-Saharan Africa — it’s estimated that obesity accounts for under 5% of deaths. ## There is a large difference in death rates from obesity across the world Death rates from obesity can also help us understand differences in the impact of obesity between countries and over time. In the map here you can see differences in death rates from obesity across the world, per 100,000 people in the population. Death rates tend to be higher in Eastern Europe, Central Asia, North Africa, and Latin America. In contrast, they tend to be much lower in Western Europe, Australia, and East Asia. When we look at the [relationship between](https://ourworldindata.org/grapher/death-rate-vs-share-obesity) death rates and the prevalence of obesity we find a positive one: death rates tend to be higher in countries where more people are obese. But what we also notice is that for a given prevalence of obesity, death rates can vary by a factor of four. While around a quarter of people in Russia and Norway are obese, death rates in Russia are much higher. It's not _only_ the prevalence of obesity that plays a role but also other factors — such as underlying health, other confounding risk factors (such as [alcohol](https://ourworldindata.org/alcohol-consumption), [drugs](https://ourworldindata.org/drug-use), [smoking](https://ourworldindata.org/smoking), and other lifestyle factors), and healthcare systems. # What share of adults are obese? ## Obesity varies widely worldwide and has become more common In the chart here, we see the share of adults (aged 18 years and older) who are obese across regions. These estimates are based on survey data and statistical modeling by the World Health Organization (WHO). Overall we see a pattern roughly in line with prosperity: the prevalence of obesity tends to be higher in richer countries across Europe, North America, and Oceania. Obesity rates tend to be much lower across South Asia and Sub-Saharan Africa. More than a third of adults in the United States were obese in 2016. In countries such as India and Nigeria, that share was far lower. The chart also shows the share of adults who are obese has grown over time. ### undefined undefined https://ourworldindata.org/grapher/obesity-in-men-vs-obesity-in-women # What share of adults are overweight? Globally, it’s estimated that around two-fifths of adults were overweight or obese in 2016.4 The classification of ""overweight"" [is also defined](https://ourworldindata.org/obesity-definition) based on the body-mass index — it refers to BMI values between 25 and 30. In the map here we see the share of adults who are overweight or obese across countries. As you can see, the share of people who are overweight tends to be higher in richer countries and lower in poorer countries. In many high-income countries such as the United States, it’s estimated that over 60% of adults are overweight or obese. In contrast, across South Asia and Sub-Saharan Africa, it’s estimated that around one in five adults are overweight or obese. ### undefined undefined https://ourworldindata.org/grapher/share-of-females-defined-as-overweight # Body Mass Index (BMI) ## Mean BMI in adult women In the map, here we see the distribution of average (mean) BMI in adult women across the world. In 2016, the global average (mean) BMI in women was around 25, which is the cut-off for overweight. The average BMI tends to be higher in North and South America and North Africa, and lower in sub-Saharan Africa and Asia. ## Mean BMI in adult men In the map here we see the distribution of mean BMI for adult men across the world. In 2016, the global average (mean) BMI in men was estimated to be around 25, which is the cut-off for overweight. The average BMI tends to be higher in North and South America and North Africa, and lower in sub-Saharan Africa and Asia. # Childhood obesity ## Share of children that are overweight Obesity and overweight in children are also measured based on body mass index (BMI). However, BMI scores are [interpreted differently](https://ourworldindata.org/obesity-definition) for children and adolescents. Weight categories are defined by comparing weight to the WHO Growth Standards — a child is defined as overweight if their weight-for-height is more than two standard deviations from the median of the WHO Child Growth Standards.4 # What are the drivers of obesity? At a basic level, weight gain — eventually leading to being overweight or obesity — is determined by a balance of energy.5 When we consume more energy — typically measured in calories — than the energy expended to maintain life and carry out daily activities, we gain weight. This is called an “energy surplus”. When we consume less energy than we expend, we lose weight — this is an “energy deficit”. This means there are two potential drivers of the increase in obesity rates in recent decades — either we eat more (an increase in calorie intake) or we expend less energy in our daily life through lower activity levels. Both elements are likely to play a role in the rise in obesity. To tackle obesity, interventions that address both energy intake and expenditure are important.6 ## Daily supply of calories Over the past century — but particularly over the past 50 years — the [supply of calories](https://ourworldindata.org/food-per-person) has increased across the world. In the 1960s, the global [average supply of calories](https://ourworldindata.org/grapher/daily-per-capita-caloric-supply?tab=chart&country=Asia~Europe~North+America~South+America~Africa) (that is, the availability of calories for people to eat) was around 2,200 kcal per person per day. By 2013, this had increased to 2800kcal. Across most countries, energy consumption has therefore increased. Without an increase in energy expenditure, weight gain and obesity tend to rise. In the chart here, we see the relationship between the share of men who are overweight or obese versus the daily average supply of kilocalories per person. Overall there is a strong positive relationship: countries with higher rates of overweight tend to have a higher supply of calories. If you press ""play"" on the interactive timeline you can see how this has changed for each country over time. Countries tend to move upwards and to the right: the supply of calories has increased as obesity rates have increased. ## Is BMI an appropriate measure of weight-related health? The merits of using BMI as an indicator of body fat and obesity are contested. A key contention to the use of BMI indicators is that it is a measure of body mass/weight rather than providing a direct measure of body fat. Whilst physicians continue to use BMI as a general indicator of weight-related health risks, there are some cases where its use should be considered more carefully, which are listed below, and physicians are recommended to evaluate BMI results carefully on an individual basis.7 * Muscle mass can increase body weight; this means athletes or individuals with a high muscle mass percentage can be deemed overweight on the BMI scale, even if they have a low or healthy body fat percentage; * Muscle and bone density tends to decline as we get older; this means that an older individual may have a higher percentage body fat than a younger individual with the same BMI; * Women tend to have a higher body fat percentage than men for a given BMI. Read more in our article: ### undefined undefined https://docs.google.com/document/d/1lcBQGkqIduZ_A-BLLfW5ejD-YK2mCI3CtDrim0Hjjp4/edit For example, an adult who weighs 70kg and whose height is 1.75m will have a BMI of 22.9. This is calculated as 70kg / 1.752 = 70 / 3.06 = 22.9 World Health Organization. BMI Classification. _Global Database on Body Mass Index_. Available [online](https://knoema.com/WHOGDOBMIMay/who-global-database-on-body-mass-index-bmi). The latest study can be found at the website of the Lancet here: [TheLancet.com/GBD](https://www.thelancet.com/gbd) The 2017 study was published as GBD 2017 Risk Factor Collaborators — ""Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017"" and is online [here](http://www.healthdata.org/research-article/global-regional-and-national-comparative-risk-assessment-84-behavioral-0). WHO (2018) — Fact sheet — Obesity and overweight. Updated February 2018. Online [here](https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight). Hall, K. D., Heymsfield, S. B., Kemnitz, J. W., Klein, S., Schoeller, D. A., & Speakman, J. R. (2012). Energy balance and its components: implications for body weight regulation. _The American Journal of Clinical Nutrition_, _95_(4), 989-994. Available [online](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3302369/). Hill, J. O., Wyatt, H. R., & Peters, J. C. (2012). [Energy balance and obesity](https://www.ahajournals.org/doi/full/10.1161/circulationaha.111.087213). _Circulation_, _126_(1), 126-132. Centers for Disease Control and Prevention. Body Mass Index: Considerations for Practitioners. Available [online](https://www.cdc.gov/obesity/downloads/bmiforpactitioners.pdf).",Obesity 1wyS9z-8SsNcezqd91TWO4U_Dn1dQOzAczukfjkF7taA,lead-pollution,topic-page,"{""toc"": [], ""body"": [{""type"": ""topic-page-intro"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Lead poisoning is estimated to account for about 1% of the global disease burden."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is a large burden for a problem that gets very little attention."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On an individual level, being exposed to lead in the environment can hinder a child’s brain development: it can result in a reduction in IQ; cognitive function; and has been linked to higher levels of antisocial behavior."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But it's a global problem that we can tackle. 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(2005) Low-Level Environmental Lead Exposure and Children’s Intellectual Function: An International Pooled Analysis. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Environmental Health Perspectives"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Ericson, B., Dowling, R., Dey, S., Caravanos, J., Mishra, N., Fisher, S., … & Fuller, R. (2018). A meta-analysis of blood lead levels in India and the attributable burden of disease. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Environment International"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", 121, 461-470."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""44baaf0bcb650a14473ece130f85b34f5d72b513"": {""id"": ""44baaf0bcb650a14473ece130f85b34f5d72b513"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The Institute for Health Metrics’ "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ghdx.healthdata.org/gbd-results-tool"", ""children"": [{""text"": ""Global Burden of Disease"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" study estimates that in 2019, lead exposure was responsible for just over 0.9% of global disability-adjusted life years (DALYs)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""topic-page"", ""title"": ""Lead Pollution"", ""authors"": [""Hannah Ritchie"", ""Max Roser""], ""excerpt"": ""Lead pollution is a widespread problem that receives little attention. What is the scale of the problem and how can we tackle it?"", ""dateline"": ""January 11, 2022"", ""subtitle"": ""Lead pollution is a widespread problem that receives little attention. 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Due to data availability, the article and charts will not be updated."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Note"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Facebook, the largest social media platform in the world, had 2.4 billion users in 2019. Other social media platforms, including YouTube and WhatsApp, also had over one billion users each."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These numbers are huge – in 2019, there were "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/world-population-by-world-regions-post-1820"", ""children"": [{""text"": ""7.7 billion people"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" worldwide, with at least "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/number-of-internet-users-by-country?tab=chart&time=1990..2016&country=OWID_WRL"", ""children"": [{""text"": ""3.5 billion online"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". This means social media platforms were used by one in three people worldwide and more than two-thirds of all Internet users."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Social media has changed the world. The rapid and vast adoption of these technologies is changing how we "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.pnas.org/content/116/36/17753"", ""children"": [{""text"": ""find partners"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", access information from the news, and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.twitterandteargas.org/"", ""children"": [{""text"": ""organize to demand political change"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Who uses social media? When did the rise of social media start, and how has the number of users changed over time? Here we answer these and other key questions to understand the history of social media worldwide."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We begin with an outline of key trends and conclude with a perspective on the social media adoption rate relative to other modern communication technologies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Social media started in the early 2000s"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""MySpace was the first social media site to reach a million monthly active users – it achieved this milestone around 2004. This is arguably the beginning of social media as we know it."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, we plot monthly active users across various platforms since 2004."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Some large social media sites, such as Facebook, YouTube, and Reddit, have been around for ten or more years, but others are much newer."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""TikTok, for example, launched in September 2016, and by mid-2018, it had already reached half a billion users. To put this in perspective: TikTok gained, on average, about 20 million new users per month over this period."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The data also shows rapid changes in the opposite direction. Once-dominant platforms have disappeared. In 2008, Hi5, MySpace, and Friendster were close competitors to Facebook, yet by 2012 they had virtually no market share. The case of MySpace is remarkable, considering that in 2006 it "", ""spanType"": ""span-simple-text""}, {""url"": ""https://mashable.com/2006/07/11/myspace-americas-number-one/"", ""children"": [{""text"": ""temporarily surpassed Google"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" as the most visited website in the US."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most social media platforms that survived the last decade have shifted significantly in what they offer users. Twitter, for example, didn’t allow users to upload videos or images initially. Since 2011 this has been possible, and today, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.bondcap.com/report/itr19/#view/78"", ""children"": [{""text"": ""more than 50%"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of the content viewed on Twitter includes images and videos."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""social-media-users-over-time.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""Facebook dominated the social media market for a decade, but five other platforms also have more than half a billion users"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""With 2.3 billion users, Facebook was the most popular social media platform in 2019. YouTube, Instagram, and WeChat followed, with over a billion users. Tumblr and TikTok came next, with over half a billion users."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The bar chart shows a ranking of the top social media platforms."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""social-media-users-by-platform.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""Some social media sites are particularly popular among specific population groups"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The aggregate numbers mask a great deal of heterogeneity across platforms. Some social media sites are much more popular than others among specific population groups."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In general, young people are more likely to use social media than older people. But some platforms are much more popular among younger people. This is shown in the chart where we plot the breakdown of social media use by age group in the US."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For Snapchat and Instagram, the ‘age gradient’ is exceptionally steep – the popularity of these platforms drops much faster with age. Most people under 25 use Snapchat (73%), while only 3% of people over 65 use it."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since these platforms are relatively new, it’s hard to know how much of this age gradient results from a “cohort effect”. In other words: it’s unclear whether today’s young people will continue using Snapchat as they age. If they do, the age gradient will narrow."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""Use-of-social-media-by-age.png"", ""hasOutline"": false, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Let’s now look at gender differences."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart shows the percentage of men and women that used different platforms in the US in 2021—the diagonal line marks parity. Sites above the diagonal line are more popular among women, and those below are more popular among men."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For some platforms, the gender differences are substantial. The share of women who used Pinterest was 3 times as high as that of men using this platform. For Reddit, it was the other way around: the share of men was twice as high."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""adults-using-social-media-by-gender.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""In rich countries, almost all young people use social media"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""From a back-of-the-envelope calculation, we know that if Facebook had 2.3 billion users in 2019, then at least 30% of the world was using social media."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This is just an average – usage rates were much higher for some world regions, specifically for some population groups."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Young people tend to use social media more frequently. In fact, in rich countries where access to the Internet is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-individuals-using-the-internet"", ""children"": [{""text"": ""nearly universal"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", the vast majority of young adults use it."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Our chart shows the proportion of people aged 16 to 24 who used social networks across various countries. As we can see, the average for the OECD is close to 90%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If today’s young adults continue using social media throughout their lives, then it’s likely that social media will continue growing rapidly as Internet adoption "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/internet#growth-of-the-internet"", ""children"": [{""text"": ""expands throughout lower-income countries"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""share-of-young-people-networking-online.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""The rise of social media in rich countries has come together with an increase in the amount of time spent online"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The increase in social media use over the last decade has, of course, come together with a large increase in the amount of time people spend online."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the US, adults spend more than 6 hours daily on digital media (apps and websites accessed through mobile phones, tablets, computers, and other connected devices such as game consoles). As the chart shows, this growth has been driven almost entirely by additional time spent on smartphones and tablets."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""daily-hours-spent-with-digital-media-per-adult-user.png"", ""hasOutline"": false, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""According to a survey from the Pew Research Center, adults aged 18 to 29 in the US are "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.pewresearch.org/fact-tank/2018/12/10/social-media-outpaces-print-newspapers-in-the-u-s-as-a-news-source/"", ""children"": [{""text"": ""more likely"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" to get news indirectly via social media than directly from print newspapers or news sites. They also report being "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.pewresearch.org/fact-tank/2019/07/25/americans-going-online-almost-constantly/"", ""children"": [{""text"": ""online “almost constantly”"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Evidence shows that in other rich countries, people also spend many hours per day online. The following chart shows how many hours young people spend online across various rich countries. As we can see, the average for the OECD is more than 4 hours per day; in some countries, the average is above 6 hours per day."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""daily-time-online-by-young-people.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""Some perspective on how fast and profound these rapid changes are"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The percentage of US adults who use social media increased from 5% in 2005 to 79% in 2019. Even on a global stage, the speed of diffusion is striking: Facebook surged from covering around 1.5% of the world population in 2008 to around 30% in 2018."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How does this compare to the diffusion of other communication technologies in today's everyday life?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following chart provides some perspective."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Social media’s growth in the US is comparable – in speed and, to some extent, reach – to most modern communication-enabling technologies, including computers, smartphones, and the Internet."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The rise of social media is an extraordinary example of how quickly and drastically social behaviors can change: Something that is today part of the everyday life of one-third of the world population was unthinkable less than a generation ago."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Rapid changes like those brought about by social media always spark fears about possible negative effects. Specifically, in the context of social media, a key question is whether these new communication technologies are harming our mental health – this is an important question and we cover the evidence in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/social-media-wellbeing"", ""children"": [{""text"": ""another article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" on Our World in Data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/technology-adoption-by-households-in-the-united-states?time=1930..2019&country=Automobile+Cellular%20phone+Colour%20TV+Computer+Ebook%20reader+Internet+Podcasting+Radio+Smartphone%20usage+Social%20media%20usage+Tablet"", ""type"": ""chart"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""28047329d362d4e27dd9b17dda0edf1adf2d2aeb"": {""id"": ""28047329d362d4e27dd9b17dda0edf1adf2d2aeb"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The US social media adoption data is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/technology-adoption-by-households-in-the-united-states?time=2005..2019&country=Social%20media%20usage"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Regarding Facebook’s global numbers: In 2018, Facebook had 2.26 billion users, and in 2008 it had 100 million; the world population in 2008 was 6.8 billion, and in 2018 it was 7.63 billion (you can check the population data "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/world-population-by-world-regions-post-1820"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "".)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""5235028be5c2a85435788c9cda639d0e25c19820"": {""id"": ""5235028be5c2a85435788c9cda639d0e25c19820"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""According to the survey from Pew Research, 36% of adults 18 to 29 in the US say they ‘often get news via social media,’ which is higher than the share saying they ‘often get news via other platforms,’ such as news sites, TV, radio or print newspapers. From the same survey, we also know that 48% of adults 18 to 29 say they go online almost constantly, and 46% say they go online multiple times daily."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""6bcb82d3f24969a68a28d6139dac4c82ea985e9f"": {""id"": ""6bcb82d3f24969a68a28d6139dac4c82ea985e9f"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""There were, of course, earlier, much smaller predecessors of social networking websites. The first recognizable social media site, in the format we know today, was "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/SixDegrees.com"", ""children"": [{""text"": ""Six Degrees"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – a platform created in 1997 that enabled users to upload a profile and make friends with other users. At the core, the features that define a social media platform are (i) profiles for users, (ii) the ability for users to upload content constantly, and (iii) the ability for users to discuss content and connect with other users."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7d3eb2a2dd9dc2c443a16c58a1c2ed2070051a75"": {""id"": ""7d3eb2a2dd9dc2c443a16c58a1c2ed2070051a75"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Digital media contrasts with print media (including books, newspapers, and magazines) and other traditional or analog media (including TV, movies, and radio)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""abc3850baccfdcd447ffceaa058776476ce9868a"": {""id"": ""abc3850baccfdcd447ffceaa058776476ce9868a"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""To be precise, Facebook had 2.3 billion ‘active users.’ There may be some discrepancies between the number of ‘active users’ and the number of people since one person could, in theory, maintain multiple accounts. In practice, these discrepancies are likely small because most social media platforms, including Facebook, have "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.facebook.com/help/975828035803295"", ""children"": [{""text"": ""policies and checks"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" to avoid multiple accounts per person."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""The rise of social media"", ""byline"": ""Esteban Ortiz-Ospina"", ""authors"": [""Esteban Ortiz-Ospina""], ""excerpt"": ""Social media sites are used by more than two-thirds of Internet users. How has social media grown over time?"", ""dateline"": ""September 18, 2019"", ""subtitle"": ""Social media sites are used by more than two-thirds of Internet users. How has social media grown over time?"", ""featured-image"": ""Rise-social-media-thumbnail.png""}",1,2023-03-13 12:30:28,2019-09-18 09:00:44,2024-03-18 15:41:59,listed,ALBJ4LsBoJwnGujQ4tWSTQv9-3Cd-WmOvnW8cnaV4Vta6zme_wH2faTSRnAPdJdHtaVQhvYEEcaTBTMgKiLs9Q,," Facebook, the largest social media platform in the world, had 2.4 billion users in 2019. Other social media platforms, including YouTube and WhatsApp, also had over one billion users each. These numbers are huge – in 2019, there were [7.7 billion people](https://ourworldindata.org/grapher/world-population-by-world-regions-post-1820) worldwide, with at least [3.5 billion online](https://ourworldindata.org/grapher/number-of-internet-users-by-country?tab=chart&time=1990..2016&country=OWID_WRL). This means social media platforms were used by one in three people worldwide and more than two-thirds of all Internet users. Social media has changed the world. The rapid and vast adoption of these technologies is changing how we [find partners](https://www.pnas.org/content/116/36/17753), access information from the news, and [organize to demand political change](https://www.twitterandteargas.org/). Who uses social media? When did the rise of social media start, and how has the number of users changed over time? Here we answer these and other key questions to understand the history of social media worldwide. We begin with an outline of key trends and conclude with a perspective on the social media adoption rate relative to other modern communication technologies. ## Social media started in the early 2000s MySpace was the first social media site to reach a million monthly active users – it achieved this milestone around 2004. This is arguably the beginning of social media as we know it.1 In the chart, we plot monthly active users across various platforms since 2004. Some large social media sites, such as Facebook, YouTube, and Reddit, have been around for ten or more years, but others are much newer. TikTok, for example, launched in September 2016, and by mid-2018, it had already reached half a billion users. To put this in perspective: TikTok gained, on average, about 20 million new users per month over this period. The data also shows rapid changes in the opposite direction. Once-dominant platforms have disappeared. In 2008, Hi5, MySpace, and Friendster were close competitors to Facebook, yet by 2012 they had virtually no market share. The case of MySpace is remarkable, considering that in 2006 it [temporarily surpassed Google](https://mashable.com/2006/07/11/myspace-americas-number-one/) as the most visited website in the US. Most social media platforms that survived the last decade have shifted significantly in what they offer users. Twitter, for example, didn’t allow users to upload videos or images initially. Since 2011 this has been possible, and today, [more than 50%](https://www.bondcap.com/report/itr19/#view/78) of the content viewed on Twitter includes images and videos. ## Facebook dominated the social media market for a decade, but five other platforms also have more than half a billion users With 2.3 billion users, Facebook was the most popular social media platform in 2019. YouTube, Instagram, and WeChat followed, with over a billion users. Tumblr and TikTok came next, with over half a billion users. The bar chart shows a ranking of the top social media platforms. ## Some social media sites are particularly popular among specific population groups The aggregate numbers mask a great deal of heterogeneity across platforms. Some social media sites are much more popular than others among specific population groups. In general, young people are more likely to use social media than older people. But some platforms are much more popular among younger people. This is shown in the chart where we plot the breakdown of social media use by age group in the US. For Snapchat and Instagram, the ‘age gradient’ is exceptionally steep – the popularity of these platforms drops much faster with age. Most people under 25 use Snapchat (73%), while only 3% of people over 65 use it. Since these platforms are relatively new, it’s hard to know how much of this age gradient results from a “cohort effect”. In other words: it’s unclear whether today’s young people will continue using Snapchat as they age. If they do, the age gradient will narrow. Let’s now look at gender differences. This chart shows the percentage of men and women that used different platforms in the US in 2021—the diagonal line marks parity. Sites above the diagonal line are more popular among women, and those below are more popular among men. For some platforms, the gender differences are substantial. The share of women who used Pinterest was 3 times as high as that of men using this platform. For Reddit, it was the other way around: the share of men was twice as high. ## In rich countries, almost all young people use social media From a back-of-the-envelope calculation, we know that if Facebook had 2.3 billion users in 2019, then at least 30% of the world was using social media.2 This is just an average – usage rates were much higher for some world regions, specifically for some population groups. Young people tend to use social media more frequently. In fact, in rich countries where access to the Internet is [nearly universal](https://ourworldindata.org/grapher/share-of-individuals-using-the-internet), the vast majority of young adults use it. Our chart shows the proportion of people aged 16 to 24 who used social networks across various countries. As we can see, the average for the OECD is close to 90%. If today’s young adults continue using social media throughout their lives, then it’s likely that social media will continue growing rapidly as Internet adoption [expands throughout lower-income countries](https://ourworldindata.org/internet#growth-of-the-internet). ## The rise of social media in rich countries has come together with an increase in the amount of time spent online The increase in social media use over the last decade has, of course, come together with a large increase in the amount of time people spend online. In the US, adults spend more than 6 hours daily on digital media (apps and websites accessed through mobile phones, tablets, computers, and other connected devices such as game consoles). As the chart shows, this growth has been driven almost entirely by additional time spent on smartphones and tablets.3 According to a survey from the Pew Research Center, adults aged 18 to 29 in the US are [more likely](https://www.pewresearch.org/fact-tank/2018/12/10/social-media-outpaces-print-newspapers-in-the-u-s-as-a-news-source/) to get news indirectly via social media than directly from print newspapers or news sites. They also report being [online “almost constantly”](https://www.pewresearch.org/fact-tank/2019/07/25/americans-going-online-almost-constantly/).4 Evidence shows that in other rich countries, people also spend many hours per day online. The following chart shows how many hours young people spend online across various rich countries. As we can see, the average for the OECD is more than 4 hours per day; in some countries, the average is above 6 hours per day. ## Some perspective on how fast and profound these rapid changes are The percentage of US adults who use social media increased from 5% in 2005 to 79% in 2019. Even on a global stage, the speed of diffusion is striking: Facebook surged from covering around 1.5% of the world population in 2008 to around 30% in 2018.5 How does this compare to the diffusion of other communication technologies in today's everyday life? The following chart provides some perspective. Social media’s growth in the US is comparable – in speed and, to some extent, reach – to most modern communication-enabling technologies, including computers, smartphones, and the Internet. The rise of social media is an extraordinary example of how quickly and drastically social behaviors can change: Something that is today part of the everyday life of one-third of the world population was unthinkable less than a generation ago. Rapid changes like those brought about by social media always spark fears about possible negative effects. Specifically, in the context of social media, a key question is whether these new communication technologies are harming our mental health – this is an important question and we cover the evidence in [another article](https://ourworldindata.org/social-media-wellbeing) on Our World in Data. The US social media adoption data is [here](https://ourworldindata.org/grapher/technology-adoption-by-households-in-the-united-states?time=2005..2019&country=Social%20media%20usage). Regarding Facebook’s global numbers: In 2018, Facebook had 2.26 billion users, and in 2008 it had 100 million; the world population in 2008 was 6.8 billion, and in 2018 it was 7.63 billion (you can check the population data [here](https://ourworldindata.org/grapher/world-population-by-world-regions-post-1820).) According to the survey from Pew Research, 36% of adults 18 to 29 in the US say they ‘often get news via social media,’ which is higher than the share saying they ‘often get news via other platforms,’ such as news sites, TV, radio or print newspapers. From the same survey, we also know that 48% of adults 18 to 29 say they go online almost constantly, and 46% say they go online multiple times daily. There were, of course, earlier, much smaller predecessors of social networking websites. The first recognizable social media site, in the format we know today, was [Six Degrees](https://en.wikipedia.org/wiki/SixDegrees.com) – a platform created in 1997 that enabled users to upload a profile and make friends with other users. At the core, the features that define a social media platform are (i) profiles for users, (ii) the ability for users to upload content constantly, and (iii) the ability for users to discuss content and connect with other users. Digital media contrasts with print media (including books, newspapers, and magazines) and other traditional or analog media (including TV, movies, and radio). To be precise, Facebook had 2.3 billion ‘active users.’ There may be some discrepancies between the number of ‘active users’ and the number of people since one person could, in theory, maintain multiple accounts. In practice, these discrepancies are likely small because most social media platforms, including Facebook, have [policies and checks](https://www.facebook.com/help/975828035803295) to avoid multiple accounts per person.",The rise of social media 1wcnF-qtMtNWOxYNgXPSXF1l2FK5OiX0uw2wyJZZwRUA,living-planet-index-decline,article,"{""toc"": [{""slug"": ""is-the-living-planet-index-sensitive-to-outliers"", ""text"": ""Is the Living Planet Index sensitive to outliers?"", ""title"": ""Is the Living Planet Index sensitive to outliers?"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""text"": [{""type"": ""text"", ""value"": [{""text"": ""The Living Planet Index is the biodiversity metric that always claims the headlines. Unfortunately, many of these headlines are wrong. The index is very easy to misinterpret."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Living Planet Index reports an average decline of 69% across tens of thousands of wildlife populations since 1970. This does not tell us anything about the number of individuals, species or populations lost, or even the share of populations that are shrinking."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Before reporting on the Living Planet Index we should understand what it actually tells us about the world’s wildlife. We should also be aware of the misconceptions and pitfalls of using this index to capture the changes in more than 30,000 of the world’s animal populations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Summary"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""“"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.indiatimes.com/news/india/last-50-years-earth-lost-68-percent-of-wildlife-85-percent-wetlands-because-of-humans-522447.html#:~:text=5%20months%20ago-,In%20The%20Last%2050%20Years%2C%20Earth%20Has%20Lost%2068%25%20Of,All%20Thanks%20To%20Us%20Humans&text=Global%20animal%2C%20bird%2C%20and%20fish,a%20new%20report%20has%20stated."", ""children"": [{""text"": ""In the last 50 years, Earth has lost 68% of wildlife, all thanks to us humans"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” (India Times)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""“"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.theguardian.com/environment/2018/oct/30/humanity-wiped-out-animals-since-1970-major-report-finds"", ""children"": [{""text"": ""Humanity has wiped out 60% of animal populations since 1970, report finds"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” (The Guardian)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""“"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.weforum.org/agenda/2018/10/weve-lost-60-of-wildlife-in-less-than-50-years/"", ""children"": [{""text"": ""We've lost 60% of wildlife in less than 50 years"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” (World Economic Forum)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These are just three of many headlines covering the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.livingplanetindex.org/home/index"", ""children"": [{""text"": ""Living Planet Index"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". But they are all wrong. They are based on a misunderstanding of what the Living Planet Index shows."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I sympathize with the journalists. Interpreting this metric is hard. I’m sure I’ve made similar mistakes in the past: using the terms ‘decline’, ‘lost’, and ‘fall’ interchangeably in biodiversity discussions. Combine this with the complexities of ‘populations’, ‘species’ and ‘extinctions’, and it gets complicated pretty quickly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Before reporting on the Living Planet Index we should understand what it actually tells us about the world’s wildlife. We should also be aware of the misconceptions and pitfalls of using this index to capture the changes in more than 30,000 of the world’s animal populations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What does ‘average decline’ actually mean?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Living Planet Index (LPI) measures the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""average change"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" in the number of individuals across the world’s animal populations. A ‘population’ is defined as a species within a geographical area. So, despite being the same species, the African elephant in South Africa and Tanzania are counted as different populations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Let’s take a look at an example. This will not only show us how easily this figure can be misinterpreted but also why we should be careful when assuming that it gives us an accurate picture of what’s really happening to all wildlife populations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We’re going to take a real-life example of two populations of the "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/mammals#rhinos"", ""children"": [{""text"": ""Black rhino"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "": one in Tanzania and one in Botswana. In the first row of the table, we see their population size in 1980: there were 3795 rhinos in Tanzania, and only 30 in Botswana. In the following decades intense poaching in Tanzania has plunged its population to critically endangered status: in the second row we see that by 2017 there were only 160 rhinos left. Things in Botswana actually improved over time: its 30 rhinos increased to 50. The difference between their population size in 1980 and 2017 is shown in the third row: this represents the number of animals lost over time. And in the final row, we see the percentage change in population size for each."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""rows"": [{""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": []}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Black rhino (Tanzania)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Black rhino (Botswana)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Total (Tanzanian and Botswananian black rhinos)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Population size in 1980"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""3795"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""30"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""3825"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Population size in 2017"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""160"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""50"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""210"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Number of animals lost since 1980"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""3635"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""-20 (gained 20 rhinos)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""3615"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Percentage change in population size"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""-96%"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""+67%"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""-95%"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}]}]}], ""size"": ""narrow"", ""type"": ""table"", ""template"": ""header-column-row"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Tanzanian rhinos obviously did not fare well: they lost 96% of their population. The group in Botswana did much better. In fact, their numbers "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""increased "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""by 67%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we calculate the average change of these two populations we get a value of -15%. Take the average of the change in Tanzania (-96%) and Botswana (67%). This means the Black rhinos saw an average decline of 15%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Here, things get slightly more complex, because the actual LPI is calculated in a similar way but with one difference. It doesn’t just take the mean change across populations (called the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""arithmetic mean"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""), it takes the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""geometric mean"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The geometric mean across these two populations is -74%."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Let's keep that in mind. I'll continue to also explain this using the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""arithmetic"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" mean because it's easier to follow and understand. But it's the geometric mean that the LPI uses. The numbers are different, but their implications are similar."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The common misinterpretation of these numbers – where headlines would incorrectly report that “we’ve "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""lost"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" 15% [or 74%] of animals” – is shown in the far right column. There we’ve summed these numbers to show the two populations combined. In 1980, the total number of animals was 3825. We then lost 3615 of them, to give us only 210 in 2017. This means we actually lost 95% of the rhinos. The LPI is therefore a different measure from the number or percentage of individual animals lost."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But this also highlights an even greater danger when reporting the LPI. By averaging these two populations we’ve ended up pretty clueless about the status of either of them. Either a 15% [or 74% using the geometric mean] decline would give a skewed understanding of the situation. The Black rhino in Tanzania has lost 96% of its rhinos and has become critically endangered. On the other hand, something is going right in Botswana because its numbers have "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""increased"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". Both of these outcomes would be missed."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This might mean we don’t prioritize the Tanzanian black rhino when we really need to. And we might lose out on an important lesson from Botswana on how to increase numbers in critically endangered populations. This is why a more population-specific approach to conservation is needed, as I discuss in the dropdown box at the end of this article."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""The Living Planet Index tells us that studied animal populations have seen an "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""average"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" relative decline of 69% since 1970"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Let’s then dig into the actual results of the LPI. The above headline is the main message of the 2022 Living Planet Index report."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It tells us that from 1970 to 2018, there was an "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""average decline"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of 69% across studied animal populations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What types and how much of the world’s wildlife does the LPI cover? In the latest report it covered 31,821 populations of 5,230 species across the world. It only covers vertebrate species – mammals, birds, fish, reptiles and amphibians."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It includes a large number of populations from each world region. In the latest report, the authors significantly increased the number of studies that were included in languages other than English. The number of included species from Asia and the Pacific increased by a quarter since the 2020 report. For Africa, species coverage increased by 37%. And most notably, it expanded by 66% across Latin America and the Caribbean."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is great progress. However, the tropics "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/living-planet-index#how-many-species-does-it-cover-what-is-the-geographical-range-of-this-coverage"", ""children"": [{""text"": ""are still underrepresented"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" relative to Europe and North America. This is not ideal, considering the tropics are home to the greatest diversity of species and is where wildlife is most threatened."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This reveals two further limitations. First, it only covers a tiny percentage of species: Only 16% of known bird species; 11% of mammals; 6% of fish; and 3% of amphibians and reptile species. It’s hard to say how representative the available data is: it’s often the case that the species we are most concerned about (deservedly) get the most attention in the research. Second, many taxonomic groups are not included at all – nothing on insects, fungi, coral or plants. This is largely due to data availability – it’s easier to count bears than ants. Still, we should be wary of generalizing these results to all life on Earth. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""[In our "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""children"": [{""url"": ""http:///faq-living-planet-index"", ""children"": [{""text"": ""FAQ on the Living Planet Index"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""children"": [{""text"": "" I look at the geographical and species breakdown of data availability, and where it’s sourced from]"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The global LPI is shown in the chart, where the value in 1970 is indexed to 100%. As we see, this has fallen to 31% in 2018, signifying a 69% decline."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/global-living-planet-index"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To be clear once again: the LPI "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""does not"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" tell us the number of species, populations or individuals lost; the number of extinctions that have occurred; or even the share of species that are declining. It tells us that between 1970 and 2018, on average, there was a 69% decline in population size across the 31,821 studied populations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Effective conservation means we have to look past the average"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To tell the real story on biodiversity, we have to be conscious of how the headlines are communicated. Losing 69% of the world’s wildlife within decades would be devastating. Thankfully it isn’t true."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But, this shouldn’t detract from the fact that the loss of many wildlife populations is deeply concerning. Unfortunately, averages are not particularly helpful in understanding "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""what"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""where"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" these populations are. When we look at more detailed analyses of the LPI we find that actually this 69% average decline hides even more drastic declines in some populations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we want to protect our most endangered species, these are the ones we need to prioritize. The average index unfortunately hides these populations from view."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We need to not only be careful with how we report the headline index itself. But we also need to be aware of what it does, or more importantly, does not tell us about how global wildlife is changing. As I cover in a "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://ourworldindata.org/living-planet-index-understanding"", ""children"": [{""text"": ""related article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""text"": "", the Living Planet database itself is an incredible resource that allows us to dig deeper into the individual stories of the 30,000 populations that have been reduced to a single number."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""gray-section"", ""items"": [{""text"": [{""text"": ""Is the Living Planet Index sensitive to outliers?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""expandable-paragraph"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""An average isn’t helpful – we need to know "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""where"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""what"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" animal populations are in greatest danger. Some are doing much worse than the headline suggests, but they get lost in the averages. This means we can’t focus our efforts on protecting the species that need it the most."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We should also be careful about what the average actually tells us. It can, quite easily, be sensitive to outliers: populations that have seen a dramatic decline or increase."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We'll look at a simple example: let’s say we had an ecosystem where one population saw a decline of 99%, and 393 other populations each "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""increased"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" by 1%. Our final result would report an average decline of 50%."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This is the difference between what we’d call a ‘catastrophic decline’ (a decline across most or all species) versus a ‘cluster decline’ (where it is a very specific set of species that are struggling). Our approach to tackling either of these scenarios would be very different."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""After the publication of the 2018 Living Planet Index report, researchers published a follow-up study in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Nature "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""where they looked at how the LPI was affected by extreme declines (or increases) in a small subset of the studied populations."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" For this study they looked at the results of the 2018 LPI report. It reported a 60% average decline in wildlife populations since 1970."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""By looking at the population data underlying the LPI they found that this 60% average decline was driven by extreme losses in a small subset of populations. If you excluded the 2.4% most-strongly declining populations – which was 356 out of 14,700 – the result reversed from a 60% average decline to a slightly positive growth. In other words, 2% to 3% of populations were doing "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""extremely "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""badly, but it appears that most species were doing okay."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart we see how the 2018 LPI result would have been affected by excluding the most extreme-negative populations. In red we see the final headline result of the report – a 60% average decline across the 14,700 populations. But as we exclude the most extreme negative populations, first 120 then 238 populations, we see that this average decline reduces significantly. Then, when we exclude the 356 most-severe populations, not only does the average decline reduce, it actually turns into a net positive. The abundance across these populations was, on average, "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""increasing"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Impact-of-extremes-on-living-planet-index.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But, extreme "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""positive"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" outliers can also have a strong impact on the final result. In the 2020 update of the LPI they "", ""spanType"": ""span-simple-text""}, {""url"": ""http://stats.livingplanetindex.org/"", ""children"": [{""text"": ""updated this analysis"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" to show the impact of removing extreme negative, extreme positive, and both outliers combined. This is shown in the chart. Again we see the impact of removing negative extremes: removing the most severe 5% of populations turns the average 68% decline into an average "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""increase"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". But removing positive extremes also has a large impact. When we remove even a few percent of the populations growing the most, we see an average decline of greater than 70%. Finally, if you remove both negative and positive outliers together we still see a significant average decline, although less than the reported LPI of 68%. If we remove the most extreme 10% of populations we get a 42% average decline since 1970."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In response to this criticism to the sensitivity of the LPI to outliers, the authors tested this sensitivity in the 2022 report. To do this they removed 2.5%, 5% and 10% of the most extreme declining and increasing populations, and recalculated the index each time. While they got slightly different results from the 69% average decline reported, the overall trend is very similar in each one."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This suggests that the final result is not mostly driven by the combination of extreme declines and increases. It might be the case that populations that have experienced very large increases cancel out the impact of very large declines. That would mean the overall result is not affected much when "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""both"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" are removed."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Regardless of how much of the final result is driven by extreme outliers, this discussion highlights the continued issue of trying to summarise global biodiversity trends into a single index number."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What is more useful for conservation is looking at clustered declines: cutting through the average to identify where and what types of populations are seeing dramatic declines. Leung et al. (2020) highlighted several clusters of species that have struggled: reptile, amphibian and mammal species across Latin America; Indo-Malayan freshwater birds and amphibians; Arctic mammals; and both marine and freshwater fishes across most of the world’s environments."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Impact of removing positive or negative outliers from the Living Planet Index"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""LPI-Outliers-2020.png"", ""parseErrors"": []}], ""parseErrors"": []}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""017f94c03a24321a2d75dec67e5f22b4ae144e63"": {""id"": ""017f94c03a24321a2d75dec67e5f22b4ae144e63"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""WWF. 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WWF, Gland, Switzerland."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c6211c8e6d4692ccf7e46166a94127b5d45fb618"": {""id"": ""c6211c8e6d4692ccf7e46166a94127b5d45fb618"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""WWF (2022) "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Living Planet Report 2022 – Building a nature-positive society"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". Almond, R.E.A., Grooten, M., Juffe Bignoli, D. & Petersen, T. (Eds). WWF, Gland, Switzerland."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""cccdd17a4a4b5b6c4219257529e062400d8b3919"": {""id"": ""cccdd17a4a4b5b6c4219257529e062400d8b3919"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The geometric mean is calculated by multiplying the numbers and taking the square root of the product (if there are two populations); cube root (if there are three populations); etc. It's often slightly better for calculating averages on rates of change."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Buckland, S. T., Studeny, A. C., Magurran, A. E., Illian, J. B., & Newson, S. E. (2011). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://esajournals.onlinelibrary.wiley.com/doi/10.1890/ES11-00186.1"", ""children"": [{""text"": ""The geometric mean of relative abundance indices: a biodiversity measure with a difference"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Ecosphere"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(9), 1-15."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""da39bda0025dcaed4863fd8557e8dc499d9d2070"": {""id"": ""da39bda0025dcaed4863fd8557e8dc499d9d2070"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Leung, B., Hargreaves, A. L., Greenberg, D. A., McGill, B., Dornelas, M., & Freeman, R. (2020). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.nature.com/articles/s41586-020-2920-6"", ""children"": [{""text"": ""Clustered versus catastrophic global vertebrate declines"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Nature"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""588"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(7837), 267-271."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Living Planet Index: what does an average decline of 69% really mean?"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""The Living Planet Index is the biodiversity metric that always claims the headlines. It’s often misinterpreted. How should we understand it?"", ""dateline"": ""October 13, 2022"", ""subtitle"": ""The Living Planet Index is the biodiversity metric that always claims the headlines. It’s often misinterpreted. How should we understand it?"", ""sidebar-toc"": false, ""featured-image"": ""Living-Planet-Index-thumbnail.png""}",1,2024-02-27 18:56:11,2022-10-13 09:00:00,2024-02-27 18:59:29,listed,ALBJ4LtJA9tjOX5gD72q-f6-og6jVfHGitSta_0dhXNXu9MteIQokrr1QyelaPkGA2tu5EZRl-OoQEr4A9QzKw,," “[In the last 50 years, Earth has lost 68% of wildlife, all thanks to us humans](https://www.indiatimes.com/news/india/last-50-years-earth-lost-68-percent-of-wildlife-85-percent-wetlands-because-of-humans-522447.html#:~:text=5%20months%20ago-,In%20The%20Last%2050%20Years%2C%20Earth%20Has%20Lost%2068%25%20Of,All%20Thanks%20To%20Us%20Humans&text=Global%20animal%2C%20bird%2C%20and%20fish,a%20new%20report%20has%20stated.)” (India Times) “[Humanity has wiped out 60% of animal populations since 1970, report finds](https://www.theguardian.com/environment/2018/oct/30/humanity-wiped-out-animals-since-1970-major-report-finds)” (The Guardian) “[We've lost 60% of wildlife in less than 50 years](https://www.weforum.org/agenda/2018/10/weve-lost-60-of-wildlife-in-less-than-50-years/)” (World Economic Forum) These are just three of many headlines covering the [Living Planet Index](https://www.livingplanetindex.org/home/index). But they are all wrong. They are based on a misunderstanding of what the Living Planet Index shows. I sympathize with the journalists. Interpreting this metric is hard. I’m sure I’ve made similar mistakes in the past: using the terms ‘decline’, ‘lost’, and ‘fall’ interchangeably in biodiversity discussions. Combine this with the complexities of ‘populations’, ‘species’ and ‘extinctions’, and it gets complicated pretty quickly. Before reporting on the Living Planet Index we should understand what it actually tells us about the world’s wildlife. We should also be aware of the misconceptions and pitfalls of using this index to capture the changes in more than 30,000 of the world’s animal populations. # What does ‘average decline’ actually mean? The Living Planet Index (LPI) measures the _average change_ in the number of individuals across the world’s animal populations. A ‘population’ is defined as a species within a geographical area. So, despite being the same species, the African elephant in South Africa and Tanzania are counted as different populations. Let’s take a look at an example. This will not only show us how easily this figure can be misinterpreted but also why we should be careful when assuming that it gives us an accurate picture of what’s really happening to all wildlife populations. We’re going to take a real-life example of two populations of the [Black rhino](http://ourworldindata.org/mammals#rhinos): one in Tanzania and one in Botswana. In the first row of the table, we see their population size in 1980: there were 3795 rhinos in Tanzania, and only 30 in Botswana. In the following decades intense poaching in Tanzania has plunged its population to critically endangered status: in the second row we see that by 2017 there were only 160 rhinos left. Things in Botswana actually improved over time: its 30 rhinos increased to 50. The difference between their population size in 1980 and 2017 is shown in the third row: this represents the number of animals lost over time. And in the final row, we see the percentage change in population size for each. ||Black rhino (Tanzania)|Black rhino (Botswana)|_Total (Tanzanian and Botswananian black rhinos)_| |Population size in 1980|3795|30|_3825_| |Population size in 2017|160|50|_210_| |Number of animals lost since 1980|3635|-20 (gained 20 rhinos)|_3615_| |Percentage change in population size|-96%|+67%|_-95%_| The Tanzanian rhinos obviously did not fare well: they lost 96% of their population. The group in Botswana did much better. In fact, their numbers _increased _by 67%. If we calculate the average change of these two populations we get a value of -15%. Take the average of the change in Tanzania (-96%) and Botswana (67%). This means the Black rhinos saw an average decline of 15%. Here, things get slightly more complex, because the actual LPI is calculated in a similar way but with one difference. It doesn’t just take the mean change across populations (called the _arithmetic mean_), it takes the _geometric mean_.1 The geometric mean across these two populations is -74%.2 Let's keep that in mind. I'll continue to also explain this using the _arithmetic_ mean because it's easier to follow and understand. But it's the geometric mean that the LPI uses. The numbers are different, but their implications are similar. The common misinterpretation of these numbers – where headlines would incorrectly report that “we’ve _lost_ 15% [or 74%] of animals” – is shown in the far right column. There we’ve summed these numbers to show the two populations combined. In 1980, the total number of animals was 3825. We then lost 3615 of them, to give us only 210 in 2017. This means we actually lost 95% of the rhinos. The LPI is therefore a different measure from the number or percentage of individual animals lost. But this also highlights an even greater danger when reporting the LPI. By averaging these two populations we’ve ended up pretty clueless about the status of either of them. Either a 15% [or 74% using the geometric mean] decline would give a skewed understanding of the situation. The Black rhino in Tanzania has lost 96% of its rhinos and has become critically endangered. On the other hand, something is going right in Botswana because its numbers have _increased_. Both of these outcomes would be missed. This might mean we don’t prioritize the Tanzanian black rhino when we really need to. And we might lose out on an important lesson from Botswana on how to increase numbers in critically endangered populations. This is why a more population-specific approach to conservation is needed, as I discuss in the dropdown box at the end of this article. # The Living Planet Index tells us that studied animal populations have seen an _average_ relative decline of 69% since 1970 Let’s then dig into the actual results of the LPI. The above headline is the main message of the 2022 Living Planet Index report.3 It tells us that from 1970 to 2018, there was an _average decline_ of 69% across studied animal populations. What types and how much of the world’s wildlife does the LPI cover? In the latest report it covered 31,821 populations of 5,230 species across the world. It only covers vertebrate species – mammals, birds, fish, reptiles and amphibians. It includes a large number of populations from each world region. In the latest report, the authors significantly increased the number of studies that were included in languages other than English. The number of included species from Asia and the Pacific increased by a quarter since the 2020 report. For Africa, species coverage increased by 37%. And most notably, it expanded by 66% across Latin America and the Caribbean. This is great progress. However, the tropics [are still underrepresented](https://ourworldindata.org/living-planet-index#how-many-species-does-it-cover-what-is-the-geographical-range-of-this-coverage) relative to Europe and North America. This is not ideal, considering the tropics are home to the greatest diversity of species and is where wildlife is most threatened. This reveals two further limitations. First, it only covers a tiny percentage of species: Only 16% of known bird species; 11% of mammals; 6% of fish; and 3% of amphibians and reptile species. It’s hard to say how representative the available data is: it’s often the case that the species we are most concerned about (deservedly) get the most attention in the research. Second, many taxonomic groups are not included at all – nothing on insects, fungi, coral or plants. This is largely due to data availability – it’s easier to count bears than ants. Still, we should be wary of generalizing these results to all life on Earth. _[In our __[FAQ on the Living Planet Index](http:///faq-living-planet-index)__ I look at the geographical and species breakdown of data availability, and where it’s sourced from]_. The global LPI is shown in the chart, where the value in 1970 is indexed to 100%. As we see, this has fallen to 31% in 2018, signifying a 69% decline. To be clear once again: the LPI _does not_ tell us the number of species, populations or individuals lost; the number of extinctions that have occurred; or even the share of species that are declining. It tells us that between 1970 and 2018, on average, there was a 69% decline in population size across the 31,821 studied populations. # Effective conservation means we have to look past the average To tell the real story on biodiversity, we have to be conscious of how the headlines are communicated. Losing 69% of the world’s wildlife within decades would be devastating. Thankfully it isn’t true. But, this shouldn’t detract from the fact that the loss of many wildlife populations is deeply concerning. Unfortunately, averages are not particularly helpful in understanding _what_ and _where_ these populations are. When we look at more detailed analyses of the LPI we find that actually this 69% average decline hides even more drastic declines in some populations. If we want to protect our most endangered species, these are the ones we need to prioritize. The average index unfortunately hides these populations from view. We need to not only be careful with how we report the headline index itself. But we also need to be aware of what it does, or more importantly, does not tell us about how global wildlife is changing. As I cover in a **[related article](https://ourworldindata.org/living-planet-index-understanding)**, the Living Planet database itself is an incredible resource that allows us to dig deeper into the individual stories of the 30,000 populations that have been reduced to a single number. ## Is the Living Planet Index sensitive to outliers? An average isn’t helpful – we need to know _where_ and _what_ animal populations are in greatest danger. Some are doing much worse than the headline suggests, but they get lost in the averages. This means we can’t focus our efforts on protecting the species that need it the most. We should also be careful about what the average actually tells us. It can, quite easily, be sensitive to outliers: populations that have seen a dramatic decline or increase. We'll look at a simple example: let’s say we had an ecosystem where one population saw a decline of 99%, and 393 other populations each _increased_ by 1%. Our final result would report an average decline of 50%.4 This is the difference between what we’d call a ‘catastrophic decline’ (a decline across most or all species) versus a ‘cluster decline’ (where it is a very specific set of species that are struggling). Our approach to tackling either of these scenarios would be very different. After the publication of the 2018 Living Planet Index report, researchers published a follow-up study in _Nature _where they looked at how the LPI was affected by extreme declines (or increases) in a small subset of the studied populations.4 For this study they looked at the results of the 2018 LPI report. It reported a 60% average decline in wildlife populations since 1970.5 By looking at the population data underlying the LPI they found that this 60% average decline was driven by extreme losses in a small subset of populations. If you excluded the 2.4% most-strongly declining populations – which was 356 out of 14,700 – the result reversed from a 60% average decline to a slightly positive growth. In other words, 2% to 3% of populations were doing _extremely _badly, but it appears that most species were doing okay. In the chart we see how the 2018 LPI result would have been affected by excluding the most extreme-negative populations. In red we see the final headline result of the report – a 60% average decline across the 14,700 populations. But as we exclude the most extreme negative populations, first 120 then 238 populations, we see that this average decline reduces significantly. Then, when we exclude the 356 most-severe populations, not only does the average decline reduce, it actually turns into a net positive. The abundance across these populations was, on average, _increasing_. But, extreme _positive_ outliers can also have a strong impact on the final result. In the 2020 update of the LPI they [updated this analysis](http://stats.livingplanetindex.org/) to show the impact of removing extreme negative, extreme positive, and both outliers combined. This is shown in the chart. Again we see the impact of removing negative extremes: removing the most severe 5% of populations turns the average 68% decline into an average _increase_. But removing positive extremes also has a large impact. When we remove even a few percent of the populations growing the most, we see an average decline of greater than 70%. Finally, if you remove both negative and positive outliers together we still see a significant average decline, although less than the reported LPI of 68%. If we remove the most extreme 10% of populations we get a 42% average decline since 1970. In response to this criticism to the sensitivity of the LPI to outliers, the authors tested this sensitivity in the 2022 report. To do this they removed 2.5%, 5% and 10% of the most extreme declining and increasing populations, and recalculated the index each time. While they got slightly different results from the 69% average decline reported, the overall trend is very similar in each one. This suggests that the final result is not mostly driven by the combination of extreme declines and increases. It might be the case that populations that have experienced very large increases cancel out the impact of very large declines. That would mean the overall result is not affected much when _both_ are removed. Regardless of how much of the final result is driven by extreme outliers, this discussion highlights the continued issue of trying to summarise global biodiversity trends into a single index number. What is more useful for conservation is looking at clustered declines: cutting through the average to identify where and what types of populations are seeing dramatic declines. Leung et al. (2020) highlighted several clusters of species that have struggled: reptile, amphibian and mammal species across Latin America; Indo-Malayan freshwater birds and amphibians; Arctic mammals; and both marine and freshwater fishes across most of the world’s environments. The geometric mean is calculated by multiplying the numbers and taking the square root of the product (if there are two populations); cube root (if there are three populations); etc. It's often slightly better for calculating averages on rates of change. Buckland, S. T., Studeny, A. C., Magurran, A. E., Illian, J. B., & Newson, S. E. (2011). [The geometric mean of relative abundance indices: a biodiversity measure with a difference](https://esajournals.onlinelibrary.wiley.com/doi/10.1890/ES11-00186.1). _Ecosphere_, _2_(9), 1-15. We can calculate this by taking the geometric mean of 1.67 and 0.04 (which is the +67% and -96%) of the two populations. This gives us 0.26. That means (1 - 0.26) = a 74% decline. WWF (2022) _Living Planet Report 2022 – Building a nature-positive society_. Almond, R.E.A., Grooten, M., Juffe Bignoli, D. & Petersen, T. (Eds). WWF, Gland, Switzerland. Leung, B., Hargreaves, A. L., Greenberg, D. A., McGill, B., Dornelas, M., & Freeman, R. (2020). [Clustered versus catastrophic global vertebrate declines](https://www.nature.com/articles/s41586-020-2920-6). _Nature_, _588_(7837), 267-271. WWF. [Living Planet Report 2018: Aiming Higher](https://www.worldwildlife.org/pages/living-planet-report-2018) (eds. Grooten, N. & Almond, R. E. A.) (WWF, 2018). WWF (2020).[ ](https://livingplanet.panda.org/)[Living Planet Report 2020 - Bending the curve of biodiversity loss](https://livingplanet.panda.org/). Almond, R.E.A., Grooten M. and Petersen, T. (Eds). WWF, Gland, Switzerland.",Living Planet Index: what does an average decline of 69% really mean? 1wWZvocIi48BhfxKGvxUTIEET5p2vsqcMhYBSn3htMCo,farm-size,topic-page,"{""toc"": [], ""body"": [{""type"": ""topic-page-intro"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""It's estimated that there are around 570 million farms in the world."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The size and productivity of farms across the world varies a lot. This matters a lot for economic development, poverty alleviation, global food production and its environmental impacts."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most of the world's farmers are smallholders – with farms less than two hectares in size. Yet these farmers are some of the poorest. The size and low labor productivity on these farms can make it difficult for smallholders to increase their incomes, and escape poverty."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The distribution of farms across the world matters for human development and wellbeing, as well as the impact that agriculture has on the environment."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On this page you can find all our data, visualizations, and writing relating to farm size."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": [], ""relatedTopics"": [{""url"": ""https://ourworldindata.org/land-use"", ""text"": ""Land use"", ""type"": ""topic-page-intro-related-topic""}, {""url"": ""https://ourworldindata.org/employment-in-agriculture"", ""text"": ""Employment in Agriculture"", ""type"": ""topic-page-intro-related-topic""}, {""url"": ""https://ourworldindata.org/fertilizers"", ""text"": ""Fertilizers"", ""type"": ""topic-page-intro-related-topic""}]}, {""more"": {""heading"": ""More articles related to Farm Size"", ""articles"": [{""value"": {""url"": ""https://ourworldindata.org/yields-habitat-loss"", ""title"": ""To protect the world’s wildlife we must improve crop yields – especially across Africa"", ""authors"": [""Hannah Ritchie""]}}, {""value"": {""url"": ""https://ourworldindata.org/peak-agriculture-land"", ""title"": ""After millennia of agricultural expansion, the world has passed ‘peak agricultural land’"", ""authors"": [""Hannah Ritchie""]}}, {""value"": {""url"": ""https://ourworldindata.org/africa-yields-problem"", ""title"": ""Increasing agricultural productivity across Sub-Saharan Africa is one of the most important problems this century"", ""authors"": [""Hannah Ritchie""]}}]}, ""rows"": [], ""type"": ""research-and-writing"", ""heading"": ""Research & Writing"", ""primary"": [{""value"": {""url"": ""https://ourworldindata.org/smallholder-food-production"", ""title"": ""Smallholders produce one-third of the world’s food, less than half of what many headlines claim"", ""authors"": [""Hannah Ritchie""], ""filename"": ""smallholders-thumbnail.png"", ""subtitle"": ""Most of the world’s farmers are smallholders. They are also often the poorest. How much of the world’s food do they produce?""}}], ""secondary"": [{""value"": {""url"": ""https://ourworldindata.org/from-1-90-to-2-15-a-day-the-updated-international-poverty-line"", ""title"": ""Do we only have 60 harvests left?"", ""authors"": [""Hannah Ritchie""], ""filename"": ""Soil-Lifespans-Thumbnail.png"", ""subtitle"": ""Claims that the world has only 100, 60, or even 30 years of harvests left often hit the headlines. These claims are overblown, but soil erosion is a problem and we can do something about it.""}}], ""parseErrors"": [], ""hide-authors"": false}, {""top"": [], ""type"": ""all-charts"", ""heading"": ""All charts"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""24554e859211667b446bf292e5cc73f000feb9c3"": {""id"": ""24554e859211667b446bf292e5cc73f000feb9c3"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Lowder, S. K., Skoet, J., & Raney, T. (2016). T"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.sciencedirect.com/science/article/pii/S0305750X15002703"", ""children"": [{""text"": ""he number, size, and distribution of farms, smallholder farms, and family farms worldwide"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""World Development"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""87"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", 16-29."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""topic-page"", ""title"": ""Farm Size and Productivity"", ""authors"": [""Hannah Ritchie"", ""Max Roser""], ""excerpt"": ""How does farm size vary across the world? How much of farmland is held by smallholders?"", ""dateline"": ""July 8, 2022"", ""subtitle"": ""How does farm size vary across the world? How much of farmland is held by smallholders?"", ""sticky-nav"": [{""text"": ""Introduction"", ""target"": ""#introduction""}, {""text"": ""Research & Writing"", ""target"": ""#research-writing""}, {""text"": ""Charts"", ""target"": ""#all-charts""}, {""text"": ""Endnotes"", ""target"": ""#article-endnotes""}, {""text"": ""Cite This Work"", ""target"": ""#article-citation""}, {""text"": ""Reuse This Work"", ""target"": ""#article-licence""}], ""featured-image"": ""farm-size-thumbnail.png""}",1,2024-06-12 14:47:37,2022-07-08 14:48:32,1970-01-01 00:00:00,unlisted,ALBJ4LuBSnrcdZtFntu67GM5gzOaGoyJuSL3aB0Zy1eMODw3hdeL4dEHNp2WbLpOJR-pCi6RbnWItmchalSa5w,,,Farm Size and Productivity 1wTcR50pw-YBNbDANNB1oy8ubzy_DiTFcyqBggjSXsBE,mass-extinctions,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""There have been five big mass extinctions in Earth's history – these are called the \""Big Five\"". Understanding the reasons and timelines of these events is important to understand the speed and scale of species extinctions today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When and why did these mass extinction events happen?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What is a mass extinction?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""First, we must be clear on what we mean by \""mass extinction\"". Extinctions are a normal part of evolution: they occur naturally and periodically over time."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There’s a natural background rate to the timing and frequency of extinctions: 10% of species are lost every million years, 30% every 10 million years, and 65% every 100 million years."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It would be wrong to assume that species going extinct is out of line with what we would expect. Evolution occurs through the balance of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""extinction"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" – the end of species – and "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""speciation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" – the creation of new ones."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Extinctions occur periodically at what we would call the \""background rate\"". We can therefore identify periods of history when extinctions were happening much faster than this background rate – this would tell us that there was an additional environmental or ecological pressure creating more extinctions than we would expect."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, mass extinctions are periods with "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""much"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" higher extinction rates than normal. They are defined by both magnitude and rate. Magnitude is the percentage of species that are lost. Rate is how quickly this happens. These metrics are inevitably linked, but we need both to qualify as a mass extinction."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In a mass extinction, at least 75% of species go extinct within a relatively (by geological standard) short period of time."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Typically less than two million years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""The five mass extinctions"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There have been five mass extinction events in Earth’s history, at least since 500 million years ago. We know very little about extinction events in the Precambrian and early Cambrian earlier, which predate this."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" These are called the \""Big Five\"" for obvious reasons."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, we see the timing of events in Earth’s history."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It shows the changing extinction rate (measured as the number of families that went extinct per million years). Again, note that this number was never zero: background extinction rates were low – typically less than 5 families per million years – but ever-present."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see the spikes in extinction rates marked as the five events:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""numbered-list"", ""items"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""End Ordovician"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" (444 million years ago; mya)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Late Devonian"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" (360 mya)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""End Permian"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" (250 mya)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""End Triassic"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" (200 mya) – many people mistake this as the event that killed off the dinosaurs. But in fact, they were killed off at the end of the Cretaceous period – the fifth of the \""Big Five\""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""End Cretaceous"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" (65 mya) – the event that killed off the dinosaurs."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Finally, at the end of the timeline, we have the question of what will come. Perhaps we are headed for a sixth mass extinction. But we are currently far from that point."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are a range of trajectories that the extinction rate could take in the decades and centuries to follow; which one we follow is determined by us."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Big-Five-Mass-Extinctions.png"", ""parseErrors"": []}, {""text"": [{""text"": ""What caused the five mass extinctions?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All of the \""Big Five\"" were caused by some combination of rapid and dramatic changes in climate, combined with significant changes in the composition of environments on land or the ocean (such as ocean acidification or acid rain from intense volcanic activity)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the table here, I detail the proposed causes for each of the five extinction events."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""rows"": [{""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Extinction Event"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Age(mya)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Percentage of species lost"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Cause of extinction"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""End Ordovician"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""444"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""86%"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Intense glacial and interglacial periods created large sea-level swings and moved shorelines dramatically. The tectonic uplift of the Appalachian mountains created lots of weathering, sequestration of CO2, and with it, changes in climate and ocean chemistry."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Late Devonian"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""360"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""75%"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Rapid growth and diversification of land plants generated rapid and severe global cooling."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""End Permian"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""250"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""96%"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Intense volcanic activity in Siberia. This caused global warming. Elevated CO2 and sulfur (H2S) levels from volcanoes caused ocean acidification, acid rain, and other changes in ocean and land chemistry."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""End Triassic"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""200"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""80%"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Underwater volcanic activity in the Central Atlantic Magmatic Province (CAMP) caused global warming and a dramatic change in the chemical composition of the oceans."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""End Cretaceous"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""65"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""76%"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Asteroid impact in Yucatán, Mexico. 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Biodiversity and Conservation, 24(10), 2497-2519."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Howard Hughes Medical Institute."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""75b0a775c30c6740129404923c26742e43e244fc"": {""id"": ""75b0a775c30c6740129404923c26742e43e244fc"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""We can see a 75% reduction in species in two ways: high extinction or very low speciation rates. If speciation – the creation of new species – slows down a lot, the extinction rate does not need to be as high as we would expect in order to deplete species numbers by 75%. These events are sometimes called \""mass depletions\"" but are treated the same way as mass extinctions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""There have been five mass extinctions in Earth's history"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""When did the \""Big Five\"" mass extinctions happen, and what were their causes?"", ""subtitle"": ""When did the \""Big Five\"" mass extinctions happen, and what were their causes?"", ""sidebar-toc"": false, ""featured-image"": ""Extinctions-thumbnail.png""}",1,2024-03-09 18:21:06,2022-11-30 11:54:16,2024-03-09 18:27:51,listed,ALBJ4LuqiePBzN1VBmWjOHiMFab8IwIap0hSNnN2G3GSyk3XbKfOrakF2Om693xAfgmTLmITq99AHBSiSIT2aQ,,"There have been five big mass extinctions in Earth's history – these are called the ""Big Five"". Understanding the reasons and timelines of these events is important to understand the speed and scale of species extinctions today. When and why did these mass extinction events happen? # What is a mass extinction? First, we must be clear on what we mean by ""mass extinction"". Extinctions are a normal part of evolution: they occur naturally and periodically over time.1 There’s a natural background rate to the timing and frequency of extinctions: 10% of species are lost every million years, 30% every 10 million years, and 65% every 100 million years.2 It would be wrong to assume that species going extinct is out of line with what we would expect. Evolution occurs through the balance of _extinction_ – the end of species – and _speciation_ – the creation of new ones. Extinctions occur periodically at what we would call the ""background rate"". We can therefore identify periods of history when extinctions were happening much faster than this background rate – this would tell us that there was an additional environmental or ecological pressure creating more extinctions than we would expect. However, mass extinctions are periods with _much_ higher extinction rates than normal. They are defined by both magnitude and rate. Magnitude is the percentage of species that are lost. Rate is how quickly this happens. These metrics are inevitably linked, but we need both to qualify as a mass extinction. In a mass extinction, at least 75% of species go extinct within a relatively (by geological standard) short period of time.3 Typically less than two million years. # The five mass extinctions There have been five mass extinction events in Earth’s history, at least since 500 million years ago. We know very little about extinction events in the Precambrian and early Cambrian earlier, which predate this.4 These are called the ""Big Five"" for obvious reasons. In the chart, we see the timing of events in Earth’s history.5 It shows the changing extinction rate (measured as the number of families that went extinct per million years). Again, note that this number was never zero: background extinction rates were low – typically less than 5 families per million years – but ever-present. We see the spikes in extinction rates marked as the five events: 0. **End Ordovician** (444 million years ago; mya) 1. **Late Devonian** (360 mya) 2. **End Permian** (250 mya) 3. **End Triassic** (200 mya) – many people mistake this as the event that killed off the dinosaurs. But in fact, they were killed off at the end of the Cretaceous period – the fifth of the ""Big Five"". 4. **End Cretaceous** (65 mya) – the event that killed off the dinosaurs. Finally, at the end of the timeline, we have the question of what will come. Perhaps we are headed for a sixth mass extinction. But we are currently far from that point. There are a range of trajectories that the extinction rate could take in the decades and centuries to follow; which one we follow is determined by us. # What caused the five mass extinctions? All of the ""Big Five"" were caused by some combination of rapid and dramatic changes in climate, combined with significant changes in the composition of environments on land or the ocean (such as ocean acidification or acid rain from intense volcanic activity). In the table here, I detail the proposed causes for each of the five extinction events.6 |**Extinction Event**|**Age(mya)**|**Percentage of species lost**|**Cause of extinction**| |End Ordovician|444|86%|Intense glacial and interglacial periods created large sea-level swings and moved shorelines dramatically. The tectonic uplift of the Appalachian mountains created lots of weathering, sequestration of CO2, and with it, changes in climate and ocean chemistry.| |Late Devonian|360|75%|Rapid growth and diversification of land plants generated rapid and severe global cooling.| |End Permian|250|96%|Intense volcanic activity in Siberia. This caused global warming. Elevated CO2 and sulfur (H2S) levels from volcanoes caused ocean acidification, acid rain, and other changes in ocean and land chemistry.| |End Triassic|200|80%|Underwater volcanic activity in the Central Atlantic Magmatic Province (CAMP) caused global warming and a dramatic change in the chemical composition of the oceans.| |End Cretaceous|65|76%|Asteroid impact in Yucatán, Mexico. This caused a global cataclysm and rapid cooling. Some changes may have already pre-dated this asteroid, with intense volcanic activity and tectonic uplift.| Jablonski D (1986) [Mass and background extinctions: the alternation of macroevolutionary regimes](https://science.sciencemag.org/content/231/4734/129.abstract?casa_token=EoFixpArJnsAAAAA:R4ejNN9Ccy8BjYWiJJMfWUToj6qJSJFQ8jWGiMsL_x2OoBRfsrBdze0p8n6kvYdps25LiL8hcRKwrcy9). _Science_ 231:129–133 Raup DM (1991) [A kill curve for Phanerozoic marine species](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=A+Kill+Curve+For+Phanerozoic+Marine+Species+David+M.+Raup+Paleobiology+Vol.+17%2C+No.+1+%28Winter%2C+1991%29%2C+pp.+37-48+%2812+pages%29&btnG=). _Paleobiology_. 17:37–48. We can see a 75% reduction in species in two ways: high extinction or very low speciation rates. If speciation – the creation of new species – slows down a lot, the extinction rate does not need to be as high as we would expect in order to deplete species numbers by 75%. These events are sometimes called ""mass depletions"" but are treated the same way as mass extinctions. Jenkins RJF (1989) The supposed terminal Precambrian extinction event in relation to the Cnidaria. Memoirs of the Association of Australasian Paleontologists 8:307–317. This data and detail comes from multiple sources: Barnosky, A. D., Matzke, N., Tomiya, S., Wogan, G. O., Swartz, B., Quental, T. B., ... & Ferrer, E. A. (2011). Has the Earth’s sixth mass extinction already arrived? Nature, 471(7336), 51-57. McCallum, M. L. (2015). Vertebrate biodiversity losses point to a sixth mass extinction. Biodiversity and Conservation, 24(10), 2497-2519. Howard Hughes Medical Institute. Barnosky, A. D., Matzke, N., Tomiya, S., Wogan, G. O., Swartz, B., Quental, T. B., ... & Ferrer, E. A. (2011). [Has the Earth’s sixth mass extinction already arrived?](https://www.nature.com/articles/nature09678). _Nature_, _471_(7336), 51-57.",There have been five mass extinctions in Earth's history 1wJrR7L_g5slr8k9TZyzPbr5_w0yUd-55ua_rezY8uz0,joe-hasell,author,"{""bio"": [{""type"": ""text"", ""value"": [{""text"": ""Joe joined us in 2017. As Head of Product and Design, he coordinates efforts to develop tools and layouts that make our research and data more accessible and understandable. He also contributes as a researcher, where his work focuses on global trends in poverty and economic inequality. He is currently working towards a Ph.D. at the University of Oxford."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""body"": [{""type"": ""pill-row"", ""pills"": [{""url"": ""https://docs.google.com/document/d/1UjJLoGF5VMfn8U4C3ZSJQrPhc_jFRkeEhrhz5Ksfq7M/edit"", ""text"": ""Poverty""}, {""url"": ""https://docs.google.com/document/d/1yzOrFd6uWvrAl2oFB3S67oOSbhgL1ffPHxjAqS7i-4w/edit"", ""text"": ""Economic inequality""}, {""url"": ""https://docs.google.com/document/d/1gVSV2gqzPSTMI80gmHEgbaKeWalj6daqe519RtZaJ7I/edit"", ""text"": ""Economic Growth""}], ""title"": ""Topics covered by Joe on Our World in Data"", ""parseErrors"": []}, {""rows"": [], ""type"": ""research-and-writing"", ""latest"": {}, ""heading"": ""Featured work"", ""primary"": [{""value"": {""url"": ""https://docs.google.com/document/d/1M2S6EP-CAZL1Oi4szQ5k0BiuPUdaUDbAXTCjnwsQK3o/edit""}}], ""secondary"": [{""value"": {""url"": ""https://docs.google.com/document/d/1w6ymndlipKyB8QEQsvpMvB3qC9Fn1QiLAUV_nnjGWtc/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/17-2C6H3kPd7w9DxWT2RG2QuLP0Pzxvm4H2RVsWFWUzM/edit""}}], ""parseErrors"": [], ""hide-authors"": true}], ""refs"": {""errors"": [], ""definitions"": {}}, ""role"": ""Head of Product and Design"", ""type"": ""author"", ""title"": ""Joe Hasell"", ""authors"": [""Our World in Data team""], ""socials"": {""type"": ""socials"", ""links"": [{""url"": ""https://twitter.com/joehasell"", ""text"": ""@JoeHasell"", ""type"": ""x""}], ""parseErrors"": []}, ""featured-image"": ""joe-hasell.jpg""}",1,2024-03-18 10:57:40,2024-05-28 12:19:29,2024-04-19 11:38:13,unlisted,ALBJ4LsVGdz397rIjc-FLfO5XlUK43iwZHDsRBI52NcOJs2T7J7oo8S9tencpKxkR1yqrON61N7JbJ0C4pEHpA,,"### Topics covered by Joe on Our World in Data * [Poverty](https://docs.google.com/document/d/1UjJLoGF5VMfn8U4C3ZSJQrPhc_jFRkeEhrhz5Ksfq7M/edit) * [Economic inequality](https://docs.google.com/document/d/1yzOrFd6uWvrAl2oFB3S67oOSbhgL1ffPHxjAqS7i-4w/edit) * [Economic Growth](https://docs.google.com/document/d/1gVSV2gqzPSTMI80gmHEgbaKeWalj6daqe519RtZaJ7I/edit) ## Featured work * https://docs.google.com/document/d/1M2S6EP-CAZL1Oi4szQ5k0BiuPUdaUDbAXTCjnwsQK3o/edit ,* https://docs.google.com/document/d/1w6ymndlipKyB8QEQsvpMvB3qC9Fn1QiLAUV_nnjGWtc/edit ,* https://docs.google.com/document/d/17-2C6H3kPd7w9DxWT2RG2QuLP0Pzxvm4H2RVsWFWUzM/edit * [@JoeHasell](https://twitter.com/joehasell) Joe joined us in 2017. As Head of Product and Design, he coordinates efforts to develop tools and layouts that make our research and data more accessible and understandable. He also contributes as a researcher, where his work focuses on global trends in poverty and economic inequality. He is currently working towards a Ph.D. at the University of Oxford.",Joe Hasell 1wF6Z7SG8ZSaiB0Mhy1vuE4UZRTrYe_QK5mD9KTv1MfU,life-expectancy-how-is-it-calculated-and-how-should-it-be-interpreted,article,"{""toc"": [{""slug"": ""how-is-life-expectancy-calculated"", ""text"": ""How is life expectancy calculated?"", ""title"": ""How is life expectancy calculated?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-else-can-we-learn-from-life-tables"", ""text"": ""What else can we learn from 'life tables'?"", ""title"": ""What else can we learn from 'life tables'?"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Life expectancy has "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/life-expectancy#twice-as-long-life-expectancy-around-the-world"", ""children"": [{""text"": ""doubled in all world regions"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". What does this mean exactly?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Despite its importance and prominence in research and policy, it is surprisingly difficult to find a simple yet detailed description of what “life expectancy” actually means. In this section, we try to fill this gap."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The term \""life expectancy\"" refers to the number of years a person can expect to live. By definition, life expectancy is based on an estimate of the average age that members of a particular population group will be when they die."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In practice, however, things are often more complicated:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One important distinction and clarification is the difference between "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""cohort"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""period life expectancy."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""cohort life expectancy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" is the average life length of a particular cohort – a group of individuals born in a given year. When we can track a group of people born in a particular year, many decades ago, and observe the exact date in which each one of them died then we can calculate this "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""cohort's"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" life expectancy by simply calculating the average of the ages of all members when they died."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can think of life expectancy in a particular year as the age a person born in that year would expect to live if the average age of death did not change over their lifetime."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is of course not possible to know this metric before all members of the cohort have died. Because of that, statisticians commonly track members of a particular cohort and predict the average age-at-death for them using a combination of observed mortality rates for past years and projections about mortality rates for future years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""An alternative approach consists in estimating the average length of life for a "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""hypothetical"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" cohort assumed to be exposed, from birth through death, to the mortality rates observed at one particular "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""period"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" – commonly a year. This approach leads to what is known as '"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""period life expectancy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""' and it is the much more commonly used life expectancy metric. It is the definition used by most international organizations, including the UN and the World Bank, when reporting 'life expectancy' figures. Period life expectancy estimates do not take into account how mortality rates are changing over time and instead only reflects the mortality pattern at one point in time. Because of this, period life expectancy figures are usually different to cohort life expectancy figures."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""An example to illustrate the measurement of life expectancy"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since period life expectancy estimates are ubiquitous in research and public debate, it is helpful to use an example to flesh out the concept. Let's consider the map showing life expectancy—specifically period life expectancy—at birth in 2005. You can hover the mouse over a country to display the corresponding estimate."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For Japan, we can see that life expectancy in 2005 was 82.3 years. This means that a hypothetical cohort of infants living through the age-specific mortality of Japan in 2005 could expect to live 82.3 years, under the assumption that mortality patterns observed in 2005 remain constant throughout their lifetime. But if life expectancies are increasing the reality for a cohort born then is that the cohort life expectancy is higher than that period life expectancy. We see this in the data: if you move the slider below the map forward, you'll see that in 2019 the period life expectancy in Japan was 84.6 years, which means that mortality patterns in Japan did improve in the period 2005-2019."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In general, the commonly-used period life expectancies tend to be lower than the cohort life expectancies, because mortality rates were falling over the course of modern development."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/life-expectancy?time=2005&tab=map"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Life expectancy is an average"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""An important point to bear in mind when interpreting life expectancy estimates is that very few people will die at precisely the age indicated by life expectancy, even if mortality patterns stay constant."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For example, very few of the infants born in South Africa in 2009 will die at 52.2 years of age, as per the figures in the map above. Most will die much earlier or much later, since the risk of death is not uniform across the lifetime. Life expectancy is the average."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In societies with high infant mortality rates many people die in the first few years of life; but once they survive childhood, people often live much longer. Indeed, this is a common source of confusion in the interpretation of life expectancy figures: It is perfectly possible that a given population has a low life expectancy at birth, and yet has a large proportion of old people."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Commonly we study life expectancy at birth, but life expectancy at higher ages are also relevant"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Given that life expectancy at birth is highly sensitive to the rate of death in the first few years of life, it is common to report life expectancy figures at different ages, both under the period and cohort approaches. For example, the UN estimates that "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/life-expectancy-at-age-15?tab=chart&time=1751..2005&country=~OWID_WRL"", ""children"": [{""text"": ""the (period) global life expectancy at age 15 in 2005 was 73.6 years"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". This means that the group of 15-year-old children alive around the world in 2005 could expect to live another 58.6 years (i.e. until the age of 73.6), provided that mortality patterns observed in 2005 remained constant throughout their lifetime."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Finally, another point to bear in mind is that period and cohort life expectancy estimates are statistical measures, and they do not take into account any person-specific factors such as lifestyle choices. Clearly, the length of life for an average person is not very informative about the predicted length of life for a person living a particularly unhealthy lifestyle."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How is life expectancy calculated?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In practical terms, estimating life expectancy entails predicting the probability of surviving successive years of life, based on observed age-specific mortality rates. How is this actually done?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Age-specific mortality rates are usually estimated by counting (or projecting) the number of age-specific deaths in a time interval (e.g. the number of people aged 10-15 who died in the year 2005), and dividing by the total observed (or projected) population alive at a given point within that interval (e.g. the number of people aged 10-15 alive on 1 July 2015)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To ensure that the resulting estimates of the probabilities of death within each age interval are smooth across the lifetime, it is common to use mathematical formulas, to model how the force of mortality changes within and across age intervals. Specifically, it is often assumed that the proportion of people dying in an age interval starting in year x and ending in year n+x corresponds to:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""narrow"", ""type"": ""image"", ""filename"": ""proportion-dying-latex.svg"", ""hasOutline"": false, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""where m(n,x) is the age-specific mortality rate as measured in the middle of that interval (a term often referred to as the 'central death rate' for the age interval)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Once we have estimates of the fraction of people dying across age intervals, it is simple to calculate a 'life table' showing the evolving probabilities of survival and the corresponding life expectancies by age. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/app/uploads/2017/07/Example-Life-Table-US.png"", ""children"": [{""text"": ""Here is an example of a life table from the US"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.measureevaluation.org/resources/training/online-courses-and-resources/non-certificate-courses-and-mini-tutorials/multiple-decrement-life-tables/lesson-3"", ""children"": [{""text"": ""this tutorial from MEASURE Evaluation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" explains how life tables are constructed, step by step (see Section 3.2 'The Fergany Method')."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Period life expectancy figures can be obtained from 'period life tables' (i.e. life tables that rely on age-specific mortality rates observed from deaths among individuals of different age groups at a fixed point in time). And similarly, cohort life expectancy figures can be obtained from 'cohort life tables' (i.e. life tables that rely on age-specific mortality rates observed from tracking and forecasting the death and survival of a group of people as they become older)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For some countries and for some time intervals, it is only possible to reconstruct life tables from either period or cohort mortality data. As a consequence, in some instances—for example in obtaining "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/life-expectancy"", ""children"": [{""text"": ""historical estimates of life expectancy across world regions"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""—it is necessary to combine period and cohort data. In these cases, the resulting life expectancy estimates cannot be simply classified into the 'period' or 'cohort' categories."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What else can we learn from 'life tables'?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Life tables are not just instrumental to the production of life expectancy figures (as noted above), they also provide many other perspectives on the mortality of a population. For example, they allow for the production of 'population survival curves', which show the share of people who are expected to survive various successive ages. This chart provides an example, plotting survival curves for individuals born at different points in time, using cohort life tables from England and Wales."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Survival-Curves-UK.png"", ""hasOutline"": false, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At any age level in the horizontal axis, the curves in this visualization mark the estimated proportion of individuals who are expected to survive that age. As we can see, less than half of the people born in 1851 in England and Wales made it past their 50th birthday. In contrast, more than 95% of the people born in England and Wales today can expect to live longer than 50 years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since life expectancy estimates only describe averages, these indicators are complementary, and help us understand how health is distributed across time and space. In our entry on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/life-expectancy/"", ""children"": [{""text"": ""Life Expectancy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" you can read more about related complementary indicators, such as the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/life-expectancy/#median-age-by-country"", ""children"": [{""text"": ""median age of a population."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""d3359009f550eefe792da1da0d482cd538727332"": {""id"": ""d3359009f550eefe792da1da0d482cd538727332"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The underlying assumption is that the force of mortality is constant within each age interval. The seminal reference introducing this method is Fergany (1971) \""On the Human Survivorship Function and Life Table Construction,\"" Demography8(3):331-334)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""\""Life Expectancy\"" – What does this actually mean?"", ""authors"": [""Esteban Ortiz-Ospina""], ""excerpt"": ""How is life expectancy calculated, what does it mean, and what’s the difference between period and cohort life expectancy?"", ""dateline"": ""August 28, 2017"", ""subtitle"": ""How is life expectancy calculated and what does it mean? What’s the difference between period and cohort life expectancy?"", ""thumbnail"": ""Survival-Curves-UK.png""}",1,2023-10-12 10:51:44,2017-08-28 16:00:00,2024-03-18 15:41:59,listed,ALBJ4LubkJPII2suDYjrYD70lUdXPiWMgYiZB9WLilrF6bY6ZokYphpSS2j-xhQAh2OayEcytlkcQaLamTJOUA,,"Life expectancy has [doubled in all world regions](https://ourworldindata.org/life-expectancy#twice-as-long-life-expectancy-around-the-world). What does this mean exactly? Despite its importance and prominence in research and policy, it is surprisingly difficult to find a simple yet detailed description of what “life expectancy” actually means. In this section, we try to fill this gap. The term ""life expectancy"" refers to the number of years a person can expect to live. By definition, life expectancy is based on an estimate of the average age that members of a particular population group will be when they die. In practice, however, things are often more complicated: One important distinction and clarification is the difference between _cohort_ and _period life expectancy._ The **cohort life expectancy** is the average life length of a particular cohort – a group of individuals born in a given year. When we can track a group of people born in a particular year, many decades ago, and observe the exact date in which each one of them died then we can calculate this _cohort's_ life expectancy by simply calculating the average of the ages of all members when they died. You can think of life expectancy in a particular year as the age a person born in that year would expect to live if the average age of death did not change over their lifetime. It is of course not possible to know this metric before all members of the cohort have died. Because of that, statisticians commonly track members of a particular cohort and predict the average age-at-death for them using a combination of observed mortality rates for past years and projections about mortality rates for future years. An alternative approach consists in estimating the average length of life for a _hypothetical_ cohort assumed to be exposed, from birth through death, to the mortality rates observed at one particular _period_ – commonly a year. This approach leads to what is known as '**period life expectancy**' and it is the much more commonly used life expectancy metric. It is the definition used by most international organizations, including the UN and the World Bank, when reporting 'life expectancy' figures. Period life expectancy estimates do not take into account how mortality rates are changing over time and instead only reflects the mortality pattern at one point in time. Because of this, period life expectancy figures are usually different to cohort life expectancy figures. # An example to illustrate the measurement of life expectancy Since period life expectancy estimates are ubiquitous in research and public debate, it is helpful to use an example to flesh out the concept. Let's consider the map showing life expectancy—specifically period life expectancy—at birth in 2005. You can hover the mouse over a country to display the corresponding estimate. For Japan, we can see that life expectancy in 2005 was 82.3 years. This means that a hypothetical cohort of infants living through the age-specific mortality of Japan in 2005 could expect to live 82.3 years, under the assumption that mortality patterns observed in 2005 remain constant throughout their lifetime. But if life expectancies are increasing the reality for a cohort born then is that the cohort life expectancy is higher than that period life expectancy. We see this in the data: if you move the slider below the map forward, you'll see that in 2019 the period life expectancy in Japan was 84.6 years, which means that mortality patterns in Japan did improve in the period 2005-2019. In general, the commonly-used period life expectancies tend to be lower than the cohort life expectancies, because mortality rates were falling over the course of modern development. # Life expectancy is an average An important point to bear in mind when interpreting life expectancy estimates is that very few people will die at precisely the age indicated by life expectancy, even if mortality patterns stay constant. For example, very few of the infants born in South Africa in 2009 will die at 52.2 years of age, as per the figures in the map above. Most will die much earlier or much later, since the risk of death is not uniform across the lifetime. Life expectancy is the average. In societies with high infant mortality rates many people die in the first few years of life; but once they survive childhood, people often live much longer. Indeed, this is a common source of confusion in the interpretation of life expectancy figures: It is perfectly possible that a given population has a low life expectancy at birth, and yet has a large proportion of old people. # Commonly we study life expectancy at birth, but life expectancy at higher ages are also relevant Given that life expectancy at birth is highly sensitive to the rate of death in the first few years of life, it is common to report life expectancy figures at different ages, both under the period and cohort approaches. For example, the UN estimates that [the (period) global life expectancy at age 15 in 2005 was 73.6 years](https://ourworldindata.org/grapher/life-expectancy-at-age-15?tab=chart&time=1751..2005&country=~OWID_WRL). This means that the group of 15-year-old children alive around the world in 2005 could expect to live another 63.6 years (i.e. until the age of 73.6), provided that mortality patterns observed in 2005 remained constant throughout their lifetime. Finally, another point to bear in mind is that period and cohort life expectancy estimates are statistical measures, and they do not take into account any person-specific factors such as lifestyle choices. Clearly, the length of life for an average person is not very informative about the predicted length of life for a person living a particularly unhealthy lifestyle. ## How is life expectancy calculated? In practical terms, estimating life expectancy entails predicting the probability of surviving successive years of life, based on observed age-specific mortality rates. How is this actually done? Age-specific mortality rates are usually estimated by counting (or projecting) the number of age-specific deaths in a time interval (e.g. the number of people aged 10-15 who died in the year 2005), and dividing by the total observed (or projected) population alive at a given point within that interval (e.g. the number of people aged 10-15 alive on 1 July 2015). To ensure that the resulting estimates of the probabilities of death within each age interval are smooth across the lifetime, it is common to use mathematical formulas, to model how the force of mortality changes within and across age intervals. Specifically, it is often assumed that the proportion of people dying in an age interval starting in year x and ending in year n+x corresponds to: where m(n,x) is the age-specific mortality rate as measured in the middle of that interval (a term often referred to as the 'central death rate' for the age interval).1 Once we have estimates of the fraction of people dying across age intervals, it is simple to calculate a 'life table' showing the evolving probabilities of survival and the corresponding life expectancies by age. [Here is an example of a life table from the US](https://ourworldindata.org/app/uploads/2017/07/Example-Life-Table-US.png), and [this tutorial from MEASURE Evaluation](https://www.measureevaluation.org/resources/training/online-courses-and-resources/non-certificate-courses-and-mini-tutorials/multiple-decrement-life-tables/lesson-3) explains how life tables are constructed, step by step (see Section 3.2 'The Fergany Method'). Period life expectancy figures can be obtained from 'period life tables' (i.e. life tables that rely on age-specific mortality rates observed from deaths among individuals of different age groups at a fixed point in time). And similarly, cohort life expectancy figures can be obtained from 'cohort life tables' (i.e. life tables that rely on age-specific mortality rates observed from tracking and forecasting the death and survival of a group of people as they become older). For some countries and for some time intervals, it is only possible to reconstruct life tables from either period or cohort mortality data. As a consequence, in some instances—for example in obtaining [historical estimates of life expectancy across world regions](https://ourworldindata.org/grapher/life-expectancy)—it is necessary to combine period and cohort data. In these cases, the resulting life expectancy estimates cannot be simply classified into the 'period' or 'cohort' categories. ## What else can we learn from 'life tables'? Life tables are not just instrumental to the production of life expectancy figures (as noted above), they also provide many other perspectives on the mortality of a population. For example, they allow for the production of 'population survival curves', which show the share of people who are expected to survive various successive ages. This chart provides an example, plotting survival curves for individuals born at different points in time, using cohort life tables from England and Wales. At any age level in the horizontal axis, the curves in this visualization mark the estimated proportion of individuals who are expected to survive that age. As we can see, less than half of the people born in 1851 in England and Wales made it past their 50th birthday. In contrast, more than 95% of the people born in England and Wales today can expect to live longer than 50 years. Since life expectancy estimates only describe averages, these indicators are complementary, and help us understand how health is distributed across time and space. In our entry on [Life Expectancy](https://ourworldindata.org/life-expectancy/) you can read more about related complementary indicators, such as the [median age of a population.](https://ourworldindata.org/life-expectancy/#median-age-by-country) The underlying assumption is that the force of mortality is constant within each age interval. The seminal reference introducing this method is Fergany (1971) ""On the Human Survivorship Function and Life Table Construction,"" Demography8(3):331-334).","""Life Expectancy"" – What does this actually mean?" 1wDBl7-adCcwzHGMsrQNYxX22qkHXhQDPEVUvHMNchjc,how-urban-is-the-world,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Urban living is a relatively "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/urbanization#urbanization-over-the-very-long-term"", ""children"": [{""text"": ""recent feature"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of human history."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In our entry on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/urbanization"", ""children"": [{""text"": ""Urbanization"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" we look at the long-run history of urban populations.  By 1800, urban shares were "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/urbanization#urban-shares-over-the-last-500-years"", ""children"": [{""text"": ""still well below 10 percent"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", even across today's rich countries."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It wasn't until the 20th century that rates of urbanization really began to increase substantially. This is seen in the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/urban-vs-rural-majority"", ""children"": [{""text"": ""second half of the century"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in particular."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But how urban is the world today? How many people live in urban areas?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What we know about urban populations and why it matters"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Before looking in more detail at the differences in estimates of urban populations, we should first clarify what we "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""do "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""know:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""globally more people live in urbanized settings than not (disputes in these figures are all above the 50% urban mark);"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""the broad distribution and density of where people live across the world (sometimes at very high resolution);"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""although it can seem like our expanding cities take up a lot of land, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/land-use-over-the-long-term?stackMode=relative&time=latest"", ""children"": [{""text"": ""less than 2% of global land"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" is defined as built-up area;"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""rates of urbanization have been increasing rapidly across all regions (in 1800, less than 10% of people across all regions lived in urban areas);"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""urbanization is expected to continue to increase with rising incomes and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/urbanization#agricultural-employment-vs-urbanization"", ""children"": [{""text"": ""shifts away from employment in agriculture"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "";"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""disagreements in urban population numbers arise from definition or boundary differences in what makes a population 'urban'."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Whilst disagreement on the numbers can seem irrelevant, understanding cities, urbanization rates, the distribution, and the density of people matters. The allocation and distribution of resources — ranging from housing and transport access to healthcare, education, and employment opportunities — should all be dependent on where people live. Understanding the distribution of people in a given country is essential to make sure the appropriate resources and services are available where they're needed."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The UN's 11th "", ""spanType"": ""span-simple-text""}, {""url"": ""https://sdg-tracker.org/"", ""children"": [{""text"": ""Sustainable Development Goal (SDG)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" is to \"""", ""spanType"": ""span-simple-text""}, {""url"": ""https://sdg-tracker.org/cities"", ""children"": [{""text"": ""make cities inclusive, safe, resilient and sustainable"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""\"". If our aim is to develop resource-efficient, inclusive cities, understanding how many people they must provide for is essential for urban planning."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Let's, therefore, look at the conflicting estimates of how urban our world is, and where these differences come from."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""UN estimates: 55% of people live in urban areas"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At first glance, this seems like a simple question to answer. Figures reported from the United Nations (UN) deliver a straightforward answer."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart here we see the total number of people defined as living in urban and rural areas, extending from 1960 to 2020."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This is based on nationally-collated census figures, combined with UN estimates where census data is unavailable. Here we see that in 1960 twice as many people lived in rural settings (2 billion) than in urban areas (1 billion). In 2007, urban and rural populations were almost exactly equal at 3.33 billion each. In 2016, urban populations increased to 4.4 billion; while the world's rural population had increased only marginally to 3.4 billion."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The UN figures are the most widely referenced and cited on global urbanization. However, they're not without their critics: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.reuters.com/article/us-global-cities/everything-weve-heard-about-global-urbanization-turns-out-to-be-wrong-researchers-idUSKBN1K21UU"", ""children"": [{""text"": ""some researchers suggest"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that far more people live in urban areas than these figures suggest. Why are they so contested?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/urban-and-rural-population"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""How is an urban area defined?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""'What defines an urban area?' lies at the center of these debates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There is currently no universal definition of what 'urban' means. The UN reports figures based on nationally-defined urban shares. The problem, however, is that countries adopt very different definitions of urbanization. Not only do the thresholds of urban versus rural vary, but the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""types"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of metrics used also differ. Some countries use minimum population thresholds, others use population density, infrastructure development, employment type, or simply the population of pre-defined cities."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the table, we highlight the varied definitions across a selection of countries. The UN World Urbanization Prospects database also provides the full "", ""spanType"": ""span-simple-text""}, {""url"": ""https://esa.un.org/unpd/wup/Download/"", ""children"": [{""text"": ""downloadable list"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of statistical definitions for each country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""National definitions of 'urban area' as used for a custom selection of countries"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""type"": ""heading"", ""level"": 5, ""parseErrors"": []}, {""rows"": [{""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Country"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""National definition of ‘urban’"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Argentina"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Localities with 2,000 inhabitants or more."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Sweden"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Built-up areas with 200 inhabitants or more and where houses are at most 200 metres apart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Japan"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Cities defined as shi. In general, shi refers to a municipality that satisfies the following conditions: (1) 50,000 inhabitants or more; (2) 60 per cent or more of the houses located in the main built-up areas; (3) 60 per cent or more of the population (including their dependents) engaged in manufacturing, trade or other urban type of business."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""India"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Statutory places with a municipality, corporation, cantonment board or notified town area committee and places satisfying all of the following three criteria: (1) 5,000 inhabitants or more; (2) at least 75 per cent of male working population engaged in non-agricultural pursuits; and (3) at least 400 inhabitants per square kilometre."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Zimbabwe"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Places officially designated as urban, as well as places with 2,500 inhabitants or more whose population resides in a compact settlement pattern and where more than 50 per cent of the employed persons are engaged in non-agricultural occupations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Singapore"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Entire population."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Uruguay"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Cities officially designated as such."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}], ""size"": ""narrow"", ""type"": ""table"", ""template"": ""header-row"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The table illustrates the broad range of definitions between countries which compromises cross-country comparisons. And since the reported global figure is simply the sum of nationally-reported shares, the lack of a universal definition is also problematic for these aggregated figures."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Even if we could define a single metric to use — such as a minimum population threshold in a settlement — countries adopt very different thresholds."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart here we have mapped the minimum population threshold for countries who adopt this within their definition of 'urban'. 2000 and 5000 inhabitants were jointly the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/countries-with-minimum-urban-threshold"", ""children"": [{""text"": ""most frequently-adopted"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" threshold. However, the variation across countries was vast. Sweden and Denmark set this threshold at only 200 inhabitants; Japan at 50,000 (a 250-fold difference)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/population-threshold-for-urban-area"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""European Commission estimates: 85% of people live in urban areas"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Critics of current UN figures, therefore, say that such varied definitions of 'urban' lead to a significant underestimation of the world's urban population. Researchers from the European Commission, for example, reported that 85% of people live in urban areas."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Its project, "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Atlas of the Human Planet"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", combines high-resolution satellite imagery with national census data to derive its estimates of urban and rural settlements."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The European Commission applied a "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ghsl.jrc.ec.europa.eu/data.php"", ""children"": [{""text"": ""universal definition"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of settlements across all countries:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Urban center"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": must have a minimum of 50,000 inhabitants "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""plus"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" a population density of at least 1500 people per square kilometre (km"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}, {""text"": "") or density of build-up area greater than 50%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Urban cluster"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": must have a minimum of 5,000 inhabitants "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""plus"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" a population density of at least 300 people per square kilometre (km"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}, {""text"": "")."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Rural"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": fewer than 5,000 inhabitants."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Using these definitions, it reports that 52% of the world lived in urban centers, 33% in urban clusters, and 15% in rural areas in 2015. This makes the total urban share 85% (more than 6.1 billion people). The reported urban share by continent is shown in the chart here."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The European Commission's estimates are also not without its critics. Researchers at the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Marron Institute of Urban Management"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" (New York University) challenged these figures as a gross overestimation."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The authors suggest multiple reasons why such figures are too high: based on agricultural employment figures, they estimate urban populations cannot exceed 60%; the low urban-density threshold adopted by the European Commission means entire cropland regions are classified as urban; and that this low-density threshold is inconsistent with observed population densities on the fringes of cities."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/urban-share-european-commission"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Will we ever reach a consensus on urban population?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Clearly how we define an urban area has a significant impact on its estimated population. UN figures report 4 billion, whilst the European Commission reports 6 billion (a difference of one-third)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While there are clear differences in estimates at the global level, the overall trend in urbanization at national levels (regardless of their definition) is still important. It's vital for India, for example, to know that from 1990 to 2016, its urban population increased by 148 million (increasing from every 4th to every 3rd person). The rate of this change is important for its evaluation of progress, demographic change, and national planning. The lack of consensus on figures at the global level therefore shouldn't overshadow what they represent at national levels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But would the world adopt a standardized definition? The UN Statistics Division has convened multiple expert groups in recent years to try to work towards a common definition, but none have been successful."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""With such a wide array of national definitions, achieving this would be a difficult task. Countries have the right to define what they consider to be urban and rural settlements. One proposed option is to maintain individual definitions for national figures but to adopt a new universal definition for estimating the global and/or regional share."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This may, at least, bring us one step closer to an agreement on how urban the world really is."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""52a3843486423eb0486d742eb1845fdcdea36595"": {""id"": ""52a3843486423eb0486d742eb1845fdcdea36595"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""UN World Urbanization Prospects (2018). Available at: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://esa.un.org/unpd/wup/Download/"", ""children"": [{""text"": ""https://esa.un.org/unpd/wup/Download/"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""5e479b6c31e9afc9c15547732f94fced08b7e1ca"": {""id"": ""5e479b6c31e9afc9c15547732f94fced08b7e1ca"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""In 1800 when urbanization rates were low, agricultural employment was very high — including in today's rich countries. For example, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/employment-in-agriculture#long-run-perspective-1300-to-today"", ""children"": [{""text"": ""around 60% of the workforce"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in France was employed in agriculture in 1800. Today this figure is only a few percent."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""6f9c56b36fcc32a5b99876be80a206c6d476b5bc"": {""id"": ""6f9c56b36fcc32a5b99876be80a206c6d476b5bc"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Angel et al. (2018). Our Not-So-Urban World. The Marron Institute of Urban Management, New York University. Available at: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://marroninstitute.nyu.edu/uploads/content/Angel_et_al_Our_Not-So-Urban_World,_revised_on_22_Aug_2018_v2.pdf"", ""children"": [{""text"": ""https://marroninstitute.nyu.edu/uploads/content/Angel_et_al_Our_Not-So-Urban_World,_revised_on_22_Aug_2018_v2.pdf"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""87f40c1a1e300193690d851fe635ed271a434c42"": {""id"": ""87f40c1a1e300193690d851fe635ed271a434c42"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""United Nations, Department of Economic and Social Affairs, Population Division (2018). World Urbanization Prospects: The 2018 Revision, Online Edition. Available at: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://esa.un.org/unpd/wup/"", ""children"": [{""text"": ""https://esa.un.org/unpd/wup/"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b15ecf054e76eff93a8e41f6069d923e46b13271"": {""id"": ""b15ecf054e76eff93a8e41f6069d923e46b13271"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Pesaresi, M., Melchiorri, M., Siragusa, A., & Kemper, T. (2016). Atlas of the human planet–Mapping human presence on earth with the global human settlement layer. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""JRC103150. Publications Office of the European Union. Luxembourg (Luxembourg): European Commission, DG JRC"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". Available at: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/atlas-human-planet-mapping-human-presence-earth-global-human-settlement-layer"", ""children"": [{""text"": ""https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/atlas-human-planet-mapping-human-presence-earth-global-human-settlement-layer"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""bd11dcd7ac1d06925a52b4cc2478b659654c9508"": {""id"": ""bd11dcd7ac1d06925a52b4cc2478b659654c9508"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Built-up area is defined as cities, town, villages and human infrastructure."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""bfa7fd95dcb6be5d2f1c5fe9794b0fe76120a997"": {""id"": ""bfa7fd95dcb6be5d2f1c5fe9794b0fe76120a997"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""US Census Bureau. Population: 1790-1990. Available at: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.census.gov/population/censusdata/table-4.pdf"", ""children"": [{""text"": ""https://www.census.gov/population/censusdata/table-4.pdf"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c4ebebc0a780d213f87f4b9563aecd66bf48bc0f"": {""id"": ""c4ebebc0a780d213f87f4b9563aecd66bf48bc0f"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Klein Goldewijk, K. , A. Beusen, and P. Janssen (2010). Long term dynamic modeling of global population and built-up area in a spatially explicit way, HYDE 3 .1. The Holocene20(4):565-573. Available at: "", ""spanType"": ""span-simple-text""}, {""url"": ""http://dx.doi.org/10.1177/0959683609356587"", ""children"": [{""text"": ""http://dx.doi.org/10.1177/0959683609356587."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c4ece5c951a01fe98f47561668e6d1c3e1230637"": {""id"": ""c4ece5c951a01fe98f47561668e6d1c3e1230637"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Note that this data can be viewed for any country or region using the \""change country\"" function in the top-left of the chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""How urban is the world?"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""More than half of the world live in urban areas, and this will continue to grow."", ""dateline"": ""September 27, 2018"", ""sidebar-toc"": false, ""featured-image"": ""urban-and-rural-population.png""}",1,2024-02-01 10:59:13,2018-09-27 12:34:00,2024-02-02 13:30:04,listed,ALBJ4LuHOCK-ZbQbRlIrV5PJ95N4YEQ42KhItIaL7itWhsDv86eotDmY6SHg8ogb4Mt6SiW4zxBkCX-r_ZL6Nw,,"Urban living is a relatively [recent feature](https://ourworldindata.org/urbanization#urbanization-over-the-very-long-term) of human history.1 In our entry on [Urbanization](https://ourworldindata.org/urbanization) we look at the long-run history of urban populations.  By 1800, urban shares were [still well below 10 percent](https://ourworldindata.org/urbanization#urban-shares-over-the-last-500-years), even across today's rich countries.2 It wasn't until the 20th century that rates of urbanization really began to increase substantially. This is seen in the [second half of the century](https://ourworldindata.org/grapher/urban-vs-rural-majority) in particular. But how urban is the world today? How many people live in urban areas? # What we know about urban populations and why it matters Before looking in more detail at the differences in estimates of urban populations, we should first clarify what we _do _know: * globally more people live in urbanized settings than not (disputes in these figures are all above the 50% urban mark); * the broad distribution and density of where people live across the world (sometimes at very high resolution); * although it can seem like our expanding cities take up a lot of land, only [around 1% of global land](https://ourworldindata.org/yields-and-land-use-in-agriculture#how-the-world-s-land-is-used-total-area-sizes-by-type-of-use-cover) is defined as built-up area;3 * rates of urbanization have been increasing rapidly across all regions (in 1800, less than 10% of people across all regions lived in urban areas); * urbanization is expected to continue to increase with rising incomes and [shifts away from employment in agriculture](https://ourworldindata.org/urbanization#agricultural-employment-vs-urbanization);4 * disagreements in urban population numbers arise from definition or boundary differences in what makes a population 'urban'. Whilst disagreement on the numbers can seem irrelevant, understanding cities, urbanization rates, the distribution, and the density of people matters. The allocation and distribution of resources — ranging from housing and transport access to healthcare, education, and employment opportunities — should all be dependent on where people live. Understanding the distribution of people in a given country is essential to make sure the appropriate resources and services are available where they're needed. The UN's 11th [Sustainable Development Goal (SDG)](https://sdg-tracker.org/) is to ""[make cities inclusive, safe, resilient and sustainable](https://sdg-tracker.org/cities)"". If our aim is to develop resource-efficient, inclusive cities, understanding how many people they must provide for is essential for urban planning. Let's, therefore, look at the conflicting estimates of how urban our world is, and where these differences come from. # UN estimates: 55% of people live in urban areas At first glance, this seems like a simple question to answer. Figures reported from the United Nations (UN) deliver a straightforward answer.5 In the chart here we see the total number of people defined as living in urban and rural areas, extending from 1960 to 2020.6 This is based on nationally-collated census figures, combined with UN estimates where census data is unavailable. Here we see that in 1960 twice as many people lived in rural settings (2 billion) than in urban areas (1 billion). In 2007, urban and rural populations were almost exactly equal at 3.33 billion each. In 2016, urban populations increased to 4.4 billion; while the world's rural population had increased only marginally to 3.4 billion. The UN figures are the most widely referenced and cited on global urbanization. However, they're not without their critics: [some researchers suggest](https://www.reuters.com/article/us-global-cities/everything-weve-heard-about-global-urbanization-turns-out-to-be-wrong-researchers-idUSKBN1K21UU) that far more people live in urban areas than these figures suggest. Why are they so contested? # How is an urban area defined? 'What defines an urban area?' lies at the center of these debates. There is currently no universal definition of what 'urban' means. The UN reports figures based on nationally-defined urban shares. The problem, however, is that countries adopt very different definitions of urbanization. Not only do the thresholds of urban versus rural vary, but the _types_ of metrics used also differ. Some countries use minimum population thresholds, others use population density, infrastructure development, employment type, or simply the population of pre-defined cities. In the table, we highlight the varied definitions across a selection of countries. The UN World Urbanization Prospects database also provides the full [downloadable list](https://esa.un.org/unpd/wup/Download/) of statistical definitions for each country. ##### National definitions of 'urban area' as used for a custom selection of countries7 |Country|National definition of ‘urban’| |Argentina|Localities with 2,000 inhabitants or more.| |Sweden|Built-up areas with 200 inhabitants or more and where houses are at most 200 metres apart.| |Japan|Cities defined as shi. In general, shi refers to a municipality that satisfies the following conditions: (1) 50,000 inhabitants or more; (2) 60 per cent or more of the houses located in the main built-up areas; (3) 60 per cent or more of the population (including their dependents) engaged in manufacturing, trade or other urban type of business.| |India|Statutory places with a municipality, corporation, cantonment board or notified town area committee and places satisfying all of the following three criteria: (1) 5,000 inhabitants or more; (2) at least 75 per cent of male working population engaged in non-agricultural pursuits; and (3) at least 400 inhabitants per square kilometre.| |Zimbabwe|Places officially designated as urban, as well as places with 2,500 inhabitants or more whose population resides in a compact settlement pattern and where more than 50 per cent of the employed persons are engaged in non-agricultural occupations.| |Singapore|Entire population.| |Uruguay|Cities officially designated as such.| The table illustrates the broad range of definitions between countries which compromises cross-country comparisons. And since the reported global figure is simply the sum of nationally-reported shares, the lack of a universal definition is also problematic for these aggregated figures. Even if we could define a single metric to use — such as a minimum population threshold in a settlement — countries adopt very different thresholds. In the chart here we have mapped the minimum population threshold for countries who adopt this within their definition of 'urban'. 2000 and 5000 inhabitants were jointly the [most frequently-adopted](https://ourworldindata.org/grapher/countries-with-minimum-urban-threshold) threshold. However, the variation across countries was vast. Sweden and Denmark set this threshold at only 200 inhabitants; Japan at 50,000 (a 250-fold difference). # European Commission estimates: 85% of people live in urban areas Critics of current UN figures, therefore, say that such varied definitions of 'urban' lead to a significant underestimation of the world's urban population. Researchers from the European Commission, for example, reported that 85% of people live in urban areas.8 Its project, _Atlas of the Human Planet_, combines high-resolution satellite imagery with national census data to derive its estimates of urban and rural settlements. The European Commission applied a [universal definition](https://ghsl.jrc.ec.europa.eu/data.php) of settlements across all countries: * **Urban center**: must have a minimum of 50,000 inhabitants **plus** a population density of at least 1500 people per square kilometre (km2) or density of build-up area greater than 50%. * **Urban cluster**: must have a minimum of 5,000 inhabitants **plus** a population density of at least 300 people per square kilometre (km2). * **Rural**: fewer than 5,000 inhabitants. Using these definitions, it reports that 52% of the world lived in urban centers, 33% in urban clusters, and 15% in rural areas in 2015. This makes the total urban share 85% (more than 6.1 billion people). The reported urban share by continent is shown in the chart here. The European Commission's estimates are also not without its critics. Researchers at the _Marron Institute of Urban Management_ (New York University) challenged these figures as a gross overestimation.9 The authors suggest multiple reasons why such figures are too high: based on agricultural employment figures, they estimate urban populations cannot exceed 60%; the low urban-density threshold adopted by the European Commission means entire cropland regions are classified as urban; and that this low-density threshold is inconsistent with observed population densities on the fringes of cities. # Will we ever reach a consensus on urban population? Clearly how we define an urban area has a significant impact on its estimated population. UN figures report 4 billion, whilst the European Commission reports 6 billion (a difference of one-third). While there are clear differences in estimates at the global level, the overall trend in urbanization at national levels (regardless of their definition) is still important. It's vital for India, for example, to know that from 1990 to 2016, its urban population increased by 148 million (increasing from every 4th to every 3rd person). The rate of this change is important for its evaluation of progress, demographic change, and national planning. The lack of consensus on figures at the global level therefore shouldn't overshadow what they represent at national levels. But would the world adopt a standardized definition? The UN Statistics Division has convened multiple expert groups in recent years to try to work towards a common definition, but none have been successful. With such a wide array of national definitions, achieving this would be a difficult task. Countries have the right to define what they consider to be urban and rural settlements. One proposed option is to maintain individual definitions for national figures but to adopt a new universal definition for estimating the global and/or regional share. This may, at least, bring us one step closer to an agreement on how urban the world really is. Klein Goldewijk, K. , A. Beusen, and P. Janssen (2010). Long term dynamic modeling of global population and built-up area in a spatially explicit way, HYDE 3 .1. The Holocene20(4):565-573. Available at: [http://dx.doi.org/10.1177/0959683609356587.](http://dx.doi.org/10.1177/0959683609356587) US Census Bureau. Population: 1790-1990. Available at: [https://www.census.gov/population/censusdata/table-4.pdf](https://www.census.gov/population/censusdata/table-4.pdf). Built-up area is defined as cities, town, villages and human infrastructure. In 1800 when urbanization rates were low, agricultural employment was very high — including in today's rich countries. For example, [around 60% of the workforce](https://ourworldindata.org/employment-in-agriculture#long-run-perspective-1300-to-today) in France was employed in agriculture in 1800. Today this figure is only a few percent. UN World Urbanization Prospects (2018). Available at: [https://esa.un.org/unpd/wup/Download/](https://esa.un.org/unpd/wup/Download/). Note that this data can be viewed for any country or region using the ""change country"" function in the top-left of the chart. United Nations, Department of Economic and Social Affairs, Population Division (2018). World Urbanization Prospects: The 2018 Revision, Online Edition. Available at: [https://esa.un.org/unpd/wup/](https://esa.un.org/unpd/wup/). Pesaresi, M., Melchiorri, M., Siragusa, A., & Kemper, T. (2016). Atlas of the human planet–Mapping human presence on earth with the global human settlement layer. _JRC103150. Publications Office of the European Union. Luxembourg (Luxembourg): European Commission, DG JRC_. Available at: [https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/atlas-human-planet-mapping-human-presence-earth-global-human-settlement-layer](https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/atlas-human-planet-mapping-human-presence-earth-global-human-settlement-layer). Angel et al. (2018). Our Not-So-Urban World. The Marron Institute of Urban Management, New York University. Available at: [https://marroninstitute.nyu.edu/uploads/content/Angel_et_al_Our_Not-So-Urban_World,_revised_on_22_Aug_2018_v2.pdf](https://marroninstitute.nyu.edu/uploads/content/Angel_et_al_Our_Not-So-Urban_World,_revised_on_22_Aug_2018_v2.pdf)",How urban is the world? 1wCZsgwS9Tlh8ySeanWnDgoX-fVftlVgzTBW8N8FSQE8,world-population-update-2022,article,"{""toc"": [{""slug"": ""explore-this-data-for-every-country-in-the-world-in-our-new-population-and-demography-data-explorer"", ""text"": ""Explore this data for every country in the world in our new Population and Demography Data Explorer"", ""title"": ""Explore this data for every country in the world in our new Population and Demography Data Explorer"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""How many people are there in the world? How many die each year, and how many babies are born?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These are key questions that we need to understand the world around us. The global population dataset is one of our most important at "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Our World in Data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "": it underpins nearly every topic we cover."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The UN updates its "", ""spanType"": ""span-simple-text""}, {""url"": ""https://population.un.org/wpp/"", ""children"": [{""text"": ""World Population Prospects"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" every two years. Its latest release was due in 2021 but was delayed due to the COVID-19 pandemic. But, on World Population Day, the long-awaited dataset was released today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this article, we highlight some key findings of the twenty-seventh publication of the ‘World Population Prospects.’"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""With early access to this new UN data, we have also published a new "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""http://ourworldindata.org/explorers/population-and-demography"", ""children"": [{""text"": ""Population and Demography Data Explorer"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""text"": "". You can explore this full dataset in detail for any country worldwide."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""The world population will pass 8 billion at the end of 2022"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since 1975 the world has been adding another billion people "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/world-population-growth#how-long-did-it-take-for-the-world-population-to-increase-by-one-billion"", ""children"": [{""text"": ""every 12 years"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It passed its last milestone of 7 billion in 2011. And, by the end of 2022, another one will pass: "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""there will be 8 billion people worldwide."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While this absolute growth is similar to previous decades, the growth "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""rate"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" continues to fall. Since 2019, the global population growth rate has fallen below 1%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""That’s less than half its peak growth rate – of 2.3% – in the 1960s."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As global fertility rates continue to fall (see below), this rate will continue to fall."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography?time=earliest..2099&facet=none&Metric=Population+growth+rate&Sex=Both+sexes&Age+group=Total&Projection+Scenario=None&country=~OWID_WRL&hideControls=true"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The UN estimates around 15 million excess deaths in 2020 and 2021 from the COVID-19 pandemic"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Coronavirus (COVID-19) pandemic has significantly impacted global population and migration trends."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We know that the confirmed death toll from COVID-19 will likely significantly underestimate the true number of deaths because of limited testing. One way to better estimate the pandemic's total mortality impact is to look at "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""excess "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""mortality data. We can look at the total number of deaths and compare this to the number we expect to occur in a non-pandemic year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""In its latest population dataset, the UN estimates that in 2020, there were approximately 5 million excess deaths. In 2021, this figure was 10 million."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This estimate of 15 million excess deaths over 2020 and 2021 aligns with estimates from other organizations. The Economist put its central estimate of excess deaths at 17.6 million. The World Health Organization, a UN organization, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/excess-deaths-cumulative-who?country=~OWID_WRL"", ""children"": [{""text"": ""estimated"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" 14.9 million excess deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These death figures are highly uncertain. But what’s clear is that the number of confirmed deaths – just 5.4 million by the end of 2021 – captures just a fraction of the true impact of the pandemic."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography?time=earliest..latest&facet=none&Metric=Deaths&Sex=Both+sexes&Age+group=Total&Projection+Scenario=None&country=~OWID_WRL&hideControls=true"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The global population is projected to peak at around 10.4 billion in 2086"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world population has increased rapidly over the last century.  When will it come to an end?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Previous versions of the UN World Population Prospects showed a significant slowdown in population growth, with "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""very slow"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" growth – almost reaching a plateau – by the end of the century. Its previous release projected that the world population would be around 10.88 billion in 2100 and would not yet have peaked."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""In this new release, the UN projects that the global population will peak before the end of the century – in 2086, at just over 10.4 billion people."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are several reasons for this earlier and lower peak. One is that the UN expects fertility rates to fall more quickly in low-income countries compared to previous revisions. It also expects less of a ‘rebound’ in fertility rates across high-income countries in the second half of the century."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography?time=earliest..latest&facet=none&Metric=Population&Sex=Both+sexes&Age+group=Total&Projection+Scenario=Medium&country=~OWID_WRL&hideControls=true"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The global fertility rate has continued to decline to 2.3 births per woman"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A key determinant of the global population rate is women's average number of children over their lifetime – the "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/fertility-rate"", ""children"": [{""text"": ""fertility rate"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Fertility rates have fallen rapidly across the world in recent decades. In 1950, the average woman gave birth around 5 times. Since then, fertility rates have more than halved. In 2021, this global figure was 2.3 births per woman."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you switch to the map tab in the interactive chart, you see that most people now live in countries where fertility rates are at – or below – the ‘replacement level.’ This is the level at which populations would stabilize or shrink over the long term. The UN reports that two-thirds of people live in countries with a fertility rate below 2.1 births per woman. In some high-income countries such as South Korea, Japan, Spain, or Italy, it is as low as 1.3 births per woman."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography?facet=none&Metric=Fertility+rate&Sex=Both+sexes&Age+group=Total&Projection+Scenario=Medium&country=High-income+countries~OWID_WRL&hideControls=true"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Next year India is expected to take over from China as the world’s most populous country"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""China has been the world’s most populous country for decades. It is now home to more than 1.4 billion people. However, its population growth rate has fallen significantly following a rapid drop in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography?time=earliest..2099&facet=none&Metric=Fertility+rate&Sex=Both+sexes&Age+group=Total&Projection+Scenario=Medium&country=CHN~IND"", ""children"": [{""text"": ""its fertility rate"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" over the 1970s and 80s."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The fertility rate in India has also fallen substantially in recent decades – from 5.7 births per woman in 1950 to just 2 births per woman today. However, the rate of this decline has been slower."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Because of this, India will very soon overtake China as the most populous country in the world. The UN expects this to happen in 2023."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography?time=earliest..latest&facet=none&Metric=Population&Sex=Both+sexes&Age+group=Total&Projection+Scenario=Medium&country=CHN~IND&hideControls=true"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""Explore this data for every country in the world in our new Population and Demography Data Explorer"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography"", ""type"": ""prominent-link"", ""title"": ""Population & Demography Data Explorer"", ""description"": """", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""20c63e5eb9919c11f490c74c9efe6a28885d22fd"": {""id"": ""20c63e5eb9919c11f490c74c9efe6a28885d22fd"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This is based on its medium-fertility projection scenario. Its ‘low’ projection scenario peaks much earlier – in 2054 – at 8.9 billion people. Its ‘high’ projection scenario does not peak by the end of the century."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Five key findings from the 2022 UN Population Prospects"", ""authors"": [""Hannah Ritchie"", ""Edouard Mathieu"", ""Lucas Rodés-Guirao"", ""Marcel Gerber""], ""excerpt"": ""Explore the key highlights from the UN’s latest release of its world population estimates."", ""subtitle"": ""Explore the key highlights from the UN’s latest release of its world population estimates."", ""sidebar-toc"": false, ""featured-image"": ""Population-2022.png""}",1,2024-03-06 13:41:31,2022-07-11 16:10:00,2024-03-06 14:01:00,listed,ALBJ4LsSnZcwsAn01Tno75ZXT4zRk-XowjDziW4isS6HknrNivptdbL3HaCLeCq_yhTKPNfITI8HVcl7Ws_oTQ,,"How many people are there in the world? How many die each year, and how many babies are born? These are key questions that we need to understand the world around us. The global population dataset is one of our most important at _Our World in Data_: it underpins nearly every topic we cover. The UN updates its [World Population Prospects](https://population.un.org/wpp/) every two years. Its latest release was due in 2021 but was delayed due to the COVID-19 pandemic. But, on World Population Day, the long-awaited dataset was released today. In this article, we highlight some key findings of the twenty-seventh publication of the ‘World Population Prospects.’ With early access to this new UN data, we have also published a new **[Population and Demography Data Explorer](http://ourworldindata.org/explorers/population-and-demography)**. You can explore this full dataset in detail for any country worldwide. # The world population will pass 8 billion at the end of 2022 Since 1975 the world has been adding another billion people [every 12 years](https://ourworldindata.org/world-population-growth#how-long-did-it-take-for-the-world-population-to-increase-by-one-billion). It passed its last milestone of 7 billion in 2011. And, by the end of 2022, another one will pass: **there will be 8 billion people worldwide.** While this absolute growth is similar to previous decades, the growth _rate_ continues to fall. Since 2019, the global population growth rate has fallen below 1%. That’s less than half its peak growth rate – of 2.3% – in the 1960s. As global fertility rates continue to fall (see below), this rate will continue to fall. # The UN estimates around 15 million excess deaths in 2020 and 2021 from the COVID-19 pandemic The Coronavirus (COVID-19) pandemic has significantly impacted global population and migration trends. We know that the confirmed death toll from COVID-19 will likely significantly underestimate the true number of deaths because of limited testing. One way to better estimate the pandemic's total mortality impact is to look at _excess _mortality data. We can look at the total number of deaths and compare this to the number we expect to occur in a non-pandemic year. **In its latest population dataset, the UN estimates that in 2020, there were approximately 5 million excess deaths. In 2021, this figure was 10 million.** This estimate of 15 million excess deaths over 2020 and 2021 aligns with estimates from other organizations. The Economist put its central estimate of excess deaths at 17.6 million. The World Health Organization, a UN organization, [estimated](https://ourworldindata.org/grapher/excess-deaths-cumulative-who?country=~OWID_WRL) 14.9 million excess deaths. These death figures are highly uncertain. But what’s clear is that the number of confirmed deaths – just 5.4 million by the end of 2021 – captures just a fraction of the true impact of the pandemic. # The global population is projected to peak at around 10.4 billion in 2086 The world population has increased rapidly over the last century.  When will it come to an end? Previous versions of the UN World Population Prospects showed a significant slowdown in population growth, with _very slow_ growth – almost reaching a plateau – by the end of the century. Its previous release projected that the world population would be around 10.88 billion in 2100 and would not yet have peaked. **In this new release, the UN projects that the global population will peak before the end of the century – in 2086, at just over 10.4 billion people.**1 There are several reasons for this earlier and lower peak. One is that the UN expects fertility rates to fall more quickly in low-income countries compared to previous revisions. It also expects less of a ‘rebound’ in fertility rates across high-income countries in the second half of the century. # The global fertility rate has continued to decline to 2.3 births per woman A key determinant of the global population rate is women's average number of children over their lifetime – the [fertility rate](http://ourworldindata.org/fertility-rate). Fertility rates have fallen rapidly across the world in recent decades. In 1950, the average woman gave birth around 5 times. Since then, fertility rates have more than halved. In 2021, this global figure was 2.3 births per woman. If you switch to the map tab in the interactive chart, you see that most people now live in countries where fertility rates are at – or below – the ‘replacement level.’ This is the level at which populations would stabilize or shrink over the long term. The UN reports that two-thirds of people live in countries with a fertility rate below 2.1 births per woman. In some high-income countries such as South Korea, Japan, Spain, or Italy, it is as low as 1.3 births per woman. # Next year India is expected to take over from China as the world’s most populous country China has been the world’s most populous country for decades. It is now home to more than 1.4 billion people. However, its population growth rate has fallen significantly following a rapid drop in [its fertility rate](https://ourworldindata.org/explorers/population-and-demography?time=earliest..2099&facet=none&Metric=Fertility+rate&Sex=Both+sexes&Age+group=Total&Projection+Scenario=Medium&country=CHN~IND) over the 1970s and 80s. The fertility rate in India has also fallen substantially in recent decades – from 5.7 births per woman in 1950 to just 2 births per woman today. However, the rate of this decline has been slower. Because of this, India will very soon overtake China as the most populous country in the world. The UN expects this to happen in 2023. --- ## Explore this data for every country in the world in our new Population and Demography Data Explorer ### Population & Demography Data Explorer https://ourworldindata.org/explorers/population-and-demography This is based on its medium-fertility projection scenario. Its ‘low’ projection scenario peaks much earlier – in 2054 – at 8.9 billion people. Its ‘high’ projection scenario does not peak by the end of the century.",Five key findings from the 2022 UN Population Prospects 1wB1j4EqkugQ6f2k_xFD8NVpKErXoUpQSISSF3EMK4ig,iea-open-data,article,"{""toc"": [{""slug"": ""what-can-you-do-to-help"", ""text"": ""What can you do to help?"", ""title"": ""What can you do to help?"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""text"": [{""type"": ""text"", ""value"": [{""text"": ""To make the transition to low-carbon energy sources and address climate change we need open data on the global energy system. High-quality data already exists; it is published by the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""International Energy Agency"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". But despite being an international institution that is largely publicly funded, most IEA data is locked behind paywalls. This makes it unusable in the public discourse and prevents many researchers from accessing it. Beyond this, it hinders data-sharing and collaboration; results in duplicated research efforts; makes the data unusable for the public discourse; and goes against the principles of transparency and reproducibility in scientific research. The high costs of the data excludes many from the global dialogue on energy and climate and thereby stands in the way of the IEA achieving its own mission."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We suggest that the countries that fund the IEA drop the requirement to place data behind paywalls and increase their funding – the benefits of opening this important data are much larger than the costs."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Transitioning to a low-carbon energy system is one of humanity's most pressing challenges. Since 87% of annual carbon dioxide emissions "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/global-co2-emissions-fossil-land?country=~OWID_WRL"", ""children"": [{""text"": ""come from the energy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and industrial sectors, this transition is essential to address climate change."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" At the same time the provision of clean energy is also a priority for global health and human development: 10% do not have "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-the-population-with-access-to-electricity?tab=chart&country=~OWID_WRL"", ""children"": [{""text"": ""access to electricity"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""; 41% do not have "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/access-to-clean-fuels-and-technologies-for-cooking?tab=chart&country=~OWID_WRL"", ""children"": [{""text"": ""access to clean fuels for cooking"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and estimates of the health burden of anthropogenic "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/outdoor-air-pollution"", ""children"": [{""text"": ""outdoor air pollution"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" range from 4 to over 10 million premature deaths per year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To understand the problems the world faces and see how we can make progress we need accessible, high-quality data. It needs to be global in scope – leaving no country absent from the conversation – and it needs to cover the range of metrics needed to understand the energy system: this includes primary energy, final energy, useful energy, the breakdown of the electricity mix, end-sector breakdowns of energy consumption, and the CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" emissions that each sector produces."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This data exists. It is produced by the International Energy Agency (IEA). But the IEA only makes a fraction of their data publicly available, and keeps the rest behind very costly paywalls. This is despite the fact that the IEA is largely funded through public money from its member countries. The reason that the IEA puts much of its data behind paywalls is that the funders made it a requirement that it raises a small share of its budget through licensed data sales. As a consequence of this requirement the data is copyrighted under a strict data license; to access more than the very basic metrics, researchers and everyone else who wants to inform themselves about the global energy system needs to "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.iea.org/data-and-statistics/data-product/iea-statistics-package"", ""children"": [{""text"": ""purchase a user license"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that often "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.iea.org/data-and-statistics/data-products"", ""children"": [{""text"": ""costs thousands of dollars"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 2018, the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://vipo.iea.org/about/structure/"", ""children"": [{""text"": ""annual budget"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of the IEA was EUR 27.8 million. According to the IEA’s budget figures, revenues from its data and publication sales finance “more than one-fifth of its annual budget”. That equates to EUR 5.6 million per year. To put this figure in perspective, it is equal to 0.03% of the total public energy RD&D budget for IEA countries in 2018, which was EUR 20.7 billion."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Or on a per capita basis split equally across IEA member countries: 0.44 cents per person per year."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We believe that the relatively small revenues that the paywalls generate do not justify the very large downsides that these restrictions cause."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Despite it being one of the most pressing challenges we face, energy is the only area of development without a global open-access dataset that researchers, policymakers and innovators can use to understand and tackle the problem. The paywalls the IEA is required to put in front of its data make it impossible for it to achieve "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.iea.org/about/mission"", ""children"": [{""text"": ""its own mission"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The IEA wants to be at the “heart of global dialogue on energy, providing authoritative analysis, data, policy recommendations, and real-world solutions to help countries provide secure and sustainable energy for all”, but as it stands the IEA is only providing data to rich elites as the restrictive licenses ensure that it cannot be part of the global dialogue on energy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As explained, the problem is not so much the IEA itself, who surely has an interest in achieving its mission. The problem is the member countries’ imposition that the IEA has to raise a part of its budget through the sales of data licenses. To make it possible for the IEA to achieve its mission, the global energy and climate research community should therefore recommend to IEA member countries that they remove the requirement to charge for data use and close the relatively small funding gap that remains."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The pandemic has taught us many lessons over the past year. One key lesson has been that timely, accurate, and "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""open"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" global data is fundamental to the understanding of a global problem and an appropriate response to it. In the same way that the lack of public data would have stood in the way of fighting the pandemic, the lack of public data on the energy and climate system is standing in the way of solving one of the biggest challenges of our lifetime."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""The IEA provides crucial energy data that is not available elsewhere"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The statistical work of the IEA is of immense value. It is the only source of energy data that captures the full range of metrics needed to understand the global energy transition: from primary energy through to final energy use by sub-sector. It is the go-to source for most researchers and forms the basis of the energy systems modelling in the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It is also heavily utilised in energy policy, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.iea.org/areas-of-work/international-collaborations/unfccc"", ""children"": [{""text"": ""collaborating with"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" the United Nations Framework Convention on Climate Change (UNFCCC) on developments in energy data and analytics."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Some alternative data sources on energy exist, but none come close to the coverage and depth of the IEA data. The "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html"", ""children"": [{""text"": ""BP Statistical Review of World Energy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", published by the multinational oil and gas company BP is the most commonly used alternative. As a freely available dataset it is widely used in research and is where the IEA would want to be – ‘at the heart of the global dialogue on energy’. But as it is published by a private fossil fuel company it has some obvious drawbacks."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One is that it focuses on commercially-traded fuels; this means most high- and middle-income countries are included but lower-income countries are almost completely absent even from very basic metrics such as primary energy. It also focuses on primary energy statistics and does not offer insight into the breakdown in final energy or sector-specific allocations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The series of maps show the comparative geographical coverage of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""primary"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""final"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" energy between the publicly available dataset from BP, and the private licensed dataset from the IEA."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""children"": [{""text"": ""Geographical coverage of the IEA’s data in comparison with an alternative dataset that is publicly available."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" Due to data restrictions on the IEA global energy dataset, the open-access BP dataset has become a default source. This energy data is only published for countries that primarily rely on commercially traded fuels, meaning most low-income countries are not included. It also does not include any information on final energy use, one of the most important metrics to understand the energy system."", ""spanType"": ""span-simple-text""}], ""filename"": ""Energy-data-coverage.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The breakdown of primary energy data to their final and end-use components is essential for understanding energy demand and future energy scenarios. Final energy demand is only one of many factors that determine primary energy consumption. Other important factors include the primary-to-secondary energy efficiency; final-to-useful energy efficiency; the sectoral structure of the economy; and the energy mix."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The lack of publicly available data on final energy use is particularly problematic when it comes to understanding the transition to low-carbon energy sources and the evaluation of future energy demands."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The gap between primary energy supply and final energy use is high for low-carbon energy sources and as the world adopts more of these energy sources the gap will increase. As an example: in the IEA and IRENA’s global energy transition scenarios for 2°C, final energy consumption in 2050 is typically 30% to 33% lower than primary energy supply."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Final energy use is perhaps the most important energy statistic and should be at the center of the public discourse, but because no other institutions publish an international dataset on this metric, it is not. There are several other metrics of key importance for which only the IEA publishes internationally-comparable figures, including final energy use by energy source; allocation of energy to end-use sector; sector-specific energy use and CO₂ emissions (for example, no long-term time-series of aviation emissions exists in the public domain); CO₂ emissions from electricity; carbon intensity of electricity production; and power generation capacity by source to evaluate energy infrastructure timelines and potential stranded assets. There are also other important datasets – including Bloomberg New Energy Finance (BNEF) or the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ember-climate.org/"", ""children"": [{""text"": ""EMBER electricity"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" dataset  – but none of these sources include the same detail as the data published by the IEA."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""Lack of open energy data hinders progress in science, technology and policy"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The lack of open data hinders progress on the energy transition in several ways."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""1. Duplicated efforts"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": since IEA data cannot be shared, researchers cannot share their analyses of IEA data, and their colleagues cannot build on their work. As a consequence, thousands of researchers and analysts across the world derive the same statistics independently from each other and many hours of work are spent on the same analyses."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""2. Inequalities in data access and perspective"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": many perspectives are excluded from research and academic debate – many researchers, especially in poorer countries, cannot afford to buy the IEA data. Publicly available datasets on energy – such as BP – do not include low-income countries which means they are excluded from the conversation. As many of these countries make decisions about the future of their energy systems right now, it is vital that this data is available as soon as possible."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""3. Credibility and replication challenges"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": since IEA data – and analyses built on top of this data – cannot be shared, verification is difficult and often impossible. Transparency and reproducibility are core principles in scientific research and neither the research that is based on IEA data nor the IEA itself (as every other group of researchers they also receive criticism for some aspects of their work) can adhere to this principle."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""4. Outreach and engagement is difficult"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": the public needs to understand the problem of energy and climate change. The cost of accessing important data and the restrictions to use it in public however makes it difficult for journalists to do their work on these key global challenges."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It goes against the FAIR Guiding Principles – a set of principles agreed by stakeholders representing academia, industry, funding agencies, and scholarly publishers – for appropriate data management and stewardship in science."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The licensing model of the IEA is in conflict with at least three of these basic principles: Findable, Accessible, Interoperable, Reusable."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The current system has many downsides. Data restrictions make work more difficult for researchers – they duplicate efforts and cannot share their results freely. It means that policymakers often rely on research and commentary that is not based on the best data. It does not serve the IEA: it hinders its mission of leading the global dialogue on energy and means the immense value of its data and research team is under-utilized. Finally, it means the general public, journalists, innovators, or others interested in global energy and climate cannot properly engage in the conversation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Because much of the research on energy and climate change is publicly funded, it is often also public money that pays for research to access IEA data; an absurd situation. The case for open energy data is not just a scientific and ethical one. There's an obvious economic case for it. The status quo where research efforts are duplicated, time is wasted, and in which researchers and decision-makers rely on sub-optimal data creates large systemic inefficiencies. The economic cost of these inefficiencies likely dwarfs the small funding gap imposed on the IEA by the governments of its member countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""Open energy data is needed to understand energy dependency and security – but the IEA keeps it behind paywalls"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In addition to climate change there is another reason that high-quality, open data is so important: energy security and dependency. It’s a reason we often overlook but the war in Ukraine has now brought into sharp focus."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To understand energy dependency and security we need to know which countries buy fuels from Russia and how much; which other countries have fuel reserves and could supply them instead; whether alternative sources of energy could be used."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Researchers across the world have been doing their best to work through the data and provide a clear picture. But it has been difficult. No single source in the public domain provides this data at a global level. Instead, researchers have had to scramble around, trying to find any nugget of data they can."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The frustrating thing is that the information "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""is "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""available. It is published by the IEA. But nearly all of it requires a paid subscription and prohibits the sharing of this data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One dataset – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.iea.org/data-and-statistics/data-product/gas-trade-flows#"", ""children"": [{""text"": ""Gas Trade Flows"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – is free to access (with an account) and provides monthly country-to-country data on gas trade between European countries. This is useful but inadequate. Its full datasets on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.iea.org/data-and-statistics/data-product/monthly-gas-data-service-2"", ""children"": [{""text"": ""gas trade"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.iea.org/data-and-statistics/data-product/natural-gas-information"", ""children"": [{""text"": ""balances"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" are paywalled. Its datasets "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.iea.org/data-and-statistics/data-product/oil-information"", ""children"": [{""text"": ""on oil"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.iea.org/data-and-statistics/data-product/coal-information-2"", ""children"": [{""text"": ""on coal"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" also require a paid subscription."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Without access to this data – or the ability to share it freely and transparently – researchers have to find alternatives. These can fill some gaps, but not all of them. Policymakers are then making important decisions based on incomplete information. It doesn’t have to be this way."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The costs of this alone far, far outstrip the five to six million Euros that would be required to make the IEA data public."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""Energy is one of the few development areas where data is private"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The challenge of making international data publicly available is not a new one. The late statistician Hans Rosling "", ""spanType"": ""span-simple-text""}, {""url"": ""https://blogs.worldbank.org/opendata/hugs-and-databases-memory-hans-rosling"", ""children"": [{""text"": ""previously diagnosed"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" some of the large international organizations with chronic cases of DBHD or “Database Hugging Disorder”."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But while other institutions were cured of DBHD and have much improved the access to the data they produced, the energy and climate sector remains one of the few – if not the only – research area that lags far behind. To understand global food systems and nutrition the world can rely on the data from the Food and Agriculture Organization (UN FAO); in global health we have the World Health Organization (WHO); in poverty and inequality we can rely on the data from the World Bank; in water and sanitation we have the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). All of these institutions are largely publicly funded and all of them make their databases available as a public good, free and open-access for everyone. In this regard, energy is now the outlier among the world’s large global problems."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Many studies have illustrated the large social and economic benefits of public data. The World Bank – which previously had a similar data licensing model as the IEA – has now published a number of flagship reports highlighting the economic benefits of open data, and its critical role in sustainable development."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" One of its studies estimated that in the EU alone, open government data provided an economic value of 40 billion euros per year."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The most widely-cited estimate of the economic value of open data globally – across both public and private sectors – comes from the McKinsey Global Institute: it estimated that open data across seven sectors could create 3 to 5 trillion dollars of economic value per year."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Energy and transport were some of the most valuable sectors, with 340 to 580 billion dollars in electricity; 240 to 510 billion dollars in oil and gas; and 720 to 920 in transport. The funding that would make the IEA data publicly available is a very small fraction of these sums."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At the national level many countries have highlighted the value of open energy data too. For example, the UK government and energy regulator (Ofgem) commissioned an Energy Data Taskforce to investigate the role of data in decarbonizing the national grid."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It found that open data-sharing was crucial to the energy transition: it facilitated improved understanding of balancing supply and demand; increased the efficiency of grid operations; reduced energy costs; and lowered the barrier of entry to innovators. The benefits we see at the national level should be transferable to understanding the global energy transition as a whole."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Faced with the urgent and global challenges of global energy access and climate change, accessing the basic data should not be this difficult.  Making this data free and accessible for everyone is a very basic – but critical – first step. If we cannot even manage this, what are our chances of tackling the much bigger international problems facing us?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What can you do to help?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To fix this problem, the energy ministries that provide finance to the IEA need to change the restrictions on their funding contributions and close the small 5 to 6 million EUR gap."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You find the list of the 30 IEA member countries "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://www.iea.org/about/membership"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""text"": "". If you want to help move this discussion forward, you can contact your respective energy ministry and ask them to change it. Skander Garroum and Christoph Proeschel "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://free-iea-data.com/"", ""children"": [{""text"": ""have built a tool"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""text"": "" to make this process easier: just select your country and it will find your representative and draft a petition email for you."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""This article was first published on October 7, 2021. It was updated on March 18 2022, with the addition of the section "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""\""Open energy data is needed to understand energy dependency and security – but the IEA keeps it behind paywalls\"" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Read our article, published in Nature, on the need for open, global data to make progress on climate, energy and other pressing problems:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""url"": ""https://www.nature.com/articles/d41586-021-02691-4"", ""type"": ""prominent-link"", ""title"": ""COVID’s lessons for climate, sustainability and more from Our World in Data"", ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""→ "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://forum.openmod.org/t/open-letter-to-iea-and-member-countries-requesting-open-data/2949"", ""children"": [{""text"": ""Open letter to the International Energy Agency and its member countries: please remove paywalls from global energy data and add appropriate open licenses"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""text"": "" – Robbie Morrison, Malte Schaefer and the OpenMod community"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""→ "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://www.theguardian.com/environment/2021/dec/10/academics-urge-iea-to-give-free-access-to-national-energy-data"", ""children"": [{""text"": ""Energy watchdog urged to give free access to government data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""text"": "" – Jillian Ambrose, in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""The Guardian"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""→ "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://web.archive.org/web/20211219063050/https://www.devex.com/news/opinion-opening-up-energy-data-is-critical-to-battling-climate-change-102313"", ""children"": [{""text"": ""Opening up energy data is critical to battling climate change"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""text"": "" – Christa Hasenkopf, in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Devex"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""→ "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://free-iea-data.com/"", ""children"": [{""text"": ""Open petition letter: Free IEA Data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""text"": "" – Skander Garroum and Christoph Proeschel"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""→ "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.nature.com/articles/542393a"", ""children"": [{""text"": ""Energy scientists must show their workings"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – Stefan Pfenninger, in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Nature "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""from 2017"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""→ Bazilian, M., & Rice, A., Rotich, J., Howells, M., DeCarolis, J., Macmillan, S., Brooks, C., Bauer, F., & Liebreich, M. (2012). \"""", ""spanType"": ""span-simple-text""}, {""url"": ""https://ideas.repec.org/a/eee/enepol/v49y2012icp149-153.html"", ""children"": [{""text"": ""Open source software and crowdsourcing for energy analysis"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""\"", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Energy Policy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Other articles covering this issue"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""0de0ef7eb63a7b4e2a49efc249d555def19d5ad8"": {""id"": ""0de0ef7eb63a7b4e2a49efc249d555def19d5ad8"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Global Carbon Project. (2019). Supplemental data of Global Carbon Budget 2019 (Version 1.0) [Data set]. 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(2014)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""35c85dc69b29d2544244a04d5c19c5b114329419"": {""id"": ""35c85dc69b29d2544244a04d5c19c5b114329419"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Guevara, Z., Henriques, Sofiateives & Sousa, T. Driving factors of differences in primary energy intensities of 14 European countries. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Energy Policy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""149,"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" (2021)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3fc9a30e10f68c86b8a3e8f10eee4ecba8bf1152"": {""id"": ""3fc9a30e10f68c86b8a3e8f10eee4ecba8bf1152"", ""index"": 13, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Sandys, L. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""et al.A strategy for Modern Digitalised Energy System"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". (2019)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""5a7cd5caebddb15157083d0f30d5705ba8643017"": {""id"": ""5a7cd5caebddb15157083d0f30d5705ba8643017"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Wilkinson, M. D. The FAIR Guiding Principles for scientific data management and stewardship. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Sci. Data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" 1–9 (2016). doi:10.1038/sdata.2016.18."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""98b74afb80f2673f25782383209a4816d495124e"": {""id"": ""98b74afb80f2673f25782383209a4816d495124e"", ""index"": 9, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The Carbon Brief provide one of world’s best summaries of the Russia energy situation. In its article "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://www.carbonbrief.org/qa-what-does-russias-invasion-of-ukraine-mean-for-energy-and-climate-change"", ""children"": [{""text"": ""Q&A: What does Russia’s invasion of Ukraine mean for energy and climate change?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""the team provides a highly-detailed overview of this problem. But, look closely and you’ll see that to do this they had to cobble together a series of Tweets from researchers across the world. Each provides one piece of the jigsaw, but to build a complete understanding they need to all be slotted into place. 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(2013)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b3162dd1a1f633fee143a19818f4a795d5d0a1fa"": {""id"": ""b3162dd1a1f633fee143a19818f4a795d5d0a1fa"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Voigt, S., Cian, E. De, Schymura, M. & Verdolini, E. Energy intensity developments in 40 major economies : Structural change or technology improvement ? "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Energy Econ."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""children"": [{""text"": ""41,"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" 47–62 (2014)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c3ac60fcfc1f37bda440626a59ceef1747e56fe0"": {""id"": ""c3ac60fcfc1f37bda440626a59ceef1747e56fe0"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For example, if one country could ramp up the production of alternatives so that they have spare gas capacity. This requires detailed data on reserves, trade, demand, and capacity across all sources of energy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f0741b47bd40317b0528eb0848cec71dc24267e6"": {""id"": ""f0741b47bd40317b0528eb0848cec71dc24267e6"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Gielen, D. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""et al."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" The role of renewable energy in the global energy transformation. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Energy Strateg. Rev."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""children"": [{""text"": ""24,"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" 38–50 (2019)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""The International Energy Agency publishes the detailed, global energy data we all need, but its funders force it behind paywalls. Let's ask them to change it."", ""authors"": [""Max Roser"", ""Hannah Ritchie""], ""excerpt"": ""For energy security and progress on climate change we need open data on energy. The funders of the IEA can make this happen."", ""dateline"": ""March 18, 2022"", ""subtitle"": ""For energy security and progress on climate change we need open data on energy. The funders of the IEA can make this happen."", ""sidebar-toc"": false, ""featured-image"": """"}",1,2024-02-01 14:25:48,2022-03-18 11:00:00,2024-03-18 15:41:59,listed,ALBJ4LsdE4EBtb456w8ouh2fr9UDvuCe9jQYKP1tyLbnAbJKr5NkIXQg3UEFkRDDhFWNA-WiDGw1HoKEijP2Hg,," Transitioning to a low-carbon energy system is one of humanity's most pressing challenges. Since 87% of annual carbon dioxide emissions [come from the energy](https://ourworldindata.org/grapher/global-co2-emissions-fossil-land?country=~OWID_WRL) and industrial sectors, this transition is essential to address climate change.1 At the same time the provision of clean energy is also a priority for global health and human development: 10% do not have [access to electricity](https://ourworldindata.org/grapher/share-of-the-population-with-access-to-electricity?tab=chart&country=~OWID_WRL); 41% do not have [access to clean fuels for cooking](https://ourworldindata.org/grapher/access-to-clean-fuels-and-technologies-for-cooking?tab=chart&country=~OWID_WRL), and estimates of the health burden of anthropogenic [outdoor air pollution](http://ourworldindata.org/outdoor-air-pollution) range from 4 to over 10 million premature deaths per year. To understand the problems the world faces and see how we can make progress we need accessible, high-quality data. It needs to be global in scope – leaving no country absent from the conversation – and it needs to cover the range of metrics needed to understand the energy system: this includes primary energy, final energy, useful energy, the breakdown of the electricity mix, end-sector breakdowns of energy consumption, and the CO2 emissions that each sector produces. This data exists. It is produced by the International Energy Agency (IEA). But the IEA only makes a fraction of their data publicly available, and keeps the rest behind very costly paywalls. This is despite the fact that the IEA is largely funded through public money from its member countries. The reason that the IEA puts much of its data behind paywalls is that the funders made it a requirement that it raises a small share of its budget through licensed data sales. As a consequence of this requirement the data is copyrighted under a strict data license; to access more than the very basic metrics, researchers and everyone else who wants to inform themselves about the global energy system needs to [purchase a user license](https://www.iea.org/data-and-statistics/data-product/iea-statistics-package) that often [costs thousands of dollars](https://www.iea.org/data-and-statistics/data-products). In 2018, the [annual budget](https://vipo.iea.org/about/structure/) of the IEA was EUR 27.8 million. According to the IEA’s budget figures, revenues from its data and publication sales finance “more than one-fifth of its annual budget”. That equates to EUR 5.6 million per year. To put this figure in perspective, it is equal to 0.03% of the total public energy RD&D budget for IEA countries in 2018, which was EUR 20.7 billion.2 Or on a per capita basis split equally across IEA member countries: 0.44 cents per person per year.3 We believe that the relatively small revenues that the paywalls generate do not justify the very large downsides that these restrictions cause. Despite it being one of the most pressing challenges we face, energy is the only area of development without a global open-access dataset that researchers, policymakers and innovators can use to understand and tackle the problem. The paywalls the IEA is required to put in front of its data make it impossible for it to achieve [its own mission](https://www.iea.org/about/mission). The IEA wants to be at the “heart of global dialogue on energy, providing authoritative analysis, data, policy recommendations, and real-world solutions to help countries provide secure and sustainable energy for all”, but as it stands the IEA is only providing data to rich elites as the restrictive licenses ensure that it cannot be part of the global dialogue on energy. As explained, the problem is not so much the IEA itself, who surely has an interest in achieving its mission. The problem is the member countries’ imposition that the IEA has to raise a part of its budget through the sales of data licenses. To make it possible for the IEA to achieve its mission, the global energy and climate research community should therefore recommend to IEA member countries that they remove the requirement to charge for data use and close the relatively small funding gap that remains. The pandemic has taught us many lessons over the past year. One key lesson has been that timely, accurate, and _open_ global data is fundamental to the understanding of a global problem and an appropriate response to it. In the same way that the lack of public data would have stood in the way of fighting the pandemic, the lack of public data on the energy and climate system is standing in the way of solving one of the biggest challenges of our lifetime. --- # The IEA provides crucial energy data that is not available elsewhere The statistical work of the IEA is of immense value. It is the only source of energy data that captures the full range of metrics needed to understand the global energy transition: from primary energy through to final energy use by sub-sector. It is the go-to source for most researchers and forms the basis of the energy systems modelling in the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports.4 It is also heavily utilised in energy policy, [collaborating with](https://www.iea.org/areas-of-work/international-collaborations/unfccc) the United Nations Framework Convention on Climate Change (UNFCCC) on developments in energy data and analytics. Some alternative data sources on energy exist, but none come close to the coverage and depth of the IEA data. The [BP Statistical Review of World Energy](https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html), published by the multinational oil and gas company BP is the most commonly used alternative. As a freely available dataset it is widely used in research and is where the IEA would want to be – ‘at the heart of the global dialogue on energy’. But as it is published by a private fossil fuel company it has some obvious drawbacks. One is that it focuses on commercially-traded fuels; this means most high- and middle-income countries are included but lower-income countries are almost completely absent even from very basic metrics such as primary energy. It also focuses on primary energy statistics and does not offer insight into the breakdown in final energy or sector-specific allocations. The series of maps show the comparative geographical coverage of _primary_ and _final_ energy between the publicly available dataset from BP, and the private licensed dataset from the IEA. The breakdown of primary energy data to their final and end-use components is essential for understanding energy demand and future energy scenarios. Final energy demand is only one of many factors that determine primary energy consumption. Other important factors include the primary-to-secondary energy efficiency; final-to-useful energy efficiency; the sectoral structure of the economy; and the energy mix.5 The lack of publicly available data on final energy use is particularly problematic when it comes to understanding the transition to low-carbon energy sources and the evaluation of future energy demands.6 The gap between primary energy supply and final energy use is high for low-carbon energy sources and as the world adopts more of these energy sources the gap will increase. As an example: in the IEA and IRENA’s global energy transition scenarios for 2°C, final energy consumption in 2050 is typically 30% to 33% lower than primary energy supply.7 Final energy use is perhaps the most important energy statistic and should be at the center of the public discourse, but because no other institutions publish an international dataset on this metric, it is not. There are several other metrics of key importance for which only the IEA publishes internationally-comparable figures, including final energy use by energy source; allocation of energy to end-use sector; sector-specific energy use and CO₂ emissions (for example, no long-term time-series of aviation emissions exists in the public domain); CO₂ emissions from electricity; carbon intensity of electricity production; and power generation capacity by source to evaluate energy infrastructure timelines and potential stranded assets. There are also other important datasets – including Bloomberg New Energy Finance (BNEF) or the [EMBER electricity](https://ember-climate.org/) dataset  – but none of these sources include the same detail as the data published by the IEA. --- # Lack of open energy data hinders progress in science, technology and policy The lack of open data hinders progress on the energy transition in several ways. **1. Duplicated efforts**: since IEA data cannot be shared, researchers cannot share their analyses of IEA data, and their colleagues cannot build on their work. As a consequence, thousands of researchers and analysts across the world derive the same statistics independently from each other and many hours of work are spent on the same analyses. **2. Inequalities in data access and perspective**: many perspectives are excluded from research and academic debate – many researchers, especially in poorer countries, cannot afford to buy the IEA data. Publicly available datasets on energy – such as BP – do not include low-income countries which means they are excluded from the conversation. As many of these countries make decisions about the future of their energy systems right now, it is vital that this data is available as soon as possible. **3. Credibility and replication challenges**: since IEA data – and analyses built on top of this data – cannot be shared, verification is difficult and often impossible. Transparency and reproducibility are core principles in scientific research and neither the research that is based on IEA data nor the IEA itself (as every other group of researchers they also receive criticism for some aspects of their work) can adhere to this principle. **4. Outreach and engagement is difficult**: the public needs to understand the problem of energy and climate change. The cost of accessing important data and the restrictions to use it in public however makes it difficult for journalists to do their work on these key global challenges. It goes against the FAIR Guiding Principles – a set of principles agreed by stakeholders representing academia, industry, funding agencies, and scholarly publishers – for appropriate data management and stewardship in science.8 The licensing model of the IEA is in conflict with at least three of these basic principles: Findable, Accessible, Interoperable, Reusable. The current system has many downsides. Data restrictions make work more difficult for researchers – they duplicate efforts and cannot share their results freely. It means that policymakers often rely on research and commentary that is not based on the best data. It does not serve the IEA: it hinders its mission of leading the global dialogue on energy and means the immense value of its data and research team is under-utilized. Finally, it means the general public, journalists, innovators, or others interested in global energy and climate cannot properly engage in the conversation. Because much of the research on energy and climate change is publicly funded, it is often also public money that pays for research to access IEA data; an absurd situation. The case for open energy data is not just a scientific and ethical one. There's an obvious economic case for it. The status quo where research efforts are duplicated, time is wasted, and in which researchers and decision-makers rely on sub-optimal data creates large systemic inefficiencies. The economic cost of these inefficiencies likely dwarfs the small funding gap imposed on the IEA by the governments of its member countries. --- # Open energy data is needed to understand energy dependency and security – but the IEA keeps it behind paywalls In addition to climate change there is another reason that high-quality, open data is so important: energy security and dependency. It’s a reason we often overlook but the war in Ukraine has now brought into sharp focus. To understand energy dependency and security we need to know which countries buy fuels from Russia and how much; which other countries have fuel reserves and could supply them instead; whether alternative sources of energy could be used.9 Researchers across the world have been doing their best to work through the data and provide a clear picture. But it has been difficult. No single source in the public domain provides this data at a global level. Instead, researchers have had to scramble around, trying to find any nugget of data they can.10 The frustrating thing is that the information _is _available. It is published by the IEA. But nearly all of it requires a paid subscription and prohibits the sharing of this data. One dataset – [Gas Trade Flows](https://www.iea.org/data-and-statistics/data-product/gas-trade-flows#) – is free to access (with an account) and provides monthly country-to-country data on gas trade between European countries. This is useful but inadequate. Its full datasets on [gas trade](https://www.iea.org/data-and-statistics/data-product/monthly-gas-data-service-2) and [balances](https://www.iea.org/data-and-statistics/data-product/natural-gas-information) are paywalled. Its datasets [on oil](https://www.iea.org/data-and-statistics/data-product/oil-information) and [on coal](https://www.iea.org/data-and-statistics/data-product/coal-information-2) also require a paid subscription. Without access to this data – or the ability to share it freely and transparently – researchers have to find alternatives. These can fill some gaps, but not all of them. Policymakers are then making important decisions based on incomplete information. It doesn’t have to be this way. The costs of this alone far, far outstrip the five to six million Euros that would be required to make the IEA data public. --- # Energy is one of the few development areas where data is private The challenge of making international data publicly available is not a new one. The late statistician Hans Rosling [previously diagnosed](https://blogs.worldbank.org/opendata/hugs-and-databases-memory-hans-rosling) some of the large international organizations with chronic cases of DBHD or “Database Hugging Disorder”. But while other institutions were cured of DBHD and have much improved the access to the data they produced, the energy and climate sector remains one of the few – if not the only – research area that lags far behind. To understand global food systems and nutrition the world can rely on the data from the Food and Agriculture Organization (UN FAO); in global health we have the World Health Organization (WHO); in poverty and inequality we can rely on the data from the World Bank; in water and sanitation we have the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). All of these institutions are largely publicly funded and all of them make their databases available as a public good, free and open-access for everyone. In this regard, energy is now the outlier among the world’s large global problems. Many studies have illustrated the large social and economic benefits of public data. The World Bank – which previously had a similar data licensing model as the IEA – has now published a number of flagship reports highlighting the economic benefits of open data, and its critical role in sustainable development.11 One of its studies estimated that in the EU alone, open government data provided an economic value of 40 billion euros per year.12 The most widely-cited estimate of the economic value of open data globally – across both public and private sectors – comes from the McKinsey Global Institute: it estimated that open data across seven sectors could create 3 to 5 trillion dollars of economic value per year.13 Energy and transport were some of the most valuable sectors, with 340 to 580 billion dollars in electricity; 240 to 510 billion dollars in oil and gas; and 720 to 920 in transport. The funding that would make the IEA data publicly available is a very small fraction of these sums. At the national level many countries have highlighted the value of open energy data too. For example, the UK government and energy regulator (Ofgem) commissioned an Energy Data Taskforce to investigate the role of data in decarbonizing the national grid.14 It found that open data-sharing was crucial to the energy transition: it facilitated improved understanding of balancing supply and demand; increased the efficiency of grid operations; reduced energy costs; and lowered the barrier of entry to innovators. The benefits we see at the national level should be transferable to understanding the global energy transition as a whole. Faced with the urgent and global challenges of global energy access and climate change, accessing the basic data should not be this difficult.  Making this data free and accessible for everyone is a very basic – but critical – first step. If we cannot even manage this, what are our chances of tackling the much bigger international problems facing us? ## What can you do to help? To fix this problem, the energy ministries that provide finance to the IEA need to change the restrictions on their funding contributions and close the small 5 to 6 million EUR gap. You find the list of the 30 IEA member countries **[here](https://www.iea.org/about/membership)**. If you want to help move this discussion forward, you can contact your respective energy ministry and ask them to change it. Skander Garroum and Christoph Proeschel **[have built a tool](https://free-iea-data.com/)** to make this process easier: just select your country and it will find your representative and draft a petition email for you. --- --- _Read our article, published in Nature, on the need for open, global data to make progress on climate, energy and other pressing problems:_ ### COVID’s lessons for climate, sustainability and more from Our World in Data undefined https://www.nature.com/articles/d41586-021-02691-4 Global Carbon Project. (2019). Supplemental data of Global Carbon Budget 2019 (Version 1.0) [Data set]. Global Carbon Project. International Energy Agency. Energy Technology RD&D Budgets: Overview. Available at: https://www.iea.org/reports/energy-technology-rdd-budgets-overview/overview In 2018, the population of IEA member countries was 1.26 billion. United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Prospects: The 2019 Revision. Bruckner T., I.A. Bashmakov, Y. Mulugetta, H. Chum, A. de la Vega Navarro, J. Edmonds, A. Faaij, B. Fungtammasan, A. G. & E. Hertwich, D. Honnery, D. Infield, M. Kainuma, S. Khennas, S. Kim, H.B. Nimir, K. Riahi, N. Strachan, R. Wiser,  and X. Z. _Energy Systems. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change_. (2014). Guevara, Z., Henriques, Sofiateives & Sousa, T. Driving factors of differences in primary energy intensities of 14 European countries. _Energy Policy_, **149,** (2021). Voigt, S., Cian, E. De, Schymura, M. & Verdolini, E. Energy intensity developments in 40 major economies : Structural change or technology improvement ? _Energy Econ._**41,** 47–62 (2014). Gielen, D. _et al._ The role of renewable energy in the global energy transformation. _Energy Strateg. Rev._**24,** 38–50 (2019). Wilkinson, M. D. The FAIR Guiding Principles for scientific data management and stewardship. _Sci. Data_ 1–9 (2016). doi:10.1038/sdata.2016.18. For example, if one country could ramp up the production of alternatives so that they have spare gas capacity. This requires detailed data on reserves, trade, demand, and capacity across all sources of energy. The Carbon Brief provide one of world’s best summaries of the Russia energy situation. In its article _[Q&A: What does Russia’s invasion of Ukraine mean for energy and climate change?](https://www.carbonbrief.org/qa-what-does-russias-invasion-of-ukraine-mean-for-energy-and-climate-change)__ _the team provides a highly-detailed overview of this problem. But, look closely and you’ll see that to do this they had to cobble together a series of Tweets from researchers across the world. Each provides one piece of the jigsaw, but to build a complete understanding they need to all be slotted into place. Even then, there are probably still pieces missing. World Bank. _Open Data for Sustainable Development_. (2015). The World Bank. _World Development Report 2021: Data for Better Lives_. (2021). Stott, A. _Open data for economic growth_. (2014). Manyika, J. _et al.Open data: Unlocking innovation and performance with liquid information_. (2013). Sandys, L. _et al.A strategy for Modern Digitalised Energy System_. (2019).","The International Energy Agency publishes the detailed, global energy data we all need, but its funders force it behind paywalls. Let's ask them to change it." 1w9rFMftjTzNXOeVgseDsxxkX40o-hEFgC2cHAhuuT9E,hiv-aids,linear-topic-page,"{""toc"": [{""slug"": ""hiv-aids-is-one-of-the-world-s-most-fatal-infectious-disease"", ""text"": ""HIV/AIDS is one of the world's most fatal infectious disease"", ""title"": ""HIV/AIDS is one of the world's most fatal infectious disease"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""almost-1-million-people-die-from-hiv-aids-each-year-in-some-countries-it-s-the-leading-cause-of-death"", ""text"": ""Almost 1 million people die from HIV/AIDS each year; in some countries, it's the leading cause of death"", ""title"": ""Almost 1 million people die from HIV/AIDS each year; in some countries, it's the leading cause of death"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""the-global-distribution-of-deaths-from-hiv-aids"", ""text"": ""The global distribution of deaths from HIV/AIDS"", ""title"": ""The global distribution of deaths from HIV/AIDS"", 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""supertitle"": """", ""isSubheading"": true}, {""slug"": ""hiv-aids-has-had-a-significant-impact-on-life-expectancy-across-sub-saharan-africa"", ""text"": ""HIV/AIDS has had a significant impact on life expectancy across Sub-Saharan Africa"", ""title"": ""HIV/AIDS has had a significant impact on life expectancy across Sub-Saharan Africa"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""the-prevalence-of-hiv-aids"", ""text"": ""The prevalence of HIV/AIDS"", ""title"": ""The prevalence of HIV/AIDS"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""prevalence-in-the-total-population"", ""text"": ""Prevalence in the total population"", ""title"": ""Prevalence in the total population"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""prevalence-by-gender"", ""text"": ""Prevalence by gender"", ""title"": ""Prevalence by gender"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""prevalence-in-children"", ""text"": ""Prevalence in children"", ""title"": ""Prevalence in children"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""tuberculosis-among-people-living-with-hiv"", ""text"": ""Tuberculosis among people living with HIV"", ""title"": ""Tuberculosis among people living with HIV"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-can-be-done-to-prevent-hiv-aids"", ""text"": ""What can be done to prevent HIV/AIDS?"", ""title"": ""What can be done to prevent HIV/AIDS?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""antiretroviral-therapy"", ""text"": ""Antiretroviral therapy"", ""title"": ""Antiretroviral therapy"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""prevention-of-mother-to-child-transmission-pmtct"", ""text"": ""Prevention of mother-to-child transmission (PMTCT)"", ""title"": ""Prevention of mother-to-child transmission (PMTCT)"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""safe-sex"", ""text"": ""Safe sex"", ""title"": ""Safe sex"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""education-on-hiv-aids"", ""text"": ""Education on HIV/AIDS"", ""title"": ""Education on HIV/AIDS"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""funding-to-support-efforts-against-hiv-aids"", ""text"": ""Funding to support efforts against HIV/AIDS"", ""title"": ""Funding to support efforts against HIV/AIDS"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""comparisons-of-unaids-and-ihme-estimates"", ""text"": ""Comparisons of UNAIDS and IHME estimates"", ""title"": ""Comparisons of UNAIDS and IHME estimates"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""all-charts"", ""text"": ""Interactive charts on HIV / AIDS"", ""title"": ""Interactive charts on HIV / AIDS"", ""isSubheading"": false}, {""slug"": ""article-endnotes"", ""text"": ""Endnotes"", ""title"": ""Endnotes"", ""isSubheading"": false}, {""slug"": ""article-citation"", ""text"": ""Citation"", ""title"": ""Citation"", ""isSubheading"": false}, {""slug"": ""article-licence"", ""text"": ""Licence"", ""title"": ""Licence"", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Infection with HIV (human immunodeficiency virus) can lead to AIDS (acquired immunodeficiency syndrome). AIDS results in a gradual and persistent decline and failure of the immune system, resulting in a heightened risk of life-threatening infection and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/cancer"", ""children"": [{""text"": ""cancers"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the majority of cases, HIV is a sexually transmitted infection. However, HIV can also be transmitted from mother to child, during pregnancy or childbirth, or through breastfeeding. Non-sexual transmission can also occur by sharing injection equipment such as needles."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Other research and writing on HIV/AIDS on Our World in Data:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/art-lives-saved"", ""children"": [{""text"": ""Antiretroviral therapy has saved millions of lives from AIDS and could save more"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""url"": ""#all-charts"", ""children"": [{""text"": ""See all interactive charts on HIV/AIDS ↓ "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""text"": [{""text"": ""HIV/AIDS is one of the world's most fatal infectious disease"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Almost 1 million people die from HIV/AIDS each year; in some countries, it's the leading cause of death"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""HIV/AIDS is one of the world's most fatal infectious diseases – particularly across Sub-Saharan Africa, where the disease has had a massive impact on health outcomes and life expectancy in recent decades."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Global Burden of Disease"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" is a major global study on the causes of death and disease published in the medical journal "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""The Lancet"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" These estimates of the annual number of deaths by cause are shown here. This chart shows the global total but can be explored for any country or region using the \""Change country\"" button."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/annual-number-of-deaths-by-cause"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""According to the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Global Burden of Disease"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" study, nearly a million people die yearly from HIV/AIDS. To put this into context: this is just over 50% higher than the number of deaths from "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/malaria"", ""children"": [{""text"": ""malaria"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". It's "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""one of"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" the largest killers globally, but for some countries – particularly across Sub-Saharan Africa, it's the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""leading"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" cause of death."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""The global distribution of deaths from HIV/AIDS"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""In some countries, HIV/AIDS is the cause of a quarter of all deaths"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Globally, around 1.5% of deaths are caused by HIV/AIDS."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This share is high but masks the wide variations in the toll of HIV/AIDS worldwide. In some countries, this share was much higher."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On this interactive map, we see the share of deaths that resulted from HIV/AIDS across the world. Across most regions, the share was low: across Europe, for example, it accounted for less than 0.1% of deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, the share is very high across some countries – focused primarily in Southern Sub-Saharan Africa."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-deaths-aids"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Death rates are high across Sub-Saharan Africa"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The significant health burden of HIV/AIDS across Sub-Saharan Africa is also reflected in death rates. Death rates measure the number of deaths from HIV/AIDS per 100,000 individuals in a country or region."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the interactive map, we see the distribution of death rates worldwide. Most countries have a rate of less than 10 deaths per 100,000 – often much lower, below 5 per 100,000. Across Europe, the death rate is less than one per 100,000."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Across Sub-Saharan Africa, the rates are much higher. Some countries in the South of the region have rates greater than 100 per 100,000."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/hiv-death-rates"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Death rates are highest for younger adults and children under five years old"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Which population groups are most at risk from HIV/AIDS?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, we show death rates by age group. Here we see that the most at-risk group is younger adults (15 to 49-year-olds). Since HIV is primarily a sexually transmitted infection, where unsafe sex is a primary risk factor, this is what we would expect."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But we also see that death rates are higher for children under five; that’s because HIV can be transmitted from mother to child if the mother is infected."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/hiv-death-rates-by-age"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Is the world making progress in its fight against HIV/AIDS?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""How have cases and deaths changed over time?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The 1990s saw a substantial increase in people infected with HIV and dying of AIDS."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the second half of that decade, over 3 million people were infected with HIV yearly. Since then, the number of new infections began to decline, and it's now below 2 million, the lowest number of new infections since 1990."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As for mortality, AIDS-related deaths increased throughout the 1990s and peaked in the mid-2000s, with nearly 2 million annual deaths. Since then, the annual number of deaths from AIDS has declined and since halved. 2016 was the first year since the peak in which fewer than 1 million people died from AIDS."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart also shows the continuing increase in the number of people living with HIV. The growth rate has slowed compared to the 1990s, but the absolute number is at the highest ever."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/deaths-and-new-cases-of-hiv"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Global deaths from HIV/AIDS halved within a decade"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world has made significant progress against HIV/AIDS. Global deaths from AIDS have halved over the past decade."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the visualization, we see the global number of deaths from HIV/AIDS in recent decades – this is shown by age group. In the mid-2000s, global deaths peaked at almost 2 million per year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Driven mainly by the development and availability of antiretroviral therapy (ART), global deaths have more than halved since then."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can explore this change for any country or region using the \""Edit countries\"" button on the interactive chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/deaths-from-hiv-by-age"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""HIV/AIDS once accounted for a large share of deaths in some countries, but rates are now falling"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Global progress on HIV/AIDS has been driven by significant improvements in the countries most affected by the epidemic."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Today, the share of deaths remains high: more than 1 in 5 deaths in some countries are caused by HIV/AIDS. But in the past, this share was even higher."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the visualization, we see the change in the share of deaths from HIV/AIDS over time. From the 1990s through to the early 2000s, it was the cause of more than 1 in 3 deaths in several countries and even more than half of annual deaths in the late 1990s in Zimbabwe."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Over the past decade, this share has fallen as antiretroviral treatment has become more widely available."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-deaths-aids?tab=chart&country=ZAF+ZWE+NAM+OWID_WRL+Sub-Saharan%20Africa+MOZ+ZMB"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""HIV/AIDS has had a significant impact on life expectancy across Sub-Saharan Africa"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The health and mortality burden of HIV/AIDS across Sub-Saharan Africa has been considerable. We see this impact on health reflected in trends in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/life-expectancy"", ""children"": [{""text"": ""life expectancy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". In the visualization, we show changes in life expectancy across select countries in Sub-Saharan Africa for which HIV/AIDS has had the most significant toll."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see a dramatic drop in life expectancy starting around 1990, coinciding with the rise of HIV. In Botswana, life expectancy fell by a decade; in Eswatini, it fell by two decades. Since the early 2000s — as progress has been made in tackling HIV — we see that life expectancy has been rising again."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In some countries, life expectancy is higher than before the epidemic began."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/life-expectancy?country=BWA+NAM+ZAF+SWZ+TZA+ZMB"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The prevalence of HIV/AIDS"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Prevalence in the total population"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Share of the population with HIV"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can explore the total number of people living with HIV/AIDS worldwide "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/number-of-people-living-with-hiv"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-population-infected-with-hiv-ihme"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Number of new infections each year"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/new-cases-of-hiv-infection"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Prevalence by gender"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Is HIV/AIDS more common in men or women?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are differences in the prevalence of HIV and death rates from AIDS between men and women. The chart shows the share of women in populations living with HIV."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we see, HIV prevalence tends to be higher in women across Sub-Saharan Africa, although higher in males across most other regions. The trend in AIDS-related deaths shows the opposite: more men tend to die from AIDS every year than women. The reasons for differences in prevalence and death rates are complex; however, across Sub-Saharan Africa, women tend to be infected with HIV earlier than men and survive longer (explaining both the higher prevalence and lower annual AIDS deaths in women). Several gender inequality and social norm issues result in a higher prevalence of HIV in females across many countries; women are at greater risk when they have a limited role in sexual decision-making and protection, lower rates of sexual education, and higher rates of transactional sex"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-women-among-the-population-living-with-hiv"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Prevalence in children"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Share of children infected with HIV"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-children-under-five-years-old-with-hiv"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Children living with HIV"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When children are infected with HIV, transmission has typically occurred from the mother either during pregnancy or childbirth or through breastfeeding. This is called mother-to-child-transmission, or MTCT. This map shows the total number of children aged 14 and under living with HIV. Globally the number of children living with HIV peaked in 2005 at approximately 2.6 million."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/children-living-with-hiv"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""New HIV infections in children"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The map shows the total number of children newly infected with HIV yearly. Globally — with similar trends at national levels — the number of new infections in children peaked around the early 2000s (with over 500,000 new infections per year globally), followed by a rapid decline over the last decade."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/number-of-children-newly-infected-with-hiv"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Children orphaned from AIDS"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Some children have lost either one or both parents to AIDS. This does not necessarily imply that children orphaned by AIDS have HIV themselves (although, in some cases, HIV has been transmitted from mother to child). The chart shows the number of children (aged 17 and under) orphaned from AIDS deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/number-of-children-orphaned-from-aids"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Tuberculosis among people living with HIV"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Tuberculosis (TB) is the leading HIV-associated opportunistic infection in low- and middle-income countries, and it is a leading cause of death globally among people living with HIV. Death due to tuberculosis remains high among people living with HIV. However, the number of deaths is decreasing. Most of the global mortality due to TB among those with HIV is from cases in Sub-Saharan Africa."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The charts here show the number of TB patients who tested positive for HIV, the number receiving antiretroviral therapy, and the number of TB-related deaths among those living with HIV."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/tb-patients-tested-positive-for-hiv"", ""type"": ""chart"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/tb-patients-living-with-hiv-receiving-art"", ""type"": ""chart"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/tb-related-deaths-hiv"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""What can be done to prevent HIV/AIDS?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Antiretroviral therapy"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A couple of decades ago, the chances of surviving more than ten years with HIV were slim. Today, thanks to antiretroviral therapy (ART), people with HIV/AIDS can expect to live long lives."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""ART is a long-term medical treatment for HIV/AIDS. It works by suppressing the virus from multiplying in the body. This keeps the infection under control and helps to prevent the disease from progressing. ART is essential in progressing against HIV/AIDS because it saves lives, allows people with HIV to live longer, and prevents new HIV infections. Read more in our article:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1KeagpzX2OMBS63fnyFRXMbU7QpBIgykKRZBTMdeHFAo/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""text"": [{""text"": ""Prevention of mother-to-child transmission (PMTCT)"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Given that most AIDS cases in children are due to the virus transmission from mother to child during pregnancy, stopping mother-to-child transmission is critical to preventing children from getting infected with HIV."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chances of an HIV-positive mother transmitting the virus to a child are between 15% and 45%. Effective prevention of mother-to-child transmission (PMTCT) services can reduce the chances of transmission to newborns down to 5%."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""PMTCT services include preventative measures such as antiviral therapy for mothers and newborns, correct breastfeeding practices, and early child testing for HIV infection."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This visualization shows the number of child infections averted by ART coverage in mothers."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can explore the number of new HIV infections prevented by PMTCT as a result of antiretroviral therapy across the world "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/new-hiv-infections-averted-due-to-pmtct"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/number-of-new-hiv-child-infections-vs-number-of-infections-averted-due-to-pmtct"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Coverage of ART in pregnant women"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This map shows the share of pregnant women infected with HIV who receive antiretroviral therapy – a vital intervention to prevent the transmission from mother to child."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/art-coverage-for-pregnant-women"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Safe sex"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Unsafe sex is a leading risk factor for death in Sub-Saharan Africa"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/number-of-deaths-by-risk-factor?country=~Sub-Saharan+Africa+%28WB%29"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Share of people practicing safe sex"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The majority of HIV infections are transmitted through sexual activity."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Sexual transmission can be prevented through condom use (both in heterosexual and homosexual relationships). In the charts here, we see the prevalence of condom use, particularly in cases of “high-risk sex”, which is defined by this data’s source as non-marital, non-cohabiting sexual partner."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/condom-use-at-last-high-risk-sex"", ""type"": ""chart"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/condom-use-during-last-high-risk-sex"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Education on HIV/AIDS"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/young-people-with-knowledge-on-hiv-prevention"", ""type"": ""chart"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/knowledge-hiv-prevention-in-males-vs-females"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Funding to support efforts against HIV/AIDS"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/hiv-expenditure"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Funding needs to meet HIV targets"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/resource-needs-to-meet-hiv-targets"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Comparisons of UNAIDS and IHME estimates"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Several sources publish estimates on HIV and AIDS. Two of the most established sources, presented on this page, are UNAIDS and the Institute of Health Metrics and Evaluation (IHME). The charts below show a comparison of these two sources' estimates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Prevalence of HIV"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/prevalence-of-hiv-unaids-vs-ihme-estimates"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Incidence/new cases of HIV"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/new-cases-of-hiv-unaids-vs-ihme"", ""type"": ""chart"", ""parseErrors"": []}, {""top"": [], ""type"": ""all-charts"", ""heading"": ""Interactive charts on HIV / AIDS"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""40a993fd1e3a888b6fa938974876b80209b923fe"": {""id"": ""40a993fd1e3a888b6fa938974876b80209b923fe"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""World Health Organization (WHO)"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.who.int/hiv/topics/mtct/about/en/"", ""children"": [{""text"": "" 'Mother-to-child transmission of HIV' "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""[accessed November 2019]"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""41207f1922c0cd82537e88f52398ff71785770e8"": {""id"": ""41207f1922c0cd82537e88f52398ff71785770e8"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Greig, A., Peacock, D., Jewkes, R., & Msimang, S. (2008). Gender and AIDS: time to act. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""AIDS (London, England)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""22"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(Suppl 2), S35. Available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3356155/"", ""children"": [{""text"": ""online"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""82f8701e97f3dbdef5e7e54bdffe5cd6fd29ef5f"": {""id"": ""82f8701e97f3dbdef5e7e54bdffe5cd6fd29ef5f"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The latest study can be found at the website of the Lancet here: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.thelancet.com/gbd"", ""children"": [{""text"": ""TheLancet.com/GBD"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The 2017 study was published in the following publication: \""Roth, G. A., Abate, D., Abate, K. H., Abay, S. M., Abbafati, C., Abbasi, N., ... & Abdollahpour, I. (2018). Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""The Lancet"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", 392(10159), 1736-1788\"". It is online "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.healthdata.org/research-article/global-regional-and-national-age-sex-specific-mortality-282-causes-death-195"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""linear-topic-page"", ""title"": ""HIV / AIDS"", ""authors"": [""Max Roser"", ""Hannah Ritchie""], ""excerpt"": ""A global epidemic and the leading cause of death in some countries."", ""dateline"": ""This page was first published in November 2014 and last revised in December 2023."", ""subtitle"": ""A global epidemic and the leading cause of death in some countries."", ""sidebar-toc"": true, ""featured-image"": ""hiv-aids-thumbnail.png""}",1,2023-11-10 15:01:06,2023-08-01 14:01:51,2024-02-28 15:16:19,unlisted,ALBJ4LvGgphFfUe64UqK084il7_KCw6-NdcJERo-T2Cq-_eDa4qk_RowUahsocJ6zkQP8sY7JIDC07a2SPn_RA,,"Infection with HIV (human immunodeficiency virus) can lead to AIDS (acquired immunodeficiency syndrome). AIDS results in a gradual and persistent decline and failure of the immune system, resulting in a heightened risk of life-threatening infection and [cancers](https://ourworldindata.org/cancer). In the majority of cases, HIV is a sexually transmitted infection. However, HIV can also be transmitted from mother to child, during pregnancy or childbirth, or through breastfeeding. Non-sexual transmission can also occur by sharing injection equipment such as needles. Other research and writing on HIV/AIDS on Our World in Data: * [Antiretroviral therapy has saved millions of lives from AIDS and could save more](https://ourworldindata.org/art-lives-saved) **[See all interactive charts on HIV/AIDS ↓ ](#all-charts)** # HIV/AIDS is one of the world's most fatal infectious disease ## Almost 1 million people die from HIV/AIDS each year; in some countries, it's the leading cause of death HIV/AIDS is one of the world's most fatal infectious diseases – particularly across Sub-Saharan Africa, where the disease has had a massive impact on health outcomes and life expectancy in recent decades. The _Global Burden of Disease_ is a major global study on the causes of death and disease published in the medical journal _The Lancet_.1 These estimates of the annual number of deaths by cause are shown here. This chart shows the global total but can be explored for any country or region using the ""Change country"" button. According to the _Global Burden of Disease_ study, nearly a million people die yearly from HIV/AIDS. To put this into context: this is just over 50% higher than the number of deaths from [malaria](https://ourworldindata.org/malaria). It's _one of_ the largest killers globally, but for some countries – particularly across Sub-Saharan Africa, it's the _leading_ cause of death. # The global distribution of deaths from HIV/AIDS ## In some countries, HIV/AIDS is the cause of a quarter of all deaths Globally, around 1.5% of deaths are caused by HIV/AIDS. This share is high but masks the wide variations in the toll of HIV/AIDS worldwide. In some countries, this share was much higher. On this interactive map, we see the share of deaths that resulted from HIV/AIDS across the world. Across most regions, the share was low: across Europe, for example, it accounted for less than 0.1% of deaths. However, the share is very high across some countries – focused primarily in Southern Sub-Saharan Africa. ## Death rates are high across Sub-Saharan Africa The significant health burden of HIV/AIDS across Sub-Saharan Africa is also reflected in death rates. Death rates measure the number of deaths from HIV/AIDS per 100,000 individuals in a country or region. In the interactive map, we see the distribution of death rates worldwide. Most countries have a rate of less than 10 deaths per 100,000 – often much lower, below 5 per 100,000. Across Europe, the death rate is less than one per 100,000. Across Sub-Saharan Africa, the rates are much higher. Some countries in the South of the region have rates greater than 100 per 100,000. ## Death rates are highest for younger adults and children under five years old Which population groups are most at risk from HIV/AIDS? In the chart, we show death rates by age group. Here we see that the most at-risk group is younger adults (15 to 49-year-olds). Since HIV is primarily a sexually transmitted infection, where unsafe sex is a primary risk factor, this is what we would expect. But we also see that death rates are higher for children under five; that’s because HIV can be transmitted from mother to child if the mother is infected. # Is the world making progress in its fight against HIV/AIDS? ## How have cases and deaths changed over time? The 1990s saw a substantial increase in people infected with HIV and dying of AIDS. In the second half of that decade, over 3 million people were infected with HIV yearly. Since then, the number of new infections began to decline, and it's now below 2 million, the lowest number of new infections since 1990. As for mortality, AIDS-related deaths increased throughout the 1990s and peaked in the mid-2000s, with nearly 2 million annual deaths. Since then, the annual number of deaths from AIDS has declined and since halved. 2016 was the first year since the peak in which fewer than 1 million people died from AIDS. The chart also shows the continuing increase in the number of people living with HIV. The growth rate has slowed compared to the 1990s, but the absolute number is at the highest ever. ## Global deaths from HIV/AIDS halved within a decade The world has made significant progress against HIV/AIDS. Global deaths from AIDS have halved over the past decade. In the visualization, we see the global number of deaths from HIV/AIDS in recent decades – this is shown by age group. In the mid-2000s, global deaths peaked at almost 2 million per year. Driven mainly by the development and availability of antiretroviral therapy (ART), global deaths have more than halved since then. You can explore this change for any country or region using the ""Edit countries"" button on the interactive chart. ## HIV/AIDS once accounted for a large share of deaths in some countries, but rates are now falling Global progress on HIV/AIDS has been driven by significant improvements in the countries most affected by the epidemic. Today, the share of deaths remains high: more than 1 in 5 deaths in some countries are caused by HIV/AIDS. But in the past, this share was even higher. In the visualization, we see the change in the share of deaths from HIV/AIDS over time. From the 1990s through to the early 2000s, it was the cause of more than 1 in 3 deaths in several countries and even more than half of annual deaths in the late 1990s in Zimbabwe. Over the past decade, this share has fallen as antiretroviral treatment has become more widely available. ## HIV/AIDS has had a significant impact on life expectancy across Sub-Saharan Africa The health and mortality burden of HIV/AIDS across Sub-Saharan Africa has been considerable. We see this impact on health reflected in trends in [life expectancy](https://ourworldindata.org/life-expectancy). In the visualization, we show changes in life expectancy across select countries in Sub-Saharan Africa for which HIV/AIDS has had the most significant toll. We see a dramatic drop in life expectancy starting around 1990, coinciding with the rise of HIV. In Botswana, life expectancy fell by a decade; in Eswatini, it fell by two decades. Since the early 2000s — as progress has been made in tackling HIV — we see that life expectancy has been rising again. In some countries, life expectancy is higher than before the epidemic began. # The prevalence of HIV/AIDS ## Prevalence in the total population ### Share of the population with HIV You can explore the total number of people living with HIV/AIDS worldwide [here](https://ourworldindata.org/grapher/number-of-people-living-with-hiv). ### Number of new infections each year ## Prevalence by gender ### Is HIV/AIDS more common in men or women? There are differences in the prevalence of HIV and death rates from AIDS between men and women. The chart shows the share of women in populations living with HIV. As we see, HIV prevalence tends to be higher in women across Sub-Saharan Africa, although higher in males across most other regions. The trend in AIDS-related deaths shows the opposite: more men tend to die from AIDS every year than women. The reasons for differences in prevalence and death rates are complex; however, across Sub-Saharan Africa, women tend to be infected with HIV earlier than men and survive longer (explaining both the higher prevalence and lower annual AIDS deaths in women). Several gender inequality and social norm issues result in a higher prevalence of HIV in females across many countries; women are at greater risk when they have a limited role in sexual decision-making and protection, lower rates of sexual education, and higher rates of transactional sex2. ## Prevalence in children ### Share of children infected with HIV ### Children living with HIV When children are infected with HIV, transmission has typically occurred from the mother either during pregnancy or childbirth or through breastfeeding. This is called mother-to-child-transmission, or MTCT. This map shows the total number of children aged 14 and under living with HIV. Globally the number of children living with HIV peaked in 2005 at approximately 2.6 million. ### New HIV infections in children The map shows the total number of children newly infected with HIV yearly. Globally — with similar trends at national levels — the number of new infections in children peaked around the early 2000s (with over 500,000 new infections per year globally), followed by a rapid decline over the last decade. ### Children orphaned from AIDS Some children have lost either one or both parents to AIDS. This does not necessarily imply that children orphaned by AIDS have HIV themselves (although, in some cases, HIV has been transmitted from mother to child). The chart shows the number of children (aged 17 and under) orphaned from AIDS deaths. # Tuberculosis among people living with HIV Tuberculosis (TB) is the leading HIV-associated opportunistic infection in low- and middle-income countries, and it is a leading cause of death globally among people living with HIV. Death due to tuberculosis remains high among people living with HIV. However, the number of deaths is decreasing. Most of the global mortality due to TB among those with HIV is from cases in Sub-Saharan Africa. The charts here show the number of TB patients who tested positive for HIV, the number receiving antiretroviral therapy, and the number of TB-related deaths among those living with HIV. # What can be done to prevent HIV/AIDS? ## Antiretroviral therapy A couple of decades ago, the chances of surviving more than ten years with HIV were slim. Today, thanks to antiretroviral therapy (ART), people with HIV/AIDS can expect to live long lives. ART is a long-term medical treatment for HIV/AIDS. It works by suppressing the virus from multiplying in the body. This keeps the infection under control and helps to prevent the disease from progressing. ART is essential in progressing against HIV/AIDS because it saves lives, allows people with HIV to live longer, and prevents new HIV infections. Read more in our article: ### undefined undefined https://docs.google.com/document/d/1KeagpzX2OMBS63fnyFRXMbU7QpBIgykKRZBTMdeHFAo/edit ## Prevention of mother-to-child transmission (PMTCT) Given that most AIDS cases in children are due to the virus transmission from mother to child during pregnancy, stopping mother-to-child transmission is critical to preventing children from getting infected with HIV. The chances of an HIV-positive mother transmitting the virus to a child are between 15% and 45%. Effective prevention of mother-to-child transmission (PMTCT) services can reduce the chances of transmission to newborns down to 5%.3 PMTCT services include preventative measures such as antiviral therapy for mothers and newborns, correct breastfeeding practices, and early child testing for HIV infection. This visualization shows the number of child infections averted by ART coverage in mothers. You can explore the number of new HIV infections prevented by PMTCT as a result of antiretroviral therapy across the world [here](https://ourworldindata.org/grapher/new-hiv-infections-averted-due-to-pmtct). ### Coverage of ART in pregnant women This map shows the share of pregnant women infected with HIV who receive antiretroviral therapy – a vital intervention to prevent the transmission from mother to child. ## Safe sex ### Unsafe sex is a leading risk factor for death in Sub-Saharan Africa ### Share of people practicing safe sex The majority of HIV infections are transmitted through sexual activity. Sexual transmission can be prevented through condom use (both in heterosexual and homosexual relationships). In the charts here, we see the prevalence of condom use, particularly in cases of “high-risk sex”, which is defined by this data’s source as non-marital, non-cohabiting sexual partner. ## Education on HIV/AIDS ## Funding to support efforts against HIV/AIDS ### Funding needs to meet HIV targets # Comparisons of UNAIDS and IHME estimates Several sources publish estimates on HIV and AIDS. Two of the most established sources, presented on this page, are UNAIDS and the Institute of Health Metrics and Evaluation (IHME). The charts below show a comparison of these two sources' estimates. ### Prevalence of HIV ### Incidence/new cases of HIV The latest study can be found at the website of the Lancet here: [TheLancet.com/GBD](https://www.thelancet.com/gbd) The 2017 study was published in the following publication: ""Roth, G. A., Abate, D., Abate, K. H., Abay, S. M., Abbafati, C., Abbasi, N., ... & Abdollahpour, I. (2018). Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. _The Lancet_, 392(10159), 1736-1788"". It is online [here](http://www.healthdata.org/research-article/global-regional-and-national-age-sex-specific-mortality-282-causes-death-195). Greig, A., Peacock, D., Jewkes, R., & Msimang, S. (2008). Gender and AIDS: time to act. _AIDS (London, England)_, _22_(Suppl 2), S35. Available [online](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3356155/). World Health Organization (WHO)[ 'Mother-to-child transmission of HIV' ](https://www.who.int/hiv/topics/mtct/about/en/)[accessed November 2019]",HIV / AIDS 1w8NnsKpAtHpFDQZ17Y_h_BMiO7uYEURbZLl4ZbMXpGc,sdgs/clean-water-sanitation,article,"{""toc"": [{""slug"": ""target-6-1-safe-and-affordable-drinking-water"", ""text"": ""Safe and affordable drinking water"", ""title"": ""Safe and affordable drinking water"", ""supertitle"": ""Target 6.1"", ""isSubheading"": false}, {""slug"": ""sdg-indicator-6-1-1-safe-drinking-water"", ""text"": ""Safe drinking water"", ""title"": ""Safe drinking water"", ""supertitle"": ""SDG Indicator 6.1.1"", ""isSubheading"": true}, {""slug"": ""target-6-2-end-open-defecation-and-provide-access-to-sanitation-and-hygiene"", ""text"": ""End open defecation and provide access to sanitation and hygiene"", ""title"": ""End open defecation and provide access to sanitation and hygiene"", ""supertitle"": ""Target 6.2"", ""isSubheading"": false}, {""slug"": 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the world stands today and how it has changed over time. More details can be found in the _Our World in Data_ topic pages on [Clean Water](https://ourworldindata.org/water-access) and [Sanitation](https://ourworldindata.org/sanitation). The [UN has defined](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf) 8 _targets_ and 11 _indicators_ for SDG 6. Targets specify the goals and indicators represent the metrics by which the world aims to track whether these targets are achieved. Below we quote the original text of all targets and show the data on the agreed indicators. ## Target 6.1 Safe and affordable drinking water ### SDG Indicator 6.1.1 Safe drinking water **Definition of the SDG indicator:** Indicator 6.1.1 is the “proportion of population using safely managed drinking water services” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). A safely managed drinking water service is defined as an improved source located on premises, available when needed and free from faecal (_E.coli _or thermotolerant coliforms) and priority chemical contamination (from arsenic and fluoride). The interactive visualizations show data for this indicator. The first chart shows the proportion of the population of countries who use safely managed drinking water, and the second chart shows how these proportions compare across rural and urban populations of a given country. **Target:** By 2030 “achieve universal and equitable access to safe and affordable drinking water for all”. **More research:** Further data and research on this topic can be found at the _Our World in Data_ topic page on [Water Access, Resources & Sanitation](https://ourworldindata.org/water-access-resources-sanitation). * [Number of people with access to safely managed drinking water](https://ourworldindata.org/explorers/water-and-sanitation?facet=none&Resource=Drinking+water&Level+of+Access=Safely+managed&Residence=Total&Relative+to+population=Number+of+people&country=IND~USA~KEN~OWID_WRL~BGD~ZAF~CHN) * [Safely managed drinking water, rural vs. urban](https://ourworldindata.org/grapher/urban-vs-rural-safely-managed-drinking-water-source) * [Drinking water service coverage in urban areas](https://ourworldindata.org/grapher/drinking-water-services-coverage-urban) * [Drinking water service coverage in rural areas](https://ourworldindata.org/grapher/drinking-water-services-coverage-rural) * [Share of population using at least basic water sources](https://ourworldindata.org/grapher/population-using-at-least-basic-drinking-water) undefined ## Target 6.2 End open defecation and provide access to sanitation and hygiene ### SDG Indicator 6.2.1 Safe sanitation and hygiene **Definition of the SDG indicator:** Indicator 6.2.1 is the “proportion of population using (a) safely managed sanitation services and (b) a hand-washing facility with soap and water” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). In this context, a managed sanitation facility one that is not shared with other households, and where excreta is safely disposed of in situ or treated off-site. A basic handwashing facility is defined by a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the home. Data for this indicator is shown in the interactive visualizations. The first chart shows the proportion of country populations using safely managed sanitation services and the second chart shows the proportion with basic handwashing facilities available in their home. **Target:** By 2030 “achieve access to adequate and equitable sanitation and hygiene for all and end open defecation, paying special attention to the needs of women and girls and those in vulnerable situations.” **More research:** Further data and research on this topic can be found at the _Our World in Data_ topic page on [Water Access, Resources & Sanitation](https://ourworldindata.org/water-access-resources-sanitation). * [Number of people without safely managed sanitation facilities](https://ourworldindata.org/grapher/safe-sanitation-without) * [Safely managed sanitation facilities, urban vs. rural](https://ourworldindata.org/grapher/urban-vs-rural-population-safely-managed-sanitation) * [Sanitation facilities coverage in urban areas](https://ourworldindata.org/grapher/sanitation-facilities-coverage-in-urban-areas) * [Sanitation facilities coverage in rural areas](https://ourworldindata.org/grapher/sanitation-facilities-coverage-in-rural-areas) * [Share of population using basic handwashing facilities, urban vs. rural](https://ourworldindata.org/grapher/proportion-with-basic-handwashing-facilities-urban-vs-rural) undefined ## Target 6.3 Improve water quality, wastewater treatment and safe reuse ### SDG Indicator 6.3.1 Wastewater safety **Definition of the SDG indicator:** Indicator 6.3.1 is the “proportion of domestic and industrial wastewater flows safely treated” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). This indicator is measured as the ratio of the volume of wastewater treated before being discharged into the environment to the volume of wastewater being generated through particular activities. Domestic wastewater refers to that generated by residential settlements, which is primarily generated through human metabolism and by household activities. Industrial wastewater refers to that which is discharged after being used in, or produced by, industrial production processes, and is of no further immediate value to these processes. Wastewater treatment involves physical, chemical or biological processes that remove solids, microorganisms and pollutants from water, to render it fit to meet environmental standards or quality norms for recycling or reuse. The interactive visualizations show data for this indicator. The first chart shows the proportion of domestic wastewater that is safely treated and the second chart shows the proportion of industrial wastewater that is treated. **Target:** “Halving the proportion of untreated wastewater and substantially increasing recycling and safe reuse globally” by 2030.1 **More research:** Further data and research on this topic can be found at the _Our World in Data_ topic page on [Water Access, Resources & Sanitation](https://ourworldindata.org/water-access-resources-sanitation). undefined ### SDG Indicator 6.3.2 Ambient water quality **Definition of the SDG indicator:** Indicator 6.3.2 is the “proportion of bodies of water with good ambient water quality” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). Good ambient water quality is defined as having at least 80 percent compliance to country-specific targets for core physical and chemical parameters that reflect nature water quality related to factors such as climate and geology, as well as factors that may impact water quality. This indicator measures the share of river, lake, and groundwater bodies in a country that have good ambient water quality, out of those that are deemed representative and significant for water quality monitoring. Data for this indicator is shown in the interactive visualization. **Target:** By 2030 “improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials.”1 **More research:** Further data and research on this topic can be found at the _Our World in Data_ topic page on [Water Access, Resources & Sanitation](https://ourworldindata.org/water-access-resources-sanitation). ## Target 6.4 Increase water use efficiency and ensure freshwater supplies ### SDG Indicator 6.4.1 Water use efficiency **Definition of the SDG indicator:** Indicator 6.4.1 is the “change in water-use efficiency over time” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). Water use efficiency is measured as the ratio of the value added by a particular sector, to the volume of the water used. Data for this indicator is shown in the interactive visualization, using the measure of water productivity. This is a measure of how water is used to generate economic value. **Target:** By 2030 “substantially increase water-use efficiency across all sectors.”2 **More research:** Further data and research on this topic can be found at the _Our World in Data_ topic page on [Water Access, Resources & Sanitation](https://ourworldindata.org/water-access-resources-sanitation). ### SDG Indicator 6.4.2 Levels of freshwater stress **Definition of the SDG indicator:** Indicator 6.4.2 is the “level of water stress: freshwater withdrawal as a proportion of available freshwater resources” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). Water stress is the ratio between total freshwater withdrawn by all major sectors of the economy and total renewable freshwater resources, taking into account environmental flow requirements. This includes water withdrawn for use in agriculture, industries and services (including domestic uses). Water stress is defined by the following categories: <25% is no stress; 25-50% is low; 50-75% medium; 75-100% high; >100% critical. The UN considers 25% to be the upper limit for a safe, no-stress water use situation, with levels above 25% indicating potentially and increasingly problematic water usage. Data on this indicator for all sectors combined is shown in the interactive visualization. **Target:** By 2030 “ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity.”2 **More research:** Further data and research on this topic can be found at the _Our World in Data_ topic page on [Water Access, Resources & Sanitation](https://ourworldindata.org/water-access-resources-sanitation). * [Water withdrawals per capita](https://ourworldindata.org/grapher/water-withdrawals-per-capita?year=2015) * [Renewable water resources per capita](https://ourworldindata.org/grapher/renewable-water-resources-per-capita) ## Target 6.5 Implement integrated water resources management ### SDG Indicator 6.5.1 Integrated water management **Definition of the SDG indicator:** Indicator 6.5.1 is the “degree of integrated water resources management” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). This measure assesses the status of national development and implementation of Integrated Water Resource Management (IWRM) across the world. IWRM is a process that balances the development and management of water and related resources to maximize social and economic welfare, with ensuring the continued sustainability of vital ecosystems. This indicator for IWRM implementation is measured on a scale of 0 to 100 in six categories, with 91-100 considered very high; 71-90 high; 51-70 medium-high; medium-low 31-50; 11-30 low; and 0-10 very low. It is calculated from country surveys that focus on the extent to which policies and laws create a suitable environment to enable IWRM; the range and roles of varied institutions that can support IWRM; the tools available for decision-makers and users to make rational and informed choices ; and provision of budgeting and finance for water resources development and management. Data for this indicator is shown in the interactive visualization. **Target:** “By 2030, implement integrated water resources management at all levels, including through transboundary cooperation as appropriate.” **More research:** Further data and research on this topic can be found at the _Our World in Data_ topic page on [Water Access, Resources & Sanitation](https://ourworldindata.org/water-access-resources-sanitation). ### SDG Indicator 6.5.2 Transboundary water cooperation **Definition of the SDG indicator:** Indicator 6.5.2 is the “proportion of transboundary basin area with an operational arrangement for water cooperation” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). This indicator measures the share of the land area covered by transboundary basins (river or lake basins, or aquifer systems, that mark, cross, or are located on the boundaries of multiple countries) that are covered by operational cooperation agreements between countries. “Operational” in this context means whether the arrangement provides the basic elements needed to enable cooperation in water management: a joint body for cooperation; regular (at least annual) formal communications between countries in the form of meetings; joint or coordinated water management plans or objectives; and regular (at least annual) exchange of data and information. Data for this indicator is shown in the interactive visualization. **Target:** “By 2030, implement integrated water resources management at all levels, including through transboundary cooperation as appropriate.” ## Target 6.6 Protect and restore water-related ecosystems ### SDG Indicator 6.6.1 Protect and restore water-related ecosystems **Definition of the SDG indicator:** Indicator 6.6.1 is the “change in the extent of water-related ecosystems over time” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). This indicator incorporates data from a wide range of water bodies and measures: surface area of lakes and rivers (both permanent and seasonal area), reservoirs, mangroves, wetlands; water quality of reservoirs and lakes; flow of rivers; and level of groundwater. Data for this indicator on lakes and rivers (first chart), wetlands (second chart), and mangrove area (third chart) are shown in the interactive visualizations. **Target:** “By 2020, protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers and lakes.” The target was set to be achieved by 2020, unlike most SDG targets which have a timeline of 2030. undefined ## Target 6.a Expand water and sanitation support to developing countries ### SDG Indicator 6.a.1 Water and sanitation support **Definition of the SDG indicator:** Indicator 6.a.1 is the “amount of water- and sanitation-related official development assistance that is part of a government coordinated spending plan” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). Although this measure is defined in terms of total official development assistance allocated to water- and sanitation-related programs that is used in government budgets, the UN currently reports data only for total official development assistance, regardless of budgetary use. Official development assistance refers to flows to countries and territories on the Organization for Economic Co-operation and Development’s Development Assistance Committee (DAC) and to multilateral institutions which meet a [set of criteria](http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm) related to the source of the funding, the purpose of the transaction, and the concessional nature of the funding. Data on total ODA for water- and sanitation-related programs is shown in the interactive visualization. **Target:** “By 2030, expand international cooperation and capacity-building support to developing countries in water- and sanitation-related activities and programmes.”3 **More research:** Further data and research on this topic can be found at the _Our World in Data_ topic page on [Water Access, Resources & Sanitation](https://ourworldindata.org/water-access-resources-sanitation). ## Target 6.b Support local engagement in water and sanitation management ### SDG Indicator 6.b.1 Local participation in water and sanitation management **Definition of the SDG indicator:** Indicator 6.b.1 is the “proportion of local administrative units with established and operational policies and procedures for participation of local communities in water and sanitation management” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). This indicator is defined in terms of the share of local administrative units (as defined by the national government) that have an established and operational mechanism for individuals and communities to meaningfully contribute to planning programs about water management, sanitation management, and hygiene promotion. However, since this data is still being collected, the UN currently reports data for this indicator in terms of national policies. Data for this indicator is shown in the interactive visualization. **Target:** By 2030 “support and strengthen the participation of local communities in improving water and sanitation management.” Full text: “By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity.” Full text: “By 2030, expand international cooperation and capacity-building support to developing countries in waterand sanitation-related activities and programmes, including water harvesting, desalination, water efficiency, wastewater treatment, recycling and reuse technologies.” Full text: “By 2030, improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater and substantially increasing recycling and safe reuse globally.”",Ensure access to water and sanitation for all 1w6ymndlipKyB8QEQsvpMvB3qC9Fn1QiLAUV_nnjGWtc,income-inequality-before-and-after-taxes,article,"{""toc"": [{""slug"": ""the-extent-of-redistribution-over-time"", ""text"": ""The extent of redistribution over time"", ""title"": ""The extent of redistribution over time"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""what-the-gap-between-before-tax-and-after-tax-income-inequality-can-and-cannot-tell-us"", ""text"": ""What the gap between before-tax and after-tax income inequality can and cannot tell us"", ""title"": ""What the gap between before-tax and after-tax income inequality can and cannot tell us"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""When interpreting data on inequality, it's important to be clear about what is being measured: inequality of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""what?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Usually, it is the inequality of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""i"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""children"": [{""text"": ""ncomes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", but even here, there are different concepts to be aware of. The two definitions of income most commonly used are:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""Incomes counted "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""before"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" people have paid taxes and received any benefits from the government;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Incomes counted "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""after"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" such transfers."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Unsurprisingly, the level of inequality when measured before and after tax can differ substantially. The difference reflects the extent of redistribution achieved through a country’s tax and benefits system."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart below shows a measure of inequality – the "", ""spanType"": ""span-simple-text""}, {""id"": ""gini"", ""children"": [{""text"": ""Gini coefficient"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" – for both definitions of income for the United States. The higher the Gini, the more unequal incomes are. We see that, after taxes and benefits, inequality in the US is reduced substantially: the incomes of poorer households "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/incomes-across-distribution-lis?time=1963..latest&country=~USA&hideControls=true&Indicator=Mean+income+or+consumption%2C+by+decile&Decile=1+%28poorest%29&Income+measure=After+tax+vs.+before+tax&Period=Year&Adjust+for+cost+sharing+within+households+%28equivalized+income%29=true"", ""children"": [{""text"": ""are higher"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and the incomes of richer households "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/incomes-across-distribution-lis?time=1963..latest&country=~USA&hideControls=true&Indicator=Mean+income+or+consumption%2C+by+decile&Decile=10+%28richest%29&Income+measure=After+tax+vs.+before+tax&Period=Year&Adjust+for+cost+sharing+within+households+%28equivalized+income%29=true"", ""children"": [{""text"": ""are lower"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""aside"", ""caption"": [{""text"": ""You can select other countries in the chart using the ‘Change country or region’ option within the chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/gini-coefficient-before-and-after-tax-lis?country=~USA"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The extent of income redistribution in different countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following charts take a look across countries using similar data from another source."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following scatter chart shows the before-tax and after-tax "", ""spanType"": ""span-simple-text""}, {""id"": ""gini"", ""children"": [{""text"": ""Gini coefficient"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" in different countries against each other. Three diagonal lines are shown for reference. Along the top ‘No reduction’ line, inequality does not change after redistribution. The further a country falls below this line, the greater the reduction in inequality seen after taxes and benefits. Along the middle reference line, the Gini falls by a third; along the bottom line, it falls by one-half."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see some correlation between countries’ levels of before-tax and after-tax income inequality. Countries with the highest before-tax inequality tend to be among those with the highest after-tax inequality."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, the chart also shows the large variation in the extent of redistribution governments achieve through taxes and benefits. Countries as diverse as Belgium, Spain, Japan, the US, Turkey, and Chile all have a similar degree of inequality before taxes – a Gini coefficient around 0.5. But they achieve very different levels of redistribution. In Chile, taxes and benefits reduce inequality only slightly. In the US, the Gini falls by around a fifth. In Japan, it falls by around a third. In Belgium, inequality is reduced by almost half. Although before-tax inequality is similar in these countries, their levels of after-tax inequality are very different."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/inequality-of-incomes-before-and-after-taxes-and-transfers-scatter"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The extent of redistribution over time"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart shows another view on the same data, plotting the percentage reduction in the Gini coefficient that countries achieve through redistribution."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/tax-reduction-in-income-inequality?country=IRL~GBR~USA~KOR~MEX~BRA~CHL~IND~BEL~TUR~DEU~JPN"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""What the gap between before-tax and after-tax income inequality can and cannot tell us"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is important to bear in mind that the before-tax distribution of income is already the result of choices made by individuals who take taxes and benefits into consideration. Although before-tax income is sometimes referred to as ‘market income’, it would be wrong to think the way it is distributed reflects market forces alone, as if no government tax-and-benefits policies existed. You may, for example, be reluctant to increase your working hours if you know that doing so would put you in a different tax bracket."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While the change in inequality before and after tax gives us a measure of the extent of government redistribution, it does not tell us the total reduction in inequality "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""caused"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" by this redistribution."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Pensions provide another stark example. In the data above, public pensions are considered part of the redistribution achieved by governments; private pensions are not."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The incomes of individuals receiving a public pension will, of course, be lower before counting these payments; but it is not the case that their incomes would be equally low if they lived in a country with no public pension scheme: in this scenario, many of these people would have had private pensions instead."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another example can be found in the work of Thomas Piketty, Emmanuel Saez, and Stefanie Stancheva (2014), who point to evidence that decreasing top marginal tax rates incentivizes top earners, like CEOs, to bargain more aggressively for higher remuneration, thereby increasing pre-tax wage inequality."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" More generally, taxes and benefits can affect people’s incentives and opportunities in a number of ways that can shape market outcomes."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is also not the case that "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""all"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" redistributive taxes and benefits are reflected in this data. Indirect taxes, such as VAT, are not deducted when measuring after-tax income. VAT is a tax on consumption, and because poorer people spend a higher share of their income on average (or save a lower share), some consider such taxes to increase inequality."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Conversely, cash transfers are only one part of how "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/government-spending"", ""children"": [{""text"": ""governments spend"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" tax revenue. The provision of public goods contributes substantially to people’s standard of living and in many cases – such as subsidized housing, public education, or healthcare – it may benefit poorer households disproportionately. As with the data shown in this article, most estimates of after-tax income inequality do not account for such non-cash or in-kind benefits given the difficulty of valuing such benefits and attributing to them to individuals."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""vat_reference"": {""id"": ""vat_reference"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For a discussion of the regressivity of VAT in OECD countries, see: Alastair Thomas. ‘Reassessing the Regressivity of the VAT’. Fiscal Studies 43, no. 1 (2022): 23–38. Available from the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.oecd-ilibrary.org/taxation/reassessing-the-regressivity-of-the-vat_b76ced82-en"", ""children"": [{""text"": ""OECD here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""indirect_tax_papers"": {""id"": ""indirect_tax_papers"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""There are, however, a number of studies that try to estimate the extent of inequality accounting for the distributional impact of such in-kind benefits. See, for example: Paulus, Alari, Holly Sutherland, and Panos Tsakloglou. ‘The Distributional Impact of In-Kind Public Benefits in European Countries’. Journal of Policy Analysis and Management 29, no. 2 (2010): 243–66. Available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.iza.org/publications/dp/4581/the-distributional-impact-of-in-kind-public-benefits-in-european-countries"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""atkinson_bourguignon"": {""id"": ""atkinson_bourguignon"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""‘Inequality of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""what?’"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" is one of a checklist of questions suggested by prominent inequality researchers Anthony Atkinson and Francois Bourguignon (2015). Atkinson AB, Bourguignon F (2015). Handbook of income distribution. Vol. 2A. Elsevier. See p. xxxiv."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""before_and_after_is_shorthand"": {""id"": ""before_and_after_is_shorthand"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""As I do in this sentence, ‘before tax’ and ‘after tax’ are often used as shorthands to refer to income measured before or after "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""both"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" taxes "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""and"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" benefits have been paid and received."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It’s worth noting that inequality is sometimes measured according to a concept of income somewhere between these two categories: for example, after taxes have been paid but before benefits are received; or after only "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""some"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" tax and benefits transactions have happened but not others."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""0a91a3ab17d4379e96d8a78e459104bc3f681e8c"": {""id"": ""0a91a3ab17d4379e96d8a78e459104bc3f681e8c"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The definitions of before and after-tax income in the two data sources relied on in this article – the OECD and the Luxembourg Income Study – are, in general, very similar, and they largely rely on the same household survey data. As such, the estimates are generally very close. There are, however, some discrepancies. For example, the level of before-tax inequality in Finland is much higher in the OECD data than in the LIS data. The OECD provides detailed descriptions of its "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.oecd.org/els/soc/IDD-ToR.pdf"", ""children"": [{""text"": ""income concepts"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", as well as country-level metadata, which you can find at its "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.oecd.org/social/income-distribution-database.htm"", ""children"": [{""text"": ""Income Distribution Database"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" website. LIS provides very comprehensive metadata in its "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.lisdatacenter.org/frontend#/home"", ""children"": [{""text"": ""METIS"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.lisdatacenter.org/our-data/survey-comparability-tool/"", ""children"": [{""text"": ""Compare.it"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" tools."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3d7e07efcafcd5c77d5bbf3729a6cb06b59422cf"": {""id"": ""3d7e07efcafcd5c77d5bbf3729a6cb06b59422cf"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This is a simplification. In practice, a binary distinction does not capture well the many different types of pensions. The exact treatment of each kind varies somewhat between data sources. A particular outlier is the World Inequality Database, whose concept of before-tax income is measured "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""after"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" the operation of both public and private pension systems. This unusual definition of income is used in order to yield more consistent comparisons across countries, less impacted by the different ways countries organize pensions. You can explore the WID data and compare it with other sources in the Data Explorers we have collected here: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://docs.google.com/document/d/1rEhPkFIeAvIQeOHj69HHlHLd1MygsBX7jb79MnpFJmI/edit"", ""children"": [{""text"": ""OWID Data Collection: Inequality and Poverty"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""57af806ff0af50ad4dde1ed6f09266b7da8c62c0"": {""id"": ""57af806ff0af50ad4dde1ed6f09266b7da8c62c0"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Piketty, Thomas, Emmanuel Saez, and Stefanie Stantcheva. ‘Optimal Taxation of Top Labor Incomes: A Tale of Three Elasticities’. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""American Economic Journal: Economic Policy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" 6, no. 1 (February 2014): 230–71."", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1257/pol.6.1.230"", ""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""Available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://eml.berkeley.edu/~saez/piketty-saez-stantchevaAEJ14.pdf"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b569f08f76c5055d2ab6fe2b323288f961a2efe0"": {""id"": ""b569f08f76c5055d2ab6fe2b323288f961a2efe0"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For a summary focussing on the top of the distribution, see: Alvaredo, Facundo, Anthony B Atkinson, Thomas Piketty, and Emmanuel Saez. ‘The Top 1 Percent in International and Historical Perspective’. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Journal of Economic Perspectives"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" 27, no. 3 (1 August 2013): 3–20."", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1257/jep.27.3.3"", ""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""Available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://eml.berkeley.edu/~saez/alvaredo-atkinson-piketty-saezJEP13top1percent.pdf"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""bf6e8a06347eb410067ca22b175ace6676cdde5a"": {""id"": ""bf6e8a06347eb410067ca22b175ace6676cdde5a"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The reduction in the chart relates to percent, not percentage points, i.e., a reduction in the Gini from 0.4 to 0.3 gives a value of 25%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Income inequality before and after taxes: How much do countries redistribute income?"", ""authors"": [""Joe Hasell""], ""excerpt"": ""The redistribution of income achieved by governments through taxes and benefits varies hugely."", ""subtitle"": ""The redistribution of income achieved by governments through taxes and benefits varies hugely."", ""featured-image"": ""inequality-before-and-after-taxes-featured-image.png""}",1,2023-06-30 12:17:05,2023-07-03 04:04:55,2024-02-02 18:50:27,listed,ALBJ4LtVY10Zbbd9ocE735aTjhtnufNfO961d_eUK8QU8khLEtmyXYlIuhjg5yb1vN7uHx1X6_vJwQIg1nS2Vg,,"When interpreting data on inequality, it's important to be clear about what is being measured: inequality of _what?_1 Usually, it is the inequality of _i__ncomes_, but even here, there are different concepts to be aware of. The two definitions of income most commonly used are: * Incomes counted _before_ people have paid taxes and received any benefits from the government; * Incomes counted _after_ such transfers. Unsurprisingly, the level of inequality when measured before and after tax can differ substantially. The difference reflects the extent of redistribution achieved through a country’s tax and benefits system.2 The chart below shows a measure of inequality – the Gini coefficient – for both definitions of income for the United States. The higher the Gini, the more unequal incomes are. We see that, after taxes and benefits, inequality in the US is reduced substantially: the incomes of poorer households [are higher](https://ourworldindata.org/explorers/incomes-across-distribution-lis?time=1963..latest&country=~USA&hideControls=true&Indicator=Mean+income+or+consumption%2C+by+decile&Decile=1+%28poorest%29&Income+measure=After+tax+vs.+before+tax&Period=Year&Adjust+for+cost+sharing+within+households+%28equivalized+income%29=true), and the incomes of richer households [are lower](https://ourworldindata.org/explorers/incomes-across-distribution-lis?time=1963..latest&country=~USA&hideControls=true&Indicator=Mean+income+or+consumption%2C+by+decile&Decile=10+%28richest%29&Income+measure=After+tax+vs.+before+tax&Period=Year&Adjust+for+cost+sharing+within+households+%28equivalized+income%29=true). You can select other countries in the chart using the ‘Change country or region’ option within the chart. # The extent of income redistribution in different countries The following charts take a look across countries using similar data from another source.3 The following scatter chart shows the before-tax and after-tax Gini coefficient in different countries against each other. Three diagonal lines are shown for reference. Along the top ‘No reduction’ line, inequality does not change after redistribution. The further a country falls below this line, the greater the reduction in inequality seen after taxes and benefits. Along the middle reference line, the Gini falls by a third; along the bottom line, it falls by one-half. We see some correlation between countries’ levels of before-tax and after-tax income inequality. Countries with the highest before-tax inequality tend to be among those with the highest after-tax inequality. However, the chart also shows the large variation in the extent of redistribution governments achieve through taxes and benefits. Countries as diverse as Belgium, Spain, Japan, the US, Turkey, and Chile all have a similar degree of inequality before taxes – a Gini coefficient around 0.5. But they achieve very different levels of redistribution. In Chile, taxes and benefits reduce inequality only slightly. In the US, the Gini falls by around a fifth. In Japan, it falls by around a third. In Belgium, inequality is reduced by almost half. Although before-tax inequality is similar in these countries, their levels of after-tax inequality are very different. ### The extent of redistribution over time This chart shows another view on the same data, plotting the percentage reduction in the Gini coefficient that countries achieve through redistribution.4 ## What the gap between before-tax and after-tax income inequality can and cannot tell us It is important to bear in mind that the before-tax distribution of income is already the result of choices made by individuals who take taxes and benefits into consideration. Although before-tax income is sometimes referred to as ‘market income’, it would be wrong to think the way it is distributed reflects market forces alone, as if no government tax-and-benefits policies existed. You may, for example, be reluctant to increase your working hours if you know that doing so would put you in a different tax bracket. While the change in inequality before and after tax gives us a measure of the extent of government redistribution, it does not tell us the total reduction in inequality _caused_ by this redistribution. Pensions provide another stark example. In the data above, public pensions are considered part of the redistribution achieved by governments; private pensions are not.5 The incomes of individuals receiving a public pension will, of course, be lower before counting these payments; but it is not the case that their incomes would be equally low if they lived in a country with no public pension scheme: in this scenario, many of these people would have had private pensions instead. Another example can be found in the work of Thomas Piketty, Emmanuel Saez, and Stefanie Stancheva (2014), who point to evidence that decreasing top marginal tax rates incentivizes top earners, like CEOs, to bargain more aggressively for higher remuneration, thereby increasing pre-tax wage inequality.6 More generally, taxes and benefits can affect people’s incentives and opportunities in a number of ways that can shape market outcomes.7 It is also not the case that _all_ redistributive taxes and benefits are reflected in this data. Indirect taxes, such as VAT, are not deducted when measuring after-tax income. VAT is a tax on consumption, and because poorer people spend a higher share of their income on average (or save a lower share), some consider such taxes to increase inequality.8 Conversely, cash transfers are only one part of how [governments spend](https://ourworldindata.org/government-spending) tax revenue. The provision of public goods contributes substantially to people’s standard of living and in many cases – such as subsidized housing, public education, or healthcare – it may benefit poorer households disproportionately. As with the data shown in this article, most estimates of after-tax income inequality do not account for such non-cash or in-kind benefits given the difficulty of valuing such benefits and attributing to them to individuals.9 As I do in this sentence, ‘before tax’ and ‘after tax’ are often used as shorthands to refer to income measured before or after _both_ taxes _and_ benefits have been paid and received. It’s worth noting that inequality is sometimes measured according to a concept of income somewhere between these two categories: for example, after taxes have been paid but before benefits are received; or after only _some_ tax and benefits transactions have happened but not others. For a discussion of the regressivity of VAT in OECD countries, see: Alastair Thomas. ‘Reassessing the Regressivity of the VAT’. Fiscal Studies 43, no. 1 (2022): 23–38. Available from the [OECD here](https://www.oecd-ilibrary.org/taxation/reassessing-the-regressivity-of-the-vat_b76ced82-en). There are, however, a number of studies that try to estimate the extent of inequality accounting for the distributional impact of such in-kind benefits. See, for example: Paulus, Alari, Holly Sutherland, and Panos Tsakloglou. ‘The Distributional Impact of In-Kind Public Benefits in European Countries’. Journal of Policy Analysis and Management 29, no. 2 (2010): 243–66. Available [here](https://www.iza.org/publications/dp/4581/the-distributional-impact-of-in-kind-public-benefits-in-european-countries). ‘Inequality of _what?’_ is one of a checklist of questions suggested by prominent inequality researchers Anthony Atkinson and Francois Bourguignon (2015). Atkinson AB, Bourguignon F (2015). Handbook of income distribution. Vol. 2A. Elsevier. See p. xxxiv. The definitions of before and after-tax income in the two data sources relied on in this article – the OECD and the Luxembourg Income Study – are, in general, very similar, and they largely rely on the same household survey data. As such, the estimates are generally very close. There are, however, some discrepancies. For example, the level of before-tax inequality in Finland is much higher in the OECD data than in the LIS data. The OECD provides detailed descriptions of its [income concepts](https://www.oecd.org/els/soc/IDD-ToR.pdf), as well as country-level metadata, which you can find at its [Income Distribution Database](https://www.oecd.org/social/income-distribution-database.htm) website. LIS provides very comprehensive metadata in its [METIS](https://www.lisdatacenter.org/frontend#/home) and [Compare.it](https://www.lisdatacenter.org/our-data/survey-comparability-tool/) tools. The reduction in the chart relates to percent, not percentage points, i.e., a reduction in the Gini from 0.4 to 0.3 gives a value of 25%. This is a simplification. In practice, a binary distinction does not capture well the many different types of pensions. The exact treatment of each kind varies somewhat between data sources. A particular outlier is the World Inequality Database, whose concept of before-tax income is measured _after_ the operation of both public and private pension systems. This unusual definition of income is used in order to yield more consistent comparisons across countries, less impacted by the different ways countries organize pensions. You can explore the WID data and compare it with other sources in the Data Explorers we have collected here: [OWID Data Collection: Inequality and Poverty](https://docs.google.com/document/d/1rEhPkFIeAvIQeOHj69HHlHLd1MygsBX7jb79MnpFJmI/edit). Piketty, Thomas, Emmanuel Saez, and Stefanie Stantcheva. ‘Optimal Taxation of Top Labor Incomes: A Tale of Three Elasticities’. _American Economic Journal: Economic Policy_ 6, no. 1 (February 2014): 230–71.[ ](https://doi.org/10.1257/pol.6.1.230)Available [here](https://eml.berkeley.edu/~saez/piketty-saez-stantchevaAEJ14.pdf). For a summary focussing on the top of the distribution, see: Alvaredo, Facundo, Anthony B Atkinson, Thomas Piketty, and Emmanuel Saez. ‘The Top 1 Percent in International and Historical Perspective’. _Journal of Economic Perspectives_ 27, no. 3 (1 August 2013): 3–20.[ ](https://doi.org/10.1257/jep.27.3.3)Available [here](https://eml.berkeley.edu/~saez/alvaredo-atkinson-piketty-saezJEP13top1percent.pdf).",Income inequality before and after taxes: How much do countries redistribute income? 1w57_m0ejckQlpRn57XuHMFTKAOn24-7kdAWL2WBOXxE,where-does-plastic-accumulate,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""The world now produces more than "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/plastic-pollution#how-much-plastic-does-the-world-produce"", ""children"": [{""text"": ""380 million tonnes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of plastic every year, which could end up as pollutants, entering our natural environment and oceans."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Of course, not all of our plastic waste ends up in the ocean, most ends up in landfills: it’s estimated that the share of global plastic waste that enters the ocean is around 3%."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In 2010 – the year for which we have the latest estimates – that was around 8 million tonnes."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most of the plastic materials we produce are less dense than water and should therefore float at the ocean surface. But our best estimates of the amount of plastic afloat at sea are orders of magnitude lower than the amount of plastic that enters our oceans in a single year: as we show in the visualization, it’s far lower than 8 million tonnes and instead in the order of 10s to 100s of thousands of tonnes. One of the most widely-quoted estimates is 250,000 tonnes."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we currently pollute our oceans with millions of tonnes of plastic each year, we must have released tens of millions of tonnes in recent decades. Why then do we find at least 100 times less plastics in our surface waters?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This discrepancy is often referred to as the ‘missing plastic problem’."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It’s a conundrum we need to address if we want to understand where plastic waste could end up, and what its impacts might be for wildlife, ecosystems and health."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Pathway-of-plastic-to-ocean.png"", ""parseErrors"": []}, {""text"": [{""text"": ""The ‘missing plastic problem’"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are several hypotheses to explain the ‘missing plastic problem’."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One possibility is that it is due to imprecise measurement: we might either grossly overestimate the amount of plastic waste we release into the ocean, or underestimate the amount floating in the surface ocean. Whilst we know that tracking ocean plastic inputs and their distribution is notoriously difficult"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" the levels of uncertainty in these measurements are much less than the several orders of magnitude that would be needed to explain the missing plastic problem."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another popular hypothesis is that ultraviolet light (UV) and mechanical wave forces break large pieces of plastic into smaller ones.These smaller particles, referred to as microplastics, are much more easily incorporated into sediments or ingested by organisms. And this is where the missing plastic might end up."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One proposed ‘sink’ for ocean plastics was deep-sea sediments; a study which sampled deep-sea sediments across several basins found that microplastic was up to four orders of magnitude more abundant (per unit volume) in deep-sea sediments from the Atlantic Ocean, Mediterranean Sea and Indian Ocean than in plastic-polluted surface waters."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But, new research may suggest a third explanation: that plastics in the ocean break down slower than previously thought, and that much of the missing plastic is washed up or buried in our shorelines."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""Plastics persist for decades and accumulate on our shorelines"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To try to understand the conundrum of what happens to plastic waste when it enters the ocean, Lebreton, Egger and Slat (2019) created a global model of ocean plastics from 1950 to 2015. This model uses data on global plastic production, emissions into the ocean by plastic type and age, and transport and degradation rates to map not only the amount of plastic in different environments in the ocean, but also its age."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The authors aimed to quantify where plastic accumulates in the ocean across three environments: the shoreline (defined as dry land bordering the ocean), coastal areas (defined as waters with a depth less than 200 meters) and offshore (waters with a depth greater than 200 meters). They wanted to understand where plastic accumulates, and how old it is: a few years old, ten years or decades?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the visualization I summarized their results. This is shown for two categories of plastics: shown in blue are ‘macroplastics’ (larger plastic materials greater than 0.5 centimeters in diameter) and shown in red microplastics (smaller particles less than 0.5 centimeters)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Where-does-plastic-accumulate.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are some key points we can take away from the visualization:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""The vast majority – 82 million tonnes of macroplastics and 40 million tonnes of microplastics – is washed up, buried or resurfaced along the world’s shorelines."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Much of the macroplastics in our shorelines is from the past 15 years, but still a significant amount is older suggesting it can persist for several decades without breaking down."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In coastal regions most macroplastics (79%) are recent – less than 5 years old."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In offshore environments, older microplastics have had longer to accumulate than in coastal regions. There macroplastics from several decades ago – even as far back as the 1950s and 1960s – persist."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most microplastics (three-quarters) in offshore environments are from the 1990s and earlier, suggesting it can take several decades for plastics to break down."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What does this mean for our understanding of the ‘missing plastic’ problem?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Firstly"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "", is that the majority of ocean plastics are washed, buried and resurface along our shorelines. Whilst we try to tally ocean inputs with the amount floating in gyres at the centre of our oceans, most of it may be accumulating around the edges of the oceans. This would explain why we find much less in surface waters than we’d expect."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Secondly"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "", accumulated plastics are much older than previously thought. Macroplastics appear to persist in the surface of the ocean for decades without breaking down. Offshore we find large plastic objects dating as far back as the 1950s and 1960s. This goes against previous hypotheses of the ‘missing plastic’ problem which suggested that UV light and wave action degrade and remove them from the surface in only a few years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How much plastic will remain in surface oceans in the coming decades?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The study by Lebreton, Egger and Slat challenges the previous hypotheses that plastics in the surface ocean have a very short lifetime, quickly degrade into microplastics and sink to greater depths. Their results suggest that macroplastics can persist for decades; can be buried and resurfaced along shorelines; and end up in offshore regions years later."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If true, this matters a lot for how much plastic we would expect in our surface oceans in the decades which follow. The same study also modelled how the mass of plastics – both macro and micro – in the world’s surface waters might evolve under three scenarios:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""numbered-list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""we stop emitting any plastics to our oceans by 2020;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""‘emissions’ of plastic to the ocean continue to increase until 2020 then level off;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""‘emissions’ continue to grow to 2050 in line with historic growth rates."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Their results are shown in the charts."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""left"": [{""url"": ""https://ourworldindata.org/grapher/macroplastics-in-ocean"", ""type"": ""chart"", ""parseErrors"": []}], ""type"": ""side-by-side"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/microplastics-in-ocean"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The scenarios of continued emissions growth are what we’d expect: if we continue to release more plastics to the ocean, we’ll have more in our surface waters."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What’s more striking is that even if we stopped ocean plastic waste by 2020, macroplastics would persist in our surface waters for many more decades. This is because we have a large legacy of plastics buried and awash on our shorelines which would continue to resurface and be transported to offshore regions; and existing plastics can persist in the ocean environment for many decades."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The amount of microplastics in our surface ocean will increase under every scenario because the large plastics that we already have on our shorelines and surface waters will continue to breakdown. And, any additional plastics we add will contribute further."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This also matters for how we solve the problem of ocean pollution."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we want to rapidly reduce the amount of both macro- and microplastics in our oceans, these results suggest two priorities:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Number one"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" — we must stop plastic waste entering our waterways as soon as possible. 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"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.nature.com/articles/s41598-019-49413-5"", ""children"": [{""text"": ""A global mass budget for positively buoyant macroplastic debris in the ocean"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Scientific reports"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(1), 1-10."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a85fc74434b1f422a3ca1be1aed5d919ecb62d2a"": {""id"": ""a85fc74434b1f422a3ca1be1aed5d919ecb62d2a"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The estimates for this figure range from around 4 to 12 million tonnes, with 8 million as a midpoint. 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"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Royal Society Open Science"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", 1(4), 140317."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d2838e510231f46568d777570ebe5d05b46ca55d"": {""id"": ""d2838e510231f46568d777570ebe5d05b46ca55d"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Under growth scenarios, the authors assume annual  growth rates continue in line with the average increase in global plastic production over the decade from 2005-2015."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d590a04e3c8615e240dc956325deb4c72630914d"": {""id"": ""d590a04e3c8615e240dc956325deb4c72630914d"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Jambeck, J. R., Geyer, R., Wilcox, C., Siegler, T. R., Perryman, M., Andrady, A., … & Law, K. L. (2015). "", ""spanType"": ""span-simple-text""}, {""url"": ""http://science.sciencemag.org/content/347/6223/768"", ""children"": [{""text"": ""Plastic waste inputs from land into the ocean"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Science"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", 347(6223), 768-771."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Where does our plastic accumulate in the ocean and what does that mean for the future?"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""An estimated 8 million tonnes of plastic enter our oceans each year. But the plastic we find in our surface waters is more than 100-fold lower. This is the 'missing plastic' problem."", ""dateline"": ""September 24, 2019"", ""subtitle"": ""An estimated 8 million tonnes of plastic enter our oceans each year. But the plastic we find in our surface waters is more than 100-fold lower. This is the 'missing plastic' problem."", ""sidebar-toc"": false, ""featured-image"": ""Where-does-plastic-accumulate.png""}",1,2024-02-08 20:19:37,2019-09-24 08:00:48,2024-02-09 11:14:03,listed,ALBJ4LuTmeJ3EmPS3Ek-swNY8fBYro0xipD8BLrJGYoqRMm4R9C_OqPsRXy98srSztbW1NyL1JjZJ0vOBfF7Hg,,"The world now produces more than [380 million tonnes](https://ourworldindata.org/plastic-pollution#how-much-plastic-does-the-world-produce) of plastic every year, which could end up as pollutants, entering our natural environment and oceans. Of course, not all of our plastic waste ends up in the ocean, most ends up in landfills: it’s estimated that the share of global plastic waste that enters the ocean is around 3%.1 In 2010 – the year for which we have the latest estimates – that was around 8 million tonnes.2 Most of the plastic materials we produce are less dense than water and should therefore float at the ocean surface. But our best estimates of the amount of plastic afloat at sea are orders of magnitude lower than the amount of plastic that enters our oceans in a single year: as we show in the visualization, it’s far lower than 8 million tonnes and instead in the order of 10s to 100s of thousands of tonnes. One of the most widely-quoted estimates is 250,000 tonnes.3 If we currently pollute our oceans with millions of tonnes of plastic each year, we must have released tens of millions of tonnes in recent decades. Why then do we find at least 100 times less plastics in our surface waters? This discrepancy is often referred to as the ‘missing plastic problem’.4 It’s a conundrum we need to address if we want to understand where plastic waste could end up, and what its impacts might be for wildlife, ecosystems and health. # The ‘missing plastic problem’ There are several hypotheses to explain the ‘missing plastic problem’. One possibility is that it is due to imprecise measurement: we might either grossly overestimate the amount of plastic waste we release into the ocean, or underestimate the amount floating in the surface ocean. Whilst we know that tracking ocean plastic inputs and their distribution is notoriously difficult5 the levels of uncertainty in these measurements are much less than the several orders of magnitude that would be needed to explain the missing plastic problem.6 Another popular hypothesis is that ultraviolet light (UV) and mechanical wave forces break large pieces of plastic into smaller ones.These smaller particles, referred to as microplastics, are much more easily incorporated into sediments or ingested by organisms. And this is where the missing plastic might end up. One proposed ‘sink’ for ocean plastics was deep-sea sediments; a study which sampled deep-sea sediments across several basins found that microplastic was up to four orders of magnitude more abundant (per unit volume) in deep-sea sediments from the Atlantic Ocean, Mediterranean Sea and Indian Ocean than in plastic-polluted surface waters.7 But, new research may suggest a third explanation: that plastics in the ocean break down slower than previously thought, and that much of the missing plastic is washed up or buried in our shorelines.6 # Plastics persist for decades and accumulate on our shorelines To try to understand the conundrum of what happens to plastic waste when it enters the ocean, Lebreton, Egger and Slat (2019) created a global model of ocean plastics from 1950 to 2015. This model uses data on global plastic production, emissions into the ocean by plastic type and age, and transport and degradation rates to map not only the amount of plastic in different environments in the ocean, but also its age. The authors aimed to quantify where plastic accumulates in the ocean across three environments: the shoreline (defined as dry land bordering the ocean), coastal areas (defined as waters with a depth less than 200 meters) and offshore (waters with a depth greater than 200 meters). They wanted to understand where plastic accumulates, and how old it is: a few years old, ten years or decades? In the visualization I summarized their results. This is shown for two categories of plastics: shown in blue are ‘macroplastics’ (larger plastic materials greater than 0.5 centimeters in diameter) and shown in red microplastics (smaller particles less than 0.5 centimeters). There are some key points we can take away from the visualization: * The vast majority – 82 million tonnes of macroplastics and 40 million tonnes of microplastics – is washed up, buried or resurfaced along the world’s shorelines. * Much of the macroplastics in our shorelines is from the past 15 years, but still a significant amount is older suggesting it can persist for several decades without breaking down. * In coastal regions most macroplastics (79%) are recent – less than 5 years old. * In offshore environments, older microplastics have had longer to accumulate than in coastal regions. There macroplastics from several decades ago – even as far back as the 1950s and 1960s – persist. * Most microplastics (three-quarters) in offshore environments are from the 1990s and earlier, suggesting it can take several decades for plastics to break down. What does this mean for our understanding of the ‘missing plastic’ problem? **Firstly**, is that the majority of ocean plastics are washed, buried and resurface along our shorelines. Whilst we try to tally ocean inputs with the amount floating in gyres at the centre of our oceans, most of it may be accumulating around the edges of the oceans. This would explain why we find much less in surface waters than we’d expect. **Secondly**, accumulated plastics are much older than previously thought. Macroplastics appear to persist in the surface of the ocean for decades without breaking down. Offshore we find large plastic objects dating as far back as the 1950s and 1960s. This goes against previous hypotheses of the ‘missing plastic’ problem which suggested that UV light and wave action degrade and remove them from the surface in only a few years. # How much plastic will remain in surface oceans in the coming decades? The study by Lebreton, Egger and Slat challenges the previous hypotheses that plastics in the surface ocean have a very short lifetime, quickly degrade into microplastics and sink to greater depths. Their results suggest that macroplastics can persist for decades; can be buried and resurfaced along shorelines; and end up in offshore regions years later. If true, this matters a lot for how much plastic we would expect in our surface oceans in the decades which follow. The same study also modelled how the mass of plastics – both macro and micro – in the world’s surface waters might evolve under three scenarios: 0. we stop emitting any plastics to our oceans by 2020; 1. ‘emissions’ of plastic to the ocean continue to increase until 2020 then level off; 2. ‘emissions’ continue to grow to 2050 in line with historic growth rates.8 Their results are shown in the charts. The scenarios of continued emissions growth are what we’d expect: if we continue to release more plastics to the ocean, we’ll have more in our surface waters. What’s more striking is that even if we stopped ocean plastic waste by 2020, macroplastics would persist in our surface waters for many more decades. This is because we have a large legacy of plastics buried and awash on our shorelines which would continue to resurface and be transported to offshore regions; and existing plastics can persist in the ocean environment for many decades. The amount of microplastics in our surface ocean will increase under every scenario because the large plastics that we already have on our shorelines and surface waters will continue to breakdown. And, any additional plastics we add will contribute further. This also matters for how we solve the problem of ocean pollution. If we want to rapidly reduce the amount of both macro- and microplastics in our oceans, these results suggest two priorities: _Number one_ — we must stop plastic waste entering our waterways as soon as possible. Most of the plastic that ends up in our oceans does so because of [poor waste management](https://ourworldindata.org/plastic-pollution#mismanaged-plastic-waste) practices – particularly in [low-to-middle income countries](https://ourworldindata.org/plastic-pollution#what-determines-how-much-mismanaged-waste-we-produce); this means that good waste management across the world is essential to achieving this. But this ambitious target alone will not be enough. We have many decades of legacy waste to contend with. This makes a _second priority_ necessary— we have to focus our efforts on recapturing and removing plastics already in our offshore waters and shorelines. This is the goal of Slat, Lebreton and Egger – the authors of this paper – with their [Ocean Cleanup](https://theoceancleanup.com/) project. Jambeck, J. R., Geyer, R., Wilcox, C., Siegler, T. R., Perryman, M., Andrady, A., … & Law, K. L. (2015). [Plastic waste inputs from land into the ocean](http://science.sciencemag.org/content/347/6223/768). _Science_, 347(6223), 768-771. The estimates for this figure range from around 4 to 12 million tonnes, with 8 million as a midpoint. In the context of this discussion, the uncertainty in this value is less important: the difference between ocean plastic inputs and observed plastic in surface ocean waters are orders of magnitude – rather than multiples – apart. Eriksen, M. et al. [Plastic pollution in the world’s oceans: more than 5 trillion plastic pieces weighing over 250,000 tons afloat at sea](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0111913). _Plos One_ 9, e111913 (2014). Lebreton, L., Slat, B., Ferrari, F., Sainte-Rose, B., Aitken, J., Marthouse, R., … & Noble, K. (2018). Evidence that the Great Pacific Garbage Patch is rapidly accumulating plastic. _Scientific Reports_, _8_(1), 4666. Available at:[ https://www.nature.com/articles/s41598-018-22939-w](https://www.nature.com/articles/s41598-018-22939-w). Cressey, D. (2016). [Bottles, bags, ropes and toothbrushes: the struggle to track ocean plastics](https://www.nature.com/news/bottles-bags-ropes-and-toothbrushes-the-struggle-to-track-ocean-plastics-1.20432). _Nature News_, _536_(7616), 263. Lebreton, L., Egger, M., & Slat, B. (2019). [A global mass budget for positively buoyant macroplastic debris in the ocean](https://www.nature.com/articles/s41598-019-49413-5). _Scientific reports_, _9_(1), 1-10. Woodall, L. C., Sanchez-Vidal, A., Canals, M., Paterson, G. L., Coppock, R., Sleight, V., … & Thompson, R. C. (2014). [The deep sea is a major sink for microplastic debris](http://rsos.royalsocietypublishing.org/content/1/4/140317). _Royal Society Open Science_, 1(4), 140317. Under growth scenarios, the authors assume annual  growth rates continue in line with the average increase in global plastic production over the decade from 2005-2015.",Where does our plastic accumulate in the ocean and what does that mean for the future? 1vuu95_oTWllhAuUEeomhMAUvprw3cdBQbs-MzZsSKEc,testing-rates-for-polio-have-rebounded-after-a-drop-amid-the-covid-19-pandemic,data-insight,"{""body"": [{""size"": ""narrow"", ""type"": ""image"", ""filename"": ""share-of-potential-polio-cases-with-adequate-stool-collection-desktop.png"", ""parseErrors"": [], ""smallFilename"": ""share-of-potential-polio-cases-with-adequate-stool-collection-mobile.png""}, {""type"": ""text"", ""value"": [{""text"": ""The world is close to eradicating polio. Annual cases have dropped from an "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/number-of-estimated-paralytic-polio-cases-by-world-region"", ""children"": [{""text"": ""estimated 400,000"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in the 1980s to less than 4,000 in recent years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But, to cross the finish line, sufficient testing is crucial to ensure that cases aren’t missed."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The World Health Organization recommends that at least 80% of potential polio cases be tested for the virus. 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Thankfully, this new data shows that polio testing has rebounded."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is due to the dedicated effort of countless health workers and opens the way to a future free from polio."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""data-insight"", ""title"": ""Testing rates for polio have rebounded after a drop amid the COVID-19 pandemic"", ""authors"": [""Saloni Dattani""], ""approved-by"": ""Ed"", ""grapher-url"": ""https://ourworldindata.org/grapher/share-of-potential-polio-cases-with-adequate-stool-collection""}",1,2024-04-26 15:59:42,2024-05-09 07:00:29,2024-05-08 06:13:55,unlisted,ALBJ4LsZKaIToD1osO6JPuoyCJsmA8A6ZuqtvRjhvZCaJOO7jCz3uJvLj393YCT1MPSl4ul0rhAnj2yVf1vi0w,," The world is close to eradicating polio. Annual cases have dropped from an [estimated 400,000](https://ourworldindata.org/grapher/number-of-estimated-paralytic-polio-cases-by-world-region) in the 1980s to less than 4,000 in recent years. But, to cross the finish line, sufficient testing is crucial to ensure that cases aren’t missed. The World Health Organization recommends that at least 80% of potential polio cases be tested for the virus. Potential cases are identified based on “acute flaccid paralysis”, a sudden onset of paralysis in the limbs. As shown in the chart, disruptions from the COVID-19 pandemic led to a [drop in polio testing rates](https://ourworldindata.org/polio-testing) in many countries. Thankfully, this new data shows that polio testing has rebounded. This is due to the dedicated effort of countless health workers and opens the way to a future free from polio.",Testing rates for polio have rebounded after a drop amid the COVID-19 pandemic 1vudtwtn6RQAbTEJlioxxvTCVWwI8fIQeRIGj1h9AokA,the-price-of-computer-storage-has-fallen-exponentially-since-the-1950s,data-insight,"{""body"": [{""size"": ""narrow"", ""type"": ""image"", ""filename"": ""historical-cost-of-computer-memory-and-storage-desktop.png"", ""parseErrors"": [], ""smallFilename"": ""historical-cost-of-computer-memory-and-storage-mobile.png""}, {""type"": ""text"", ""value"": [{""text"": ""This chart shows the dramatic fall in the price of computer storage between 1956 and 2023. It relies on the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://jcmit.net/memoryprice.htm"", ""children"": [{""text"": ""data carefully collected"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" by the computer scientist John C. McCallum."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the last 70 years, the price for a unit of storage has fallen by almost ten orders of magnitude. The data is plotted on a logarithmic scale on the vertical axis. The line follows an almost straight path, indicating an exponential reduction in price."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A 256-gigabyte storage capacity — commonly found in standard laptops sold today — would have cost around 20 billion dollars in the 1950s. (That’s in today’s prices.)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And cost has not been the only improvement: modern solid-state drives offer much faster and more reliable data access than early magnetic and hard disk drives."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/moores-law"", ""children"": [{""text"": ""Read more on the exponential growth of computing capabilities"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" →"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""data-insight"", ""title"": ""The price of computer storage has fallen exponentially since the 1950s"", ""authors"": [""Edouard Mathieu""], ""approved-by"": ""Hannah"", ""grapher-url"": ""https://ourworldindata.org/grapher/historical-cost-of-computer-memory-and-storage""}",1,2024-05-13 14:34:37,2024-05-21 06:43:44,2024-05-16 09:46:39,unlisted,ALBJ4Lt4uhNjhoY0bMtb-sWpArMVN9-kPc6gNKOmmoivYmVbL-9SyTPSBFH00orGZuphjfI68a0GBPH1EGLCkA,," This chart shows the dramatic fall in the price of various computer storage types between 1956 and 2023. It relies on the data carefully collected by the computer scientist John C. McCallum. In the last 70 years, the price for a given unit of storage has been divided by almost nine orders of magnitude. A 256-gigabyte storage capacity, commonly found in standard laptops sold today, would have cost around 2 billion dollars in the 1950s. But price has not been the only improvement: modern solid-state drives offer vastly faster and more reliable data access than early magnetic and hard disk drives. [Read more on the exponential growth of computing capabilities](https://ourworldindata.org/moores-law) →",The price of computer storage has fallen exponentially since the 1950s 1vuVtBRkB1BncGprb7OJr-8wk0wGnnWoM65x88CEtdFc,carbon-opportunity-costs-food,article,"{""toc"": [{""slug"": ""the-carbon-opportunity-costs-of-different-diets"", ""text"": ""The carbon opportunity costs of different diets"", ""title"": ""The carbon opportunity costs of different diets"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""text"": [{""type"": ""text"", ""value"": [{""text"": ""The climate impact of diets are usually compared in terms of greenhouse gases that are emitted "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""today"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". But this misses a hidden cost: the carbon opportunity costs of agricultural land. If we were not using this land to grow food, it would be possible that forests and wild grasslands grow on these lands. They would not only harbour wildlife, but also store much more carbon. Meat and dairy products need more land than alternatives, and therefore have a higher opportunity cost."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Going vegan would result in the largest carbon savings, but even just a reduction of meat and dairy consumption – without eliminating it completely – can also have a massive impact. In fact, a diet that replaces beef with chicken and cuts out dairy would achieve almost as much as a fully vegan diet."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Summary"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Over the last 10,000 years  agricultural land has expanded into forests, wild grasslands and other ecosystems. The world "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/world-lost-one-third-forests"", ""children"": [{""text"": ""lost one-third of its forests"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and today agricultural land makes up "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/global-land-for-agriculture"", ""children"": [{""text"": ""half of the world’s ice- and desert-free land"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The loss of these forests and other natural vegetation has released a lot of carbon into the atmosphere: we have emitted around 1400 billion tonnes of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" over millennia."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" That’s equal to 40 years’ worth of our current emissions from fossil fuels."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It can be easy to forget about these emissions that happened decades, centuries, or even millennia ago. We tend to only focus on emissions "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""today"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". This undersells the role that our agricultural land use could play in tackling climate change. In this article I’ll explain why this is the case."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To understand this we need to consider one of the key concepts in economics: opportunity costs. An opportunity cost is the potential benefit you’re giving up by choosing one option over the other. Every decision you make has an opportunity cost – you could be spending your time or money on something else. Spending time watching television comes at the ‘cost’ of not reading a book or not visiting a friend. Choosing pizza comes at the cost of not having pasta instead."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the standard framework of counting greenhouse gas emissions, opportunity costs are not taken into account. The ‘carbon footprint’ figures usually reported for different foods are based on greenhouse gas "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""emissions today"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "": how much nitrous oxide is produced when we add fertilizers; methane released by cows; carbon released when we cut down forest and replace it with crops. Land use is not included unless it "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""changed"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" in the last year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The opportunity costs of land are the possible alternative uses for this land. If we weren’t using it to grow crops or raise livestock, it could be restored to forest or wild grasslands. Restoring these could take at least some of the 1400 billion tonnes of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" back out of the atmosphere, and put it back into vegetation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Storing this carbon in vegetation and soils is the opposite of emissions. It’s negative emissions. Since we need to urgently reduce the amount of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" in the atmosphere, minimising the amount of land the world needs to feed itself is a possible solution. Of course, CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" in the atmosphere is not the only metric we care about: there is a complex range of socioeconomic factors (such as the livelihoods of people who work in the farming sector) to consider. What we’re doing here is presenting the scientific understanding of what happens to one of those elements – carbon – across a range of possible futures.  It’s up to society to decide what it should do, given the choices available."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Here, we look at the impact our dietary choices could have when we factor in the opportunity costs. If the world gave up meat and dairy completely, how much carbon would we possibly save? Do we need to go vegan to make a big difference?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Vegan, vegetarian, flexitarian: how much carbon could different diets save?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Let’s first see how taking opportunity costs into account affect the comparisons of individual food items."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart here we see the comparison of the carbon costs of different meat and dairy foods, and high-protein substitutes."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In brown we see the emissions from food "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""production"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" – this is the metric that almost everyone uses to compare products. This tells us how much greenhouse gases have been emitted to produce the food over the full supply chain – from farm to supermarket. In green we see the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""opportunity costs"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "": this is the amount of carbon that could be stored on the land if we would decide to abandon it and let natural vegetation regrow."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we’d expect, because livestock such as cows and sheep need a lot of land, they have much higher opportunity costs. Producing one kilogram of beef can have total carbon costs at least ten times higher than protein-rich alternatives such as tofu or tempeh. In extreme cases, where beef and lamb are produced at low intensities – such as in Brazil – the opportunity costs of agricultural land are huge. Total carbon costs can be as much as 100 times higher than the alternatives."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What this also makes clear is that we can save a lot of carbon by making wiser choices about where to produce our food. Producing food where the yields are high reduces the amount of land we need for agriculture, giving us the opportunity to store carbon in forests and grasslands."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Carbon-opportunity-costs-Schmidinger-Stehfest.png"", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""The carbon opportunity costs of different diets"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Of course, for proper nutrition and health we need to eat a diverse range of foods. So let’s see how different diets – rather than individual foods – compare."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In the chart we see the potential carbon reductions that we could achieve through dietary changes across the world. This measures – in brown – the annual reduction in global greenhouse gas emissions from food if everyone in the world adopted a given diet. In green we see the amount of carbon that could additionally be sequestered in restored vegetation and soils."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This sequestration in restored vegetation represents how much carbon is stored as forests, grasslands and other landscapes grow back. Obviously these plants won’t keep growing forever. Eventually regrowth will level off, but it will take many decades to get there. In the next section we will look at the maximum amount of carbon that could be stored once vegetation stops regrowing."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Our food system is currently responsible for 13.7 billion tonnes of carbon dioxide equivalents (GtCO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": ""e) each year. That’s "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/food-ghg-emissions"", ""children"": [{""text"": ""one-quarter (26%) of total"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" greenhouse gas emissions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If people decided to cut out beef and lamb, we would reduce emissions by 2.6 GtCO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": ""e per year (a 20% reduction), and save an additional 4.5 GtCO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": ""e by restoring vegetation on abandoned farmland. If we also cut out dairy we could save 12.3 GtCO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": ""e each year – almost as much as global food emissions today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What’s interesting is that most of the carbon reductions come from cutting out beef and dairy. This means eating chicken, pork, fish or plant-based substitutes is the most effective way to reduce the land use and carbon impact of your diet."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If everyone would decide to become vegan we would achieve the largest carbon reduction. We could halve annual emissions from food production. And, as we saw in a "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://ourworldindata.org/land-use-diets"", ""children"": [{""text"": ""related article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""text"": "", switching to a vegan diet would reduce our agricultural land use by 75%. This means we could sequester an additional 8 billion tonnes of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" in vegetation and soils each year. Combined, this would reduce greenhouse gases by 14.7 billion tonnes of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": ""e each year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Thankfully there is no trade-off between production emissions and opportunity costs: what reduces emissions the most also results in the greatest reduction in opportunity costs. Shifting to a more plant-based diet achieves both."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Carbon-savings-of-diets-Poore-and-Stehfest-–-bar-chart.png"", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""How much carbon dioxide could the regrowth of trees and wilderness store if we would reduce the consumption of meat and dairy?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""So far we’ve looked at the potential carbon costs of individual diets. We’ve calculated this in terms of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""annual"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" carbon reductions – the amount that’s absorbed as vegetation is regrowing. But there is a limit to how much carbon we can possibly store in the world’s vegetation. If we abandoned our farmland, forests, grasslands and other vegetation would regrow over the course of many decades. They’d be sequestering more and more carbon as they go. But eventually this growth will level off: they will continue to store carbon, but not sequester more and more."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To understand how much carbon the world could save, researchers Matthew Hayek, Helen Harwatt, William Ripple and Nathaniel Mueller estimated the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""cumulative"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" carbon opportunity costs of global dietary change."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" They looked at the changes in carbon that could be sequestered if everyone in the world adopted a given diet today, under three scenarios."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" These are shown in the chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Business-as-usual:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""The change of global diets up until 2050 follows a similar trajectory to the past – meat and dairy consumption in lower- and middle-income countries rises as they get richer, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/meat-consumption-vs-gdp-per-capita?stackMode=absolute&endpointsOnly=1&time=earliest..2017"", ""children"": [{""text"": ""just as it did in high-income countries"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This scenario expects "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/crop-yields"", ""children"": [{""text"": ""crop yields"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" to increase, but not enough to keep up with demand and so we’d actually need "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""more"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" agricultural land than we have today. We’d emit additional carbon rather than saving it."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Vegan diet:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" In a hypothetical scenario in which everyone in the world went vegan by 2050, the regrowth of trees and wilderness could sequester around 547 billion tonnes of additional CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "". Each year we emit "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/annual-co-emissions-by-region?tab=chart&stackMode=absolute&time=earliest..latest®ion=World"", ""children"": [{""text"": ""around 36 billion tonnes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" from fossil fuels, so that’s equal to around 15 years of emissions at our current levels."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" They also estimate an additional 225 billion tonnes of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" could be stored in soils, although soil sequestration estimates are more uncertain."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""EAT-Lancet diet: "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""This diet is one in which people decide to reduce meat and dairy consumption but doesn’t cut it out completely."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Relative to the 2050 ‘business-as-usual’ diet, it reduces beef consumption by 80%; lamb by 70%; milk by 27%; pork by 87%; chicken by 49%; and eggs by 52%. This is a diet in which everyone would eat on average 47 grams of beef per week (equivalent to one burger); 19 grams of lamb; one to two rashers of bacon; a few portions of chicken; and one to two eggs. It also includes around 200 grams of dairy (milk, cheese, yoghurt and other dairy products) per day. A shift towards this more plant-based would save 332 billion tonnes of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" – equal to around 9 years’ worth of current fossil fuel emissions."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" They also estimate an additional 135 billion tonnes of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" could be stored in soils."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Carbon-Opportunity-Costs-of-Livestock-Hayek-et-al.png"", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Dietary changes could double our carbon budget for 1.5°C – but it’s no substitute for getting off fossil fuels"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How close could transforming the global food system in these ways take us to the UN climate targets?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Let’s put these numbers in the context of our global carbon budget. Our ‘carbon budget’ is an estimate of how much carbon we can emit from this point forward and still keep global temperature rise below a given threshold."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart we see the potential carbon sequestration from the vegan and EAT-Lancet diet compared to the median carbon budget for keeping temperature rise below 1.5℃ and 2℃."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" We see that if everyone adopted a vegan diet, by 2050 we could increase our carbon budget for 1.5℃ by 125%; we would more than double our budget. If we adopted a reduced meat diet, we’d still increase our budget by 75%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These numbers are large – increasing our carbon budget even by 50% would make a massive difference. But it’s no substitute for getting off fossil fuels. The 547 billion tonnes CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" we could sequester from a vegan diet is equal to 16 years of current fossil fuel emissions. If we don’t change how we "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/worlds-energy-problem"", ""children"": [{""text"": ""produce our energy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", all of this carbon sequestration would only mean that we are in the exact same position 16 years later."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Changing the way we eat will not solve climate change on its own, it would buy us more time to do so."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Carbon-Opportunity-Costs-of-Land-vs-Carbon-Budget.png"", ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""Many thanks to Matthew Hayek and Joseph Poore for providing feedback and comments on this article."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Acknowledgments"", ""parseErrors"": []}, {""text"": [{""text"": ""More of our articles on this topic:"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/land-use-diets"", ""type"": ""prominent-link"", ""title"": ""If the world adopted a plant-based diet we would reduce global agricultural land use from 4 to 1 billion hectares"", ""description"": """", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/less-meat-or-sustainable-meat"", ""type"": ""prominent-link"", ""title"": ""How does the carbon footprint of foods compare across the world?"", ""description"": """", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/food-choice-vs-eating-local"", ""type"": ""prominent-link"", ""title"": ""You want to reduce the carbon footprint of your food? Focus on what you eat, not whether your food is local"", ""description"": """", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""2ea4b7761f5b7e28bd89c71e346c3e1103b77e85"": {""id"": ""2ea4b7761f5b7e28bd89c71e346c3e1103b77e85"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Alexandratos, N. & Bruinsma, J. "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.fao.org/fileadmin/templates/esa/Global_persepctives/world_ag_2030_50_2012_rev.pdf"", ""children"": [{""text"": ""World agriculture towards 2030/2050: the 2012 revision"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". FAO 20, 375 (2012)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""40260b8b30fed792a60874f99a06bb3bf7c63519"": {""id"": ""40260b8b30fed792a60874f99a06bb3bf7c63519"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Hayek, M. N., Harwatt, H., Ripple, W. 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This is shown by the confidence intervals for each."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""73849d375cf27126b5893e23176e7a61bfa890b9"": {""id"": ""73849d375cf27126b5893e23176e7a61bfa890b9"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This we calculate as [547 / 36 = 15.2 years]."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Obviously this scenario would only be desirable if we could still produce enough food for everyone to have a nutritious diet. This scenario takes this into account, and after accounting for consumer waste, provides an average of 2845 kilocalories and 76 grams of protein per person per day. 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"", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://apps.who.int/iris/bitstream/handle/10665/43411/WHO_TRS_935_eng.pdf?ua=1"", ""children"": [{""text"": ""Protein and amino acid requirements in human nutrition"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""url"": ""https://apps.who.int/iris/bitstream/handle/10665/43411/WHO_TRS_935_eng.pdf?ua=1"", ""children"": [{""text"": "" (Vol. 935)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". World Health Organization."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""79cdf4179253658afa2f3cfc74a3aa0318479429"": {""id"": ""79cdf4179253658afa2f3cfc74a3aa0318479429"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Schmidinger, K., & Stehfest, E. (2012). 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"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""The International Journal of Life Cycle Assessment"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", 17(8), 962-972."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""800d5aa68dbf4ae737cbb71f6fdb4dd28fcd95b0"": {""id"": ""800d5aa68dbf4ae737cbb71f6fdb4dd28fcd95b0"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Each year we emit "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/annual-co-emissions-by-region?tab=chart&stackMode=absolute&time=earliest..latest®ion=World"", ""children"": [{""text"": ""around 36 billion tonnes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" from fossil fuels. So we calculate this as [1417 / 36 = 40 years]."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""90a17c9eb52a46fc22ce6489f28fee0794bc8ded"": {""id"": ""90a17c9eb52a46fc22ce6489f28fee0794bc8ded"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""One of the challenges of quantifying these carbon opportunity costs on an annual per capita basis is that it is highly dependent on a couple of factors: the uptake rate of different diets by individuals, and the period over which these carbon savings in vegetation would accumulate. When a forest or grassland is returning, carbon storage takes years (in fact, decades) to accumulate until eventually this additional sequestration saturates. In other words, this additional carbon saving will not continue indefinitely. Later in the article we look at the total maximum carbon sequestration approach which accounts for this time dependence. Nonetheless, in this study by Joseph Poore and Thomas Nemecek (2018), based on the work of Kurt Schmidinger & Elke Stehfest (2012) is based on the savings over a 100-year period."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Poore, J., & Nemecek, T. (2018). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://science.sciencemag.org/content/360/6392/987"", ""children"": [{""text"": ""Reducing food’s environmental impacts through producers and consumers"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Science"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", 360(6392), 987-992."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9a7b41c5f9188aba2287bf22ac6340ef20a5e573"": {""id"": ""9a7b41c5f9188aba2287bf22ac6340ef20a5e573"", ""index"": 12, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Since climate models come with a certain degree of uncertainty – there is a range within which we can predict how the climate will respond – we tend to give probability values to each of these budgets. For example, as of 2020, to have a 50% change of keeping global average temperature rise below 1.5℃, we can emit 440 billion tonnes of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "". To have a 67% chance, we can emit only 227 billion tonnes; and for a 33% chance, this increases to 673 billion tonnes CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "". That’s around 12 years of fossil fuel emissions, if they stay at their current levels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Matthews, H. D., Tokarska, K. B., Rogelj, J., Smith, C. J., MacDougall, A. H., Haustein, K., ... & Knutti, R. (2021). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.nature.com/articles/s43247-020-00064-9"", ""children"": [{""text"": ""An integrated approach to quantifying uncertainties in the remaining carbon budget"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Communications Earth & Environment"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(1), 1-11."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b211bb17a9f7254d31c3242a27b309b78abcbdbc"": {""id"": ""b211bb17a9f7254d31c3242a27b309b78abcbdbc"", ""index"": 9, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This would give us a total of 775 billion tonnes of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" by 2100 – very close to the estimate of 810 billion tonnes we’d get by multiplying the 8.1 GtCO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": ""e carbon sequestration figure from the previous section by 100 years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b43c9e7827c952eccd560288cc87955cce2835d8"": {""id"": ""b43c9e7827c952eccd560288cc87955cce2835d8"", ""index"": 11, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""We emit "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/annual-co-emissions-by-region?tab=chart&stackMode=absolute&time=earliest..latest®ion=World"", ""children"": [{""text"": ""around 36 billion tonnes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" each year from fossil fuels. So we can calculate this as [332 / 36 = 9.2 years]."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b6bad3f64b1e113ada099ce9948fa2a85c51080c"": {""id"": ""b6bad3f64b1e113ada099ce9948fa2a85c51080c"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""We get this figure from the change in actual versus potential carbon stocks from current agricultural land. This amounts to 387 gigatonnes of carbon, which is equal to 1417 gigatonnes of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Erb, K. H., Kastner, T., Plutzar, C., Bais, A. L. S., Carvalhais, N., Fetzel, T., ... & Luyssaert, S. (2018). 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Obviously land potential across the world is very different: pasture tends to store more carbon than cropland; wild grasslands store more than managed pastures; tropical forests store more than temperate forests. They aimed to take all of this into account by quantifying these differences at a high spatial resolution."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""West, P. C., Gibbs, H. K., Monfreda, C., Wagner, J., Barford, C. C., Carpenter, S. R., & Foley, J. A. (2010). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.pnas.org/content/107/46/19645"", ""children"": [{""text"": ""Trading carbon for food: Global comparison of carbon stocks vs. crop yields on agricultural land"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Proceedings of the National Academy of Sciences"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""107"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(46), 19645-19648."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""What are the carbon opportunity costs of our food?"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""What are the carbon opportunity costs of our diet? How much carbon could we store by regrowing forests and wild habitats on existing farmland?"", ""dateline"": ""March 19, 2021"", ""subtitle"": ""What are the carbon opportunity costs of our diet? How much carbon could we store by regrowing forests and wild habitats on existing farmland?"", ""sidebar-toc"": false, ""featured-image"": ""ghg-opportunity-costs-land-01.png""}",1,2024-02-22 11:50:58,2021-03-19 11:00:00,2024-02-22 12:03:05,listed,ALBJ4LuNzfRE0zH1daxQ9XOl_hwX6C5SswvRqbsCEPJRHwqLpuc0H3YNXwb1as6ah6tK83ecn-bKVYkBfSXZvA,," Over the last 10,000 years  agricultural land has expanded into forests, wild grasslands and other ecosystems. The world [lost one-third of its forests](https://ourworldindata.org/world-lost-one-third-forests), and today agricultural land makes up [half of the world’s ice- and desert-free land](https://ourworldindata.org/global-land-for-agriculture). The loss of these forests and other natural vegetation has released a lot of carbon into the atmosphere: we have emitted around 1400 billion tonnes of CO2 over millennia.1 That’s equal to 40 years’ worth of our current emissions from fossil fuels.2 It can be easy to forget about these emissions that happened decades, centuries, or even millennia ago. We tend to only focus on emissions _today_. This undersells the role that our agricultural land use could play in tackling climate change. In this article I’ll explain why this is the case. To understand this we need to consider one of the key concepts in economics: opportunity costs. An opportunity cost is the potential benefit you’re giving up by choosing one option over the other. Every decision you make has an opportunity cost – you could be spending your time or money on something else. Spending time watching television comes at the ‘cost’ of not reading a book or not visiting a friend. Choosing pizza comes at the cost of not having pasta instead. In the standard framework of counting greenhouse gas emissions, opportunity costs are not taken into account. The ‘carbon footprint’ figures usually reported for different foods are based on greenhouse gas _emissions today_: how much nitrous oxide is produced when we add fertilizers; methane released by cows; carbon released when we cut down forest and replace it with crops. Land use is not included unless it _changed_ in the last year. The opportunity costs of land are the possible alternative uses for this land. If we weren’t using it to grow crops or raise livestock, it could be restored to forest or wild grasslands. Restoring these could take at least some of the 1400 billion tonnes of CO2 back out of the atmosphere, and put it back into vegetation. Storing this carbon in vegetation and soils is the opposite of emissions. It’s negative emissions. Since we need to urgently reduce the amount of CO2 in the atmosphere, minimising the amount of land the world needs to feed itself is a possible solution. Of course, CO2 in the atmosphere is not the only metric we care about: there is a complex range of socioeconomic factors (such as the livelihoods of people who work in the farming sector) to consider. What we’re doing here is presenting the scientific understanding of what happens to one of those elements – carbon – across a range of possible futures.  It’s up to society to decide what it should do, given the choices available. Here, we look at the impact our dietary choices could have when we factor in the opportunity costs. If the world gave up meat and dairy completely, how much carbon would we possibly save? Do we need to go vegan to make a big difference? # Vegan, vegetarian, flexitarian: how much carbon could different diets save? Let’s first see how taking opportunity costs into account affect the comparisons of individual food items. In the chart here we see the comparison of the carbon costs of different meat and dairy foods, and high-protein substitutes.3 In brown we see the emissions from food _production_ – this is the metric that almost everyone uses to compare products. This tells us how much greenhouse gases have been emitted to produce the food over the full supply chain – from farm to supermarket. In green we see the _opportunity costs_: this is the amount of carbon that could be stored on the land if we would decide to abandon it and let natural vegetation regrow. As we’d expect, because livestock such as cows and sheep need a lot of land, they have much higher opportunity costs. Producing one kilogram of beef can have total carbon costs at least ten times higher than protein-rich alternatives such as tofu or tempeh. In extreme cases, where beef and lamb are produced at low intensities – such as in Brazil – the opportunity costs of agricultural land are huge. Total carbon costs can be as much as 100 times higher than the alternatives. What this also makes clear is that we can save a lot of carbon by making wiser choices about where to produce our food. Producing food where the yields are high reduces the amount of land we need for agriculture, giving us the opportunity to store carbon in forests and grasslands. ## The carbon opportunity costs of different diets Of course, for proper nutrition and health we need to eat a diverse range of foods. So let’s see how different diets – rather than individual foods – compare.4 In the chart we see the potential carbon reductions that we could achieve through dietary changes across the world. This measures – in brown – the annual reduction in global greenhouse gas emissions from food if everyone in the world adopted a given diet. In green we see the amount of carbon that could additionally be sequestered in restored vegetation and soils.5 This sequestration in restored vegetation represents how much carbon is stored as forests, grasslands and other landscapes grow back. Obviously these plants won’t keep growing forever. Eventually regrowth will level off, but it will take many decades to get there. In the next section we will look at the maximum amount of carbon that could be stored once vegetation stops regrowing. Our food system is currently responsible for 13.7 billion tonnes of carbon dioxide equivalents (GtCO2e) each year. That’s [one-quarter (26%) of total](https://ourworldindata.org/food-ghg-emissions) greenhouse gas emissions. If people decided to cut out beef and lamb, we would reduce emissions by 2.6 GtCO2e per year (a 20% reduction), and save an additional 4.5 GtCO2e by restoring vegetation on abandoned farmland. If we also cut out dairy we could save 12.3 GtCO2e each year – almost as much as global food emissions today. What’s interesting is that most of the carbon reductions come from cutting out beef and dairy. This means eating chicken, pork, fish or plant-based substitutes is the most effective way to reduce the land use and carbon impact of your diet. If everyone would decide to become vegan we would achieve the largest carbon reduction. We could halve annual emissions from food production. And, as we saw in a **[related article](https://ourworldindata.org/land-use-diets)**, switching to a vegan diet would reduce our agricultural land use by 75%. This means we could sequester an additional 8 billion tonnes of CO2 in vegetation and soils each year. Combined, this would reduce greenhouse gases by 14.7 billion tonnes of CO2e each year. Thankfully there is no trade-off between production emissions and opportunity costs: what reduces emissions the most also results in the greatest reduction in opportunity costs. Shifting to a more plant-based diet achieves both. # How much carbon dioxide could the regrowth of trees and wilderness store if we would reduce the consumption of meat and dairy? So far we’ve looked at the potential carbon costs of individual diets. We’ve calculated this in terms of _annual_ carbon reductions – the amount that’s absorbed as vegetation is regrowing. But there is a limit to how much carbon we can possibly store in the world’s vegetation. If we abandoned our farmland, forests, grasslands and other vegetation would regrow over the course of many decades. They’d be sequestering more and more carbon as they go. But eventually this growth will level off: they will continue to store carbon, but not sequester more and more. To understand how much carbon the world could save, researchers Matthew Hayek, Helen Harwatt, William Ripple and Nathaniel Mueller estimated the _cumulative_ carbon opportunity costs of global dietary change.6 They looked at the changes in carbon that could be sequestered if everyone in the world adopted a given diet today, under three scenarios.7 These are shown in the chart. **Business-as-usual:**** **The change of global diets up until 2050 follows a similar trajectory to the past – meat and dairy consumption in lower- and middle-income countries rises as they get richer, [just as it did in high-income countries](https://ourworldindata.org/grapher/meat-consumption-vs-gdp-per-capita?stackMode=absolute&endpointsOnly=1&time=earliest..2017).8 This scenario expects [crop yields](http://ourworldindata.org/crop-yields) to increase, but not enough to keep up with demand and so we’d actually need _more_ agricultural land than we have today. We’d emit additional carbon rather than saving it. **Vegan diet:** In a hypothetical scenario in which everyone in the world went vegan by 2050, the regrowth of trees and wilderness could sequester around 547 billion tonnes of additional CO2. Each year we emit [around 36 billion tonnes](https://ourworldindata.org/grapher/annual-co-emissions-by-region?tab=chart&stackMode=absolute&time=earliest..latest®ion=World) of CO2 from fossil fuels, so that’s equal to around 15 years of emissions at our current levels.9 They also estimate an additional 225 billion tonnes of CO2 could be stored in soils, although soil sequestration estimates are more uncertain.10 **EAT-Lancet diet: **This diet is one in which people decide to reduce meat and dairy consumption but doesn’t cut it out completely.11 Relative to the 2050 ‘business-as-usual’ diet, it reduces beef consumption by 80%; lamb by 70%; milk by 27%; pork by 87%; chicken by 49%; and eggs by 52%. This is a diet in which everyone would eat on average 47 grams of beef per week (equivalent to one burger); 19 grams of lamb; one to two rashers of bacon; a few portions of chicken; and one to two eggs. It also includes around 200 grams of dairy (milk, cheese, yoghurt and other dairy products) per day. A shift towards this more plant-based would save 332 billion tonnes of CO2 – equal to around 9 years’ worth of current fossil fuel emissions.12 They also estimate an additional 135 billion tonnes of CO2 could be stored in soils. # Dietary changes could double our carbon budget for 1.5°C – but it’s no substitute for getting off fossil fuels How close could transforming the global food system in these ways take us to the UN climate targets? Let’s put these numbers in the context of our global carbon budget. Our ‘carbon budget’ is an estimate of how much carbon we can emit from this point forward and still keep global temperature rise below a given threshold.13 In the chart we see the potential carbon sequestration from the vegan and EAT-Lancet diet compared to the median carbon budget for keeping temperature rise below 1.5℃ and 2℃.14 We see that if everyone adopted a vegan diet, by 2050 we could increase our carbon budget for 1.5℃ by 125%; we would more than double our budget. If we adopted a reduced meat diet, we’d still increase our budget by 75%. These numbers are large – increasing our carbon budget even by 50% would make a massive difference. But it’s no substitute for getting off fossil fuels. The 547 billion tonnes CO2 we could sequester from a vegan diet is equal to 16 years of current fossil fuel emissions. If we don’t change how we [produce our energy](https://ourworldindata.org/worlds-energy-problem), all of this carbon sequestration would only mean that we are in the exact same position 16 years later. Changing the way we eat will not solve climate change on its own, it would buy us more time to do so. --- # More of our articles on this topic: ### If the world adopted a plant-based diet we would reduce global agricultural land use from 4 to 1 billion hectares https://ourworldindata.org/land-use-diets ### How does the carbon footprint of foods compare across the world? https://ourworldindata.org/less-meat-or-sustainable-meat ### You want to reduce the carbon footprint of your food? Focus on what you eat, not whether your food is local https://ourworldindata.org/food-choice-vs-eating-local We get this figure from the change in actual versus potential carbon stocks from current agricultural land. This amounts to 387 gigatonnes of carbon, which is equal to 1417 gigatonnes of CO2. Erb, K. H., Kastner, T., Plutzar, C., Bais, A. L. S., Carvalhais, N., Fetzel, T., ... & Luyssaert, S. (2018). [Unexpectedly large impact of forest management and grazing on global vegetation biomass](https://www.nature.com/articles/nature25138). _Nature_, _553_(7686), 73-76. Each year we emit [around 36 billion tonnes](https://ourworldindata.org/grapher/annual-co-emissions-by-region?tab=chart&stackMode=absolute&time=earliest..latest®ion=World) of CO2 from fossil fuels. So we calculate this as [1417 / 36 = 40 years]. Schmidinger, K., & Stehfest, E. (2012). [Including CO2 implications of land occupation in LCAs—method and example for livestock products](https://link.springer.com/article/10.1007/s11367-012-0434-7). _The International Journal of Life Cycle Assessment_, 17(8), 962-972. All of the dietary scenarios have been designed to meet nutritional requirements. One of the challenges of quantifying these carbon opportunity costs on an annual per capita basis is that it is highly dependent on a couple of factors: the uptake rate of different diets by individuals, and the period over which these carbon savings in vegetation would accumulate. When a forest or grassland is returning, carbon storage takes years (in fact, decades) to accumulate until eventually this additional sequestration saturates. In other words, this additional carbon saving will not continue indefinitely. Later in the article we look at the total maximum carbon sequestration approach which accounts for this time dependence. Nonetheless, in this study by Joseph Poore and Thomas Nemecek (2018), based on the work of Kurt Schmidinger & Elke Stehfest (2012) is based on the savings over a 100-year period. Poore, J., & Nemecek, T. (2018). [Reducing food’s environmental impacts through producers and consumers](https://science.sciencemag.org/content/360/6392/987). _Science_, 360(6392), 987-992. Hayek, M. N., Harwatt, H., Ripple, W. J., & Mueller, N. D. (2020). [The carbon opportunity cost of animal-sourced food production on land](https://www.nature.com/articles/s41893-020-00603-4). _Nature Sustainability_, 1-4. To quantify the opportunity costs, they took the amount of carbon stored in vegetation and soils in existing pastures (used for livestock grazing) and croplands used to grow animal feed today, versus the amount that could be stored if we abandoned this land and let it naturally regenerate (accounting for the changes in cropland that would be required to replace meat and dairy products with plant-based alternatives). Obviously land potential across the world is very different: pasture tends to store more carbon than cropland; wild grasslands store more than managed pastures; tropical forests store more than temperate forests. They aimed to take all of this into account by quantifying these differences at a high spatial resolution. West, P. C., Gibbs, H. K., Monfreda, C., Wagner, J., Barford, C. C., Carpenter, S. R., & Foley, J. A. (2010). [Trading carbon for food: Global comparison of carbon stocks vs. crop yields on agricultural land](https://www.pnas.org/content/107/46/19645). _Proceedings of the National Academy of Sciences_, _107_(46), 19645-19648. Alexandratos, N. & Bruinsma, J. [World agriculture towards 2030/2050: the 2012 revision](http://www.fao.org/fileadmin/templates/esa/Global_persepctives/world_ag_2030_50_2012_rev.pdf). FAO 20, 375 (2012). This we calculate as [547 / 36 = 15.2 years]. Obviously this scenario would only be desirable if we could still produce enough food for everyone to have a nutritious diet. This scenario takes this into account, and after accounting for consumer waste, provides an average of 2845 kilocalories and 76 grams of protein per person per day. This would be more than the global average requirements. The WHO recommends a minimum protein intake of 0.8 grams per day per kilogram of bodyweight. For a person that weighs 60 kilograms, this would equate to 48 grams of protein per day; for a 70kg person this would be 56 grams; and for a 90 kilogram person this would be 72 grams of protein. Averaged over a population, 76 grams of protein would be sufficient for everyone to meet this requirement. World Health Organization, & United Nations University. (2007). _[Protein and amino acid requirements in human nutrition](https://apps.who.int/iris/bitstream/handle/10665/43411/WHO_TRS_935_eng.pdf?ua=1)_[ (Vol. 935)](https://apps.who.int/iris/bitstream/handle/10665/43411/WHO_TRS_935_eng.pdf?ua=1). World Health Organization. This would give us a total of 775 billion tonnes of CO2 by 2100 – very close to the estimate of 810 billion tonnes we’d get by multiplying the 8.1 GtCO2e carbon sequestration figure from the previous section by 100 years. The EAT-Lancet diet was designed by a group of researchers in nutrition, health, sustainability and policy to balance and improve both human and environmental health. Willett, W., Rockström, J., Loken, B., Springmann, M., Lang, T., Vermeulen, S., ... & Murray, C. J. (2019). [Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(18)31788-4/fulltext). _The Lancet_, _393_(10170), 447-492. We emit [around 36 billion tonnes](https://ourworldindata.org/grapher/annual-co-emissions-by-region?tab=chart&stackMode=absolute&time=earliest..latest®ion=World) of CO2 each year from fossil fuels. So we can calculate this as [332 / 36 = 9.2 years]. Since climate models come with a certain degree of uncertainty – there is a range within which we can predict how the climate will respond – we tend to give probability values to each of these budgets. For example, as of 2020, to have a 50% change of keeping global average temperature rise below 1.5℃, we can emit 440 billion tonnes of CO2. To have a 67% chance, we can emit only 227 billion tonnes; and for a 33% chance, this increases to 673 billion tonnes CO2. That’s around 12 years of fossil fuel emissions, if they stay at their current levels. Matthews, H. D., Tokarska, K. B., Rogelj, J., Smith, C. J., MacDougall, A. H., Haustein, K., ... & Knutti, R. (2021). [An integrated approach to quantifying uncertainties in the remaining carbon budget](https://www.nature.com/articles/s43247-020-00064-9). _Communications Earth & Environment_, _2_(1), 1-11. For each carbon budget we also show the remaining budget to have a 33% and 67% chance of keeping temperatures below this value. This is shown by the confidence intervals for each.",What are the carbon opportunity costs of our food? 1vkvQehkmXKV_mS2hFw1COZCGQYxC1aadis1-9WbHN9I,energy-poverty-air-pollution,article,"{""toc"": [{""slug"": ""undefined-indoor-air-pollution-what-s-the-problem"", ""text"": ""Indoor air pollution: what’s the problem?"", ""title"": ""Indoor air pollution: what’s the problem?"", ""isSubheading"": false}, {""slug"": ""undefined-history-our-ancestors-have-been-suffering-from-indoor-air-pollution-since-prehistoric-times"", ""text"": ""History: Our ancestors have been suffering from indoor air pollution since prehistoric times"", ""title"": ""History: Our ancestors have been suffering from indoor air pollution since prehistoric times"", ""isSubheading"": false}, {""slug"": ""undefined-billions-still-live-in-energy-poverty"", ""text"": ""Billions still live in energy poverty"", ""title"": ""Billions still live in energy poverty"", ""isSubheading"": false}, {""slug"": ""undefined-the-reliance-on-wood-as-a-source-of-energy-contributes-to-environmental-destruction"", ""text"": ""The reliance on wood as a source of energy contributes to environmental destruction"", ""title"": ""The reliance on wood as a source of energy contributes to environmental destruction"", ""isSubheading"": false}, {""slug"": ""undefined-how-can-the-world-make-progress-against-energy-poverty-and-indoor-air-pollution"", ""text"": ""How can the world make progress against energy poverty and indoor air pollution?"", ""title"": ""How can the world make progress against energy poverty and indoor air pollution?"", ""isSubheading"": false}, {""slug"": ""undefined-conclusion"", ""text"": ""Conclusion"", ""title"": ""Conclusion"", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""The lack of access to modern energy sources subjects people to a life of poverty. No electricity means no refrigeration of food, no washing machine, and no light at night."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you don’t have artificial light, your day is over at sunset. This is why the students in this photo are out on the street: they had to find a spot under a streetlight to do their homework. It’s a photo that shows both the determination of those who were born into poverty, but also the steep odds that they have to work against."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Energy poverty is so common that you can see it from space. In Sub-Saharan Africa "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-the-population-with-access-to-electricity"", ""children"": [{""text"": ""43% of the population"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" do not have access to electricity. The poorest regions in the world are dark at night, as the satellite image shows."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""The Earth at night – NASA"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""size"": ""narrow"", ""type"": ""image"", ""filename"": ""earth-night.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Students doing their homework under a streetlight in Conakry, Guinea."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""size"": ""narrow"", ""type"": ""image"", ""filename"": ""homework-streetlight.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But to understand one of the world’s biggest problems that comes with energy poverty we need to zoom in to what’s happening within family households around the world. More specifically, we need to take a look in the world’s kitchens. In high-income countries, people use electricity or gas to cook a meal. But 40% of the world do not have access to these clean, modern energy sources for cooking. What do they rely on instead?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The visualization below is the World Health Organization's answer."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The so-called ‘Energy Ladder’ shows the dominant sources of household energy at different levels of income. From very low incomes on the left to high incomes on the right."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The poorest households burn wood and other biomass, like crop waste and dried dung. Those who can afford it cook and heat with charcoal or coal. Burning these solid fuels on open fires or simple stoves fills the room with smoke and toxic chemicals. These traditional energy sources expose those in the household – often women and children – to pollution levels that are "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""far"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" higher than in even the most polluted cities in the world."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""Energy-Ladder.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Indoor air pollution: what’s the problem?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The lack of modern energy comes at a terrible cost to the health of billions of people."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Millions die from diseases that are caused by air pollution within the household. Chronic exposure to pollution in the household leads to pneumonia, COPD (chronic obstructive pulmonary disease), and lung cancer."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It is the leading risk factor of burns,"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" it increases the risk of cataracts,"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" and it impacts the health of babies before they are born and leads to a higher rate of stillbirths."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Global estimates of how many people die from indoor air pollution vary. We need more data on the levels of pollution that people are exposed to; and better research on how this exposure impacts people’s health. The major studies "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""do"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" however all agree that the death toll is extremely high. The IHME estimates that 2.3 million people die from indoor air pollution every year. The WHO estimates the death toll to be substantially higher: 3.8 million annual deaths."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To put this in perspective, the annual death count from HIV/AIDS is about 1 million and homicides sum up to about 400,000 globally."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The impacts of indoor air pollution are not limited to the household. As the air escapes the home, indoor pollution is also one of the most important sources of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""outdoor"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" air pollution, which kills millions more every year. We discuss this in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/outdoor-air-pollution"", ""children"": [{""text"": ""our entry on outdoor air pollution"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""History: Our ancestors have been suffering from indoor air pollution since prehistoric times"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Humanity suffered and died from indoor air pollution for thousands of years. As the name ‘traditional’ fuels implies, these were the sources that our ancestors in premodern days relied on."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The use of fire by humans goes back one and a half million years."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It kept our ancestors warm and protected; it allowed them to hunt and cook. But it also always had the negative side-effect of polluting the air that they breathed. The impact of manmade air pollution is documented in the remains of hunter-gatherers that lived in caves (close to modern-day Tel Aviv) about 400,000 years ago."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The archeological research suggests that it came from the smoke of indoor fires used to roast meat. High levels of air pollution have also been documented in the preserved lung tissue of Egyptian mummies."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Accounts of air pollution – indoors and outdoors – are common in the ancient world. The residents of ancient Rome referred to the periods in which their city was cloaked in thick smoke as "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""gravioris caeli"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" (“heavy heaven”). After leaving Rome the philosopher and statesman Seneca wrote in a letter in the year 61:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""“I expect you’re keen to hear what effect it had on my health, this decision of mine to leave?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Well, no sooner had I left behind the oppressive atmosphere of the city and that reek of smoking cookers which pour out, along with a cloud of ashes, all the poisonous fumes they’ve accumulated in their interiors whenever they’re started up, than I noticed the change in my condition at once."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""You can imagine how much stronger I felt after reaching my vineyards! I fairly waded into my food – talk about animals just turned out on to spring grass! So by now I am quite my old self again."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""That feeling of listlessness, being bodily ill at ease and mentally inefficient, didn’t last. I’m beginning to get down to some whole-hearted work.”"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""Billions still live in energy poverty"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The premodern energy systems that bothered Seneca are a thing of the past for those who live in rich countries today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But as the ‘energy ladder’ suggests, billions in low- and middle-income countries still do not have access to clean fuels. The two charts here show this."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I’m showing two charts here so we can compare what these two different measures of energy poverty tell us about the world. If you compare the data country-by-country you find that the share that has access to electricity is generally much higher than the share that has access to clean cooking fuels. We can use electricity for cooking, so why would having access to electricity not automatically mean that people have access to clean cooking technology?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It tells us that the cutoff for what it means to have ‘access to electricity’ is very low in these international statistics."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-15"", ""children"": [{""children"": [{""text"": ""15"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Having access to electricity means that a household can use it for basic purposes – such as some light at night or for charging a mobile phone – but might not be able to afford electricity for energy intensive purposes, such as cooking. A family that is able to charge their mobile phones often still relies on cheaper fuels, especially wood, for cooking."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The same was true in today’s richest countries in the past. In pre-war London, 65% of households had access to electricity, but only 11% used it for cooking; the majority still relied on wood and coal."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-16"", ""children"": [{""children"": [{""text"": ""16"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Globally 40% do not have access to clean fuels for cooking. Four out of ten people – that’s "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""3 billion people "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""– do not have access to clean, modern energy for cooking today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/access-to-clean-fuels-for-cooking-vs-gdp-per-capita"", ""type"": ""chart"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/access-to-electricity-vs-gdp-per-capita"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The reliance on wood as a source of energy contributes to environmental destruction"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The use of wood as a source of energy also has a large environmental impact."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Globally about "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""half"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of all wood extracted from forests is used to produce energy, mostly for cooking and heating."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-17"", ""children"": [{""children"": [{""text"": ""17"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" On the African continent the reliance on wood as fuel is the single most important driver of forest degradation."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-18"", ""children"": [{""children"": [{""text"": ""18"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In addition to the destruction of the natural environment, the reliance on fuelwood also contributes between 2 and 7% of global greenhouse gas emissions."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-19"", ""children"": [{""children"": [{""text"": ""19"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The fact that poor people have to rely on wood as a source of energy is one of the key reasons that deforestation is so rapid in poor countries – and why, on the other hand, forests in richer countries tend to "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/deforestation"", ""children"": [{""text"": ""expand in size"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-20"", ""children"": [{""children"": [{""text"": ""20"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The modernization of the energy system – the transition to "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/safest-sources-of-energy"", ""children"": [{""text"": ""safe, low-carbon sources"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – is not only key to improving the health of billions of people in the world, but also to protecting the environment around us."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How can the world make progress against energy poverty and indoor air pollution?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Indoor air pollution is a global problem that is very much solvable. The benefits are especially large for women, who not only suffer the largest health consequences but are also mostly responsible for collecting and carrying the wood and biomass to their homes."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-21"", ""children"": [{""children"": [{""text"": ""21"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world is solving this problem. We see this in the chart. Strong economic growth made people around the world richer, and the death rate from indoor air pollution declined."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Globally the death toll from indoor air pollution has declined by 40% since 1990."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-22"", ""children"": [{""children"": [{""text"": ""22"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Yet it’s still a massive problem. The map next to it makes this clear. In many countries this very solvable problem is still responsible for over 5% of all deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-deaths-indoor-pollution"", ""type"": ""chart"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/death-rates-from-indoor-air-pollution-vs-gdp-per-capita?zoomToSelection=true&minPopulationFilter=1000000&endpointsOnly=1&time=1990..2017&country=CHN~IND~MEX~IDN~PHL~BGD~PAK~BRA~ETH~EGY~MOZ~GIN~CIV~NAM~GTM~PRT~EST~CRI~UZB~KEN~NPL~RWA~SEN~SDN~NIC~MAR~BWA"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is one of the many reasons why growth and electrification are so important for people’s wellbeing and health."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But economic growth is often slow and with 3 billion people in energy poverty it is still a "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""very"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" long way to go. Based on past trends, the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""International Energy Agency"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" expects that by 2030 there will still be 2.4 billion people without access to clean cooking facilities."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-23"", ""children"": [{""children"": [{""text"": ""23"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Is there anything that can be done in the meantime to improve this?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Yes, even at lower incomes it is possible to move away from the most polluting fuel sources."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-24"", ""children"": [{""children"": [{""text"": ""24"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" China has focused on replacing the coal cookstoves that many relied on until recently and has achieved "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/death-rates-from-indoor-air-pollution-vs-gdp-per-capita?zoomToSelection=true&minPopulationFilter=1000000&time=1990..2017&country=~CHN"", ""children"": [{""text"": ""dramatic reductions"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in household air pollution. India "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/death-rates-from-indoor-air-pollution-vs-gdp-per-capita?zoomToSelection=true&minPopulationFilter=1000000&time=1990..2017&country=~IND"", ""children"": [{""text"": ""achieved progress"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" by expanding access to cleaner fuels – especially liquefied petroleum gas."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For many who live in places where modern fuels are not yet available, so-called ‘improved cook stoves’ can be an interim step towards clean cooking. Good stoves burn the fuel more efficiently and are therefore both more environmentally friendly and keep the air in the household cleaner."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Berkouwer and Dean (2019) studied the use of such stoves in Kenya in a randomized control trial to understand how to increase their adoption."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-25"", ""children"": [{""children"": [{""text"": ""25"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The research documented that these stoves save households significant sums of money. They reduced fuel costs by $120 per year, equivalent to around one month of income for the average household. As the stove's market price is only $40 this implies a rate of return of 300% per year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Poor households are aware of the benefits of these stoves. The problem, the researchers found, is that they cannot afford the upfront cost of $40 to purchase them. This makes a strong case for subsidizing this technology."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On the basis of this study, the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Our World in Data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" team decided to offset our carbon emissions by subsidizing these cooking stoves. We cause greenhouse gas emissions from travel and to power the servers that keep the website running. A key argument for our decision to offset our emissions in this way was that these stoves have additional benefits beyond the emissions reduction. They reduce deforestation and biodiversity losses, provide economic benefits to the households, and reduce indoor air pollution."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Conclusion"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Air pollution in the household is a problem that goes back hundreds of thousands of years. Millions of people lost their lives to it over the course of history."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Energy poverty is still the reality for around 3 billion people in the world today. What’s different from the past is that it’s now a solvable problem. What was unavoidable for the ancient Romans or the hunter-gatherers is entirely avoidable today. The millions that die today do not have to, if we find ways to end energy poverty and provide clean modern energy to everyone."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Children who want to do their homework after sunset do not have to sit under street lamps at night."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Key for making progress against energy poverty in the coming years are "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/what-is-economic-growth"", ""children"": [{""text"": ""economic growth"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", increased production of clean energy, and electrification. Access to energy and clean cooking technologies would mean large benefits for environmental protection, gender equality, and for health."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Indoor air pollution is a problem as old as humanity itself. It is now possible to end it within our lifetimes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you want to follow us and support improved cook stoves, you can do so here: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://burnstoves.com/carbon-credits"", ""children"": [{""text"": ""burnstoves.com/carbon-credits"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Acknowledgements"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I would like to thank Hannah Ritchie for reading drafts of this text and for her very helpful comments and ideas."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""0c06f2e9ffd04bf2651588589c4456cdedabd862"": {""id"": ""0c06f2e9ffd04bf2651588589c4456cdedabd862"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For more information on the relevant research see "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/indoor-air-pollution#how-are-deaths-caused-by-pollution-estimated"", ""children"": [{""text"": ""the final section of our entry on indoor air pollution"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And for an overview of the health consequences of indoor air pollution see Bruce, N., Perez-Padilla, R., & Albalak, R. (2000) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.who.int/bulletin/archives/78(9)1078.pdf"", ""children"": [{""text"": ""Indoor air pollution in developing countries: a major environmental and public health challenge"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". In "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Bulletin of the World Health Organization"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", 78(9), 1078–1092."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""209c626a4dd612b5791a2fff2850b3b812f552d8"": {""id"": ""209c626a4dd612b5791a2fff2850b3b812f552d8"", ""index"": 15, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Fouquet, R. (2011) – Long run trends in energy-related external costs. Ecological Economics, 70(12), 2380–2389. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.sciencedirect.com/science/article/pii/S0301421512003734"", ""children"": [{""text"": ""https://www.sciencedirect.com/science/article/pii/S0301421512003734"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This practice is called ‘fuel stacking’."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are many reasons for that, a large one is that cooking is very energy-intensive and that poor households do have access to sufficient electrical energy to rely on it for such energy-intensive uses. There can also be other practical considerations, for example that a modern stove cannot accommodate a pot that is large enough to cook for the whole family. Or that people prefer the taste of food cooked on a wood or a charcoal stove."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""24acab0e58b432c08fcf179aecadb9d5ca0f5f88"": {""id"": ""24acab0e58b432c08fcf179aecadb9d5ca0f5f88"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""These are the latest estimates at the time of writing in June 2021. In 2018 (the latest available WHO publication) the WHO estimated 3.8 million deaths. WHO (2018) –"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health"", ""children"": [{""text"": "" Fact Sheet - Household air pollution and health"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". This data refers to 2016 (as "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.who.int/gho/phe/indoor_air_pollution/en/"", ""children"": [{""text"": ""indicated here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "")."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The results of the Global Burden of Disease study by the IHME can be found in their scientific publications (usually published in The Lancet) and the specific results on the impact of air pollution can also be found in the annual report ‘State of Global Air’. This report is published jointly by the IHME and the Health Effects Institute.Health Effects Institute (2020) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.stateofglobalair.org/"", ""children"": [{""text"": ""State of Global Air 2020"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Special Report. Boston, MA."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""1990: 4,358,000 deaths due to household air pollution from solid fuels"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""2019: 2,314,000 deaths due to household air pollution from solid fuels"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Because the IHME data is more recent and updated annually we rely mostly on IHME data in our work on indoor air pollution."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We are unfortunately not aware of any detailed explanation for the large discrepancy between these two estimates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""344bb235302fdc1a768c7c776a00a11ed8af489d"": {""id"": ""344bb235302fdc1a768c7c776a00a11ed8af489d"", ""index"": 17, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""FAO and UNEP. 2020. The State of the World’s Forests 2020. Forests, biodiversity and people. Rome. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.4060/ca8642en"", ""children"": [{""text"": ""https://doi.org/10.4060/ca8642en"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The same report also reports that an estimated 880 million people worldwide are collecting fuelwood or producing charcoal with it"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""4d1360fa8304c05e3f034d2c7dbe1cc8ebcfaa68"": {""id"": ""4d1360fa8304c05e3f034d2c7dbe1cc8ebcfaa68"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This visualization is based on the Energy Ladder presented in WHO (2006) – Fuel for life: household energy and health. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""http://apps.who.int/iris/handle/10665/43421"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Clean fuels are those that do not cause harmful levels of emissions within the household. Among those fuels that are considered in the ‘Energy Ladder’ these are all fuels except the solid fuels and biomass."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On clean fuels see:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""WHO, IEA, GACC, UNDP and World Bank (2018) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://sustainabledevelopment.un.org/content/documents/17465PB_2_Draft.pdf"", ""children"": [{""text"": ""Achieving Universal Access to Clean and Modern Cooking Fuels and Technologies"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://www.iea.org/articles/defining-energy-access-2020-methodology"", ""children"": [{""text"": ""WHO (2014) – WHO indoor air quality guidelines: household fuel combustion"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""IEA (2020) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.iea.org/articles/defining-energy-access-2020-methodology"", ""children"": [{""text"": ""Defining energy access: 2020 methodology"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It would be possible to add specific technologies (rather than just fuels) to the Energy Ladder, in such an extended version one might consider to include improved biomass cookstoves (ICS) and solar cookers."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""An improved biomass cookstove (ICS) describes a stove with higher efficiency or lower emissions than a traditional stove. The WHO however cautions:  “Most ICS models do not meet WHO Guidelines, but offer some benefits and can be used as transitional solutions.Further innovation, research and investment may indeed produce affordable and widely available biomass stoves that meet the WHO Guidelines levels.” Further below in this text I discuss some of the benefits of high-quality ICS.Solar cookers are not as widely adopted as the fuels considered here and are not included in the Energy Ladder published by the WHO."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""5034cbf0eaa3daa136d8e716e5cf6b1c6329fccd"": {""id"": ""5034cbf0eaa3daa136d8e716e5cf6b1c6329fccd"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""On the impact of indoor air pollution on "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""adverse pregnancy outcomes (including stillbirth)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" see Pope DP, Mishra V, Thompson L, Siddiqui AR, Rehfuess EA, Weber Met al. (2010) – Risk of low birth weight and stillbirth associated with indoor air pollution from solid fuel use in developing countries. Epidemiol Rev 32:70-8120378629."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""50508b368b7df063552d4bc2d66f9d24c8fda96f"": {""id"": ""50508b368b7df063552d4bc2d66f9d24c8fda96f"", ""index"": 10, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For an overview see Gowlett, J. a. J. (2016) – The discovery of fire by humans: A long and convoluted process. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Philosophical Transactions of the Royal Society B: Biological Sciences"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""371"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(1696), 20150164."", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1098/rstb.2015.0164"", ""children"": [{""text"": "" https://doi.org/10.1098/rstb.2015.0164"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""6b5c83f3ac8052b2082f4e4321f97c7342a1d0ce"": {""id"": ""6b5c83f3ac8052b2082f4e4321f97c7342a1d0ce"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""WHO (2018) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health"", ""children"": [{""text"": ""Fact Sheet - Household air pollution and health"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Junfeng (Jim) Zhang, Kirk R Smith (2003) – Indoor air pollution: a global health concern, British Medical Bulletin, Volume 68, Issue 1, December 2003, Pages 209–225, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1093/bmb/ldg029"", ""children"": [{""text"": ""https://doi.org/10.1093/bmb/ldg029"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The pollutants encompass a wide range of different compounds, the most important ones being fine particulate matter (PM2.5), black carbon, and carbon monoxide."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""WHO 2006) reports: "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""“Burning solid fuels produces extremely high levels of indoor air pollution: typical 24-hour levels of PM10 in biomass-using homes in Africa, Asia or Latin America range from 300 to 3000 micrograms per cubic metre (µg/m3). Peaks during cooking may be as high as 10 000 µg/m3. By comparison, the United States Environmental Protection Agency has set the standard for annual mean PM10 levels in outdoor air at 50 µg/m3; the annual mean PM10 limit agreed by the European Union is 40 µg/m3.” –– "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""WHO (2006) – Fuel for life: household energy and health. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""http://apps.who.int/iris/handle/10665/43421"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Delhi is one of the cities with the worst air quality in recent years. Wikipedia has an overview of measurements on their site "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/Air_pollution_in_Delhi"", ""children"": [{""text"": ""Air pollution in Delhi"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and daily data on the air quality in Delhi can be found "", ""spanType"": ""span-simple-text""}, {""url"": ""https://aqicn.org/city/delhi/"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For a comparison of indoor air pollution levels from different energy sources see Shupler, M., Hystad, P., Birch, A., Miller-Lionberg, D., Jeronimo, M., Arku, R. E., Chu, Y. L., Mushtaha, M., Heenan, L., Rangarajan, S., Seron, P., Lanas, F., Cazor, F., Lopez-Jaramillo, P., Camacho, P. A., Perez, M., Yeates, K., West, N., Ncube, T., … Brauer, M. (2020) – Household and personal air pollution exposure measurements from 120 communities in eight countries: Results from the PURE-AIR study. In "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""The Lancet Planetary Health"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(10), e451–e462."", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/S2542-5196(20)30197-2"", ""children"": [{""text"": "" https://doi.org/10.1016/S2542-5196(20)30197-2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""6e21b4b0d4715a0412beff3584dda1802906a151"": {""id"": ""6e21b4b0d4715a0412beff3584dda1802906a151"", ""index"": 14, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.energyforgrowth.org/report/modern-energy-minimum/"", ""children"": [{""text"": "" Raising Global Energy Ambitions: The 1,000 kWh Modern Energy Minimum"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and IEA (2020) –"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.iea.org/articles/defining-energy-access-2020-methodology"", ""children"": [{""text"": "" Defining energy access: 2020 methodology,"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" IEA, Paris."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""717c0bf0f4c15ce1b17d3fc96ce82231e8edebcb"": {""id"": ""717c0bf0f4c15ce1b17d3fc96ce82231e8edebcb"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This is a composite image of Europe, Africa, and the Middle East assembled from data acquired by the Suomi NPP satellite in April and October 2012. It was"", ""spanType"": ""span-simple-text""}, {""url"": ""https://earthobservatory.nasa.gov/images/79793/city-lights-of-africa-europe-and-the-middle-east"", ""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""url"": ""https://earthobservatory.nasa.gov/images/79793/city-lights-of-africa-europe-and-the-middle-east"", ""children"": [{""text"": ""published by NASA"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The earth at night – from all angles – can be explored via"", ""spanType"": ""span-simple-text""}, {""url"": ""https://earth.google.com/web/@3.34855185,23.23626372,-1364.21907436a,13565199.94435787d,35y,0h,0t,0r/data=CiQSIhIgMGY3ZTJkYzdlOGExMTFlNjk5MGQ2ZjgxOGQ2OWE2ZTc"", ""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""url"": ""https://earth.google.com/web/@3.34855185,23.23626372,-1364.21907436a,13565199.94435787d,35y,0h,0t,0r/data=CiQSIhIgMGY3ZTJkYzdlOGExMTFlNjk5MGQ2ZjgxOGQ2OWE2ZTc"", ""children"": [{""text"": ""NASA’s Black Marble Project in Google Earth"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (as of 2021 Google Earth is showing images of the earth at night in 2016."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""720f14178a77a6769ea2f70d6e1686347f57860b"": {""id"": ""720f14178a77a6769ea2f70d6e1686347f57860b"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Cooking on an open fire or with simple, poorly designed stoves is a leading cause of burns, especially among women and children in poorer countries. See World Health Organization (2018) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.who.int/mediacentre/factsheets/fs365/en/"", ""children"": [{""text"": ""Fact sheets: Burns"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7258b55638a353e02b77ed5bc2f553741c10e705"": {""id"": ""7258b55638a353e02b77ed5bc2f553741c10e705"", ""index"": 16, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""FAO (2017) – "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.fao.org/3/i6934e/i6934e.pdf"", ""children"": [{""text"": ""The Charcoal Transition"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". In "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.fao.org/sustainable-forest-management/toolbox/modules/wood-energy/basic-knowledge/en/?type=111"", ""children"": [{""text"": ""FAO – Wood Energy - Basic Knowledge"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" the authors write “The annual removal of wood worldwide was estimated at about 3.7 billion m3, of which 1.87 billion m3 was used as fuel."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""789c84898e5f3bc8539b24052ce6f4d4e2b15e8e"": {""id"": ""789c84898e5f3bc8539b24052ce6f4d4e2b15e8e"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Indoor air pollution increases the risk of cataracts. See: Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H et al. (2012) – A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380:2224-226023245609."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7a4d161f286b22c6361edccf40cc10526fc49ef5"": {""id"": ""7a4d161f286b22c6361edccf40cc10526fc49ef5"", ""index"": 13, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Seneca – Letters from a Stoic: Epistulae Morales Ad Lucilium. In Letter CIV. Partly online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://books.google.co.uk/books?id=hWlZTVgLmAkC&pg=PT172&lpg=PT172&dq#v=onepage&q&f=false"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7db6a1ba84d61fe00d86a8f9880268115cbeddf2"": {""id"": ""7db6a1ba84d61fe00d86a8f9880268115cbeddf2"", ""index"": 12, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Owen Jarus – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.livescience.com/14420-ancient-egyptian-mummies-lung-disease-pollution.html"", ""children"": [{""text"": ""Egyptian Mummies Hold Clues of Ancient Air Pollution"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". In LiveScience."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9e3474deb0f21b005c9d4b716c1ce64158c6873d"": {""id"": ""9e3474deb0f21b005c9d4b716c1ce64158c6873d"", ""index"": 18, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""According to the 2017 FAO publication "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.fao.org/3/i6934e/i6934e.pdf"", ""children"": [{""text"": ""The Charcoal Transition"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" the use of firewood and charcoal contributes between 1-2.4 gigatons of CO2-equivalent greenhouse gases annually, which is 2-7% of global anthropogenic emissions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a57c83e849bd8f0c4b5f39289fae5c50a4fc61a2"": {""id"": ""a57c83e849bd8f0c4b5f39289fae5c50a4fc61a2"", ""index"": 24, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Berkouwer, S. B., & Dean, J. T. (2019). Credit and attention in the adoption of profitable energy efficient technologies in Kenya. UC Berkeley CEGA Working Paper. Available at "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.susannaberkouwer.com/research.html"", ""children"": [{""text"": ""http://www.susannaberkouwer.com/research.html"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A short post about this study by one of the authors is published "", ""spanType"": ""span-simple-text""}, {""url"": ""https://blogs.worldbank.org/impactevaluations/what-causes-under-adoption-profitable-energy-efficient-technologies-kenya-guest"", ""children"": [{""text"": ""on the World Bank blog"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a6d6805cbe1f34d7c0eb2232c4972bf8d57a70ed"": {""id"": ""a6d6805cbe1f34d7c0eb2232c4972bf8d57a70ed"", ""index"": 20, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""WHO (2018) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health"", ""children"": [{""text"": ""Fact Sheet - Household air pollution and health"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Dasgupta, Susmita; Huq, Mainul; Khaliquzzaman, M.; Pandey, Kiran; Wheeler, David (2004) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://openknowledge.worldbank.org/handle/10986/14229"", ""children"": [{""text"": ""Who Suffers from Indoor Air Pollution? Evidence from Bangladesh"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Policy Research Working Paper; No.3428. World Bank."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Shupler et al (2020) on the other hand, report only small differences in the exposure between men and women.Shupler, M., Hystad, P., Birch, A., Miller-Lionberg, D., Jeronimo, M., Arku, R. E., Chu, Y. L., Mushtaha, M., Heenan, L., Rangarajan, S., Seron, P., Lanas, F., Cazor, F., Lopez-Jaramillo, P., Camacho, P. A., Perez, M., Yeates, K., West, N., Ncube, T., … Brauer, M. (2020) – Household and personal air pollution exposure measurements from 120 communities in eight countries: Results from the PURE-AIR study. In "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""The Lancet Planetary Health"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(10), e451–e462."", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/S2542-5196(20)30197-2"", ""children"": [{""text"": "" https://doi.org/10.1016/S2542-5196(20)30197-2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a9260c1588ec9b159d5491b1abd049d7d504a709"": {""id"": ""a9260c1588ec9b159d5491b1abd049d7d504a709"", ""index"": 19, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""On this see the linked work on Our World in Data on deforestation and also Rufus D Edwards, Smith KR, Zhang J, Ma Y. (2004) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.cleancookingalliance.org/binary-data/RESOURCE/file/000/000/27-1.pdf"", ""children"": [{""text"": ""Implications of changes in household stoves and fuel use in China"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". In Energy Policy 32:395-411."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ae0128469c0b036408fe0c31daab75d73b57a075"": {""id"": ""ae0128469c0b036408fe0c31daab75d73b57a075"", ""index"": 23, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See Health Effects Institute (2020) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.stateofglobalair.org/"", ""children"": [{""text"": ""State of Global Air 2020"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Special Report. Boston, MA."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d1b93d31372ba7558bb426fd40547ae5bf232bf5"": {""id"": ""d1b93d31372ba7558bb426fd40547ae5bf232bf5"", ""index"": 22, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This is according to their Stated Policies Scenario, or STEPS, scenario which is taking into account current and announced policies. It is also taking into account a reversal in progress in 2020 and 2021 due to the pandemic."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d660e7a0154fbba484cbc7dfbfffef08067248ec"": {""id"": ""d660e7a0154fbba484cbc7dfbfffef08067248ec"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The photo was taken by Rebecca Blackwell for the Associated Press."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It was published by the New York Times"", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20201109025203/https://thelede.blogs.nytimes.com/2007/07/20/squinting-at-the-future-in-an-airport-parking-lot/"", ""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""url"": ""https://web.archive.org/web/20201109025203/https://thelede.blogs.nytimes.com/2007/07/20/squinting-at-the-future-in-an-airport-parking-lot/"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""eea4a974a111a799499e477c0dfd50a7f5981f14"": {""id"": ""eea4a974a111a799499e477c0dfd50a7f5981f14"", ""index"": 11, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Ran Barkai, Jordi Rosell, Ruth Blasco, and Avi Gopher (2017) – Fire for a Reason: Barbecue at Middle Pleistocene Qesem Cave, Israel. In Current Anthropology; Volume 58, Number S16, August 2017, Fire and the Genus Homo."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""An overview of the team’s research in Qesem cave can be found in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.eurekalert.org/pub_releases/2015-06/afot-4dt061715.php"", ""children"": [{""text"": ""400,000-year-old dental tartar provides earliest evidence of manmade pollution"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in EurekAlert."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ef4de871f73e026e70afc8db4987875f5fa7fdb7"": {""id"": ""ef4de871f73e026e70afc8db4987875f5fa7fdb7"", ""index"": 21, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This is according to the figures from the IHME. In 1990 – just a generation ago – 2.7 million people died from indoor air pollution. Since then the number of deaths has fallen to 1.6 million."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f266de0dda5ebc5dd36a94593c31c6f678ae92e6"": {""id"": ""f266de0dda5ebc5dd36a94593c31c6f678ae92e6"", ""index"": 9, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""These are rounded numbers for 2017 from the IHME. For details see "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/causes-of-death"", ""children"": [{""text"": ""our entry on causes of death"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Energy poverty and indoor air pollution: a problem as old as humanity that we can end within our lifetime"", ""authors"": [""Max Roser""], ""excerpt"": ""About 3 billion people in the world do not have access to modern energy sources for cooking. Millions die from indoor air pollution every year."", ""dateline"": ""July 5, 2021"", ""subtitle"": ""About 3 billion people in the world do not have access to modern energy sources for cooking. Millions die from indoor air pollution every year."", ""featured-image"": ""energy-poverty-pollution-thumbnail.png""}",1,2023-10-10 09:39:37,2021-07-05 09:00:00,2024-03-18 15:41:59,listed,ALBJ4LsJVGCYYa9wzSuKjvt74D21XlRQjS6capExO8Z8FLXjJfr7-Gc12k77En4fMp4EsdRdhApWyxrZthSB1A,,"The lack of access to modern energy sources subjects people to a life of poverty. No electricity means no refrigeration of food, no washing machine, and no light at night. If you don’t have artificial light, your day is over at sunset. This is why the students in this photo are out on the street: they had to find a spot under a streetlight to do their homework. It’s a photo that shows both the determination of those who were born into poverty, but also the steep odds that they have to work against. Energy poverty is so common that you can see it from space. In Sub-Saharan Africa [43% of the population](https://ourworldindata.org/grapher/share-of-the-population-with-access-to-electricity) do not have access to electricity. The poorest regions in the world are dark at night, as the satellite image shows. **The Earth at night – NASA**1 **Students doing their homework under a streetlight in Conakry, Guinea.**2 But to understand one of the world’s biggest problems that comes with energy poverty we need to zoom in to what’s happening within family households around the world. More specifically, we need to take a look in the world’s kitchens. In high-income countries, people use electricity or gas to cook a meal. But 40% of the world do not have access to these clean, modern energy sources for cooking. What do they rely on instead? The visualization below is the World Health Organization's answer.3 The so-called ‘Energy Ladder’ shows the dominant sources of household energy at different levels of income. From very low incomes on the left to high incomes on the right. The poorest households burn wood and other biomass, like crop waste and dried dung. Those who can afford it cook and heat with charcoal or coal. Burning these solid fuels on open fires or simple stoves fills the room with smoke and toxic chemicals. These traditional energy sources expose those in the household – often women and children – to pollution levels that are _far_ higher than in even the most polluted cities in the world.4 ## Indoor air pollution: what’s the problem? The lack of modern energy comes at a terrible cost to the health of billions of people. Millions die from diseases that are caused by air pollution within the household. Chronic exposure to pollution in the household leads to pneumonia, COPD (chronic obstructive pulmonary disease), and lung cancer.5 It is the leading risk factor of burns,6 it increases the risk of cataracts,7 and it impacts the health of babies before they are born and leads to a higher rate of stillbirths.8 Global estimates of how many people die from indoor air pollution vary. We need more data on the levels of pollution that people are exposed to; and better research on how this exposure impacts people’s health. The major studies _do_ however all agree that the death toll is extremely high. The IHME estimates that 2.3 million people die from indoor air pollution every year. The WHO estimates the death toll to be substantially higher: 3.8 million annual deaths.9 To put this in perspective, the annual death count from HIV/AIDS is about 1 million and homicides sum up to about 400,000 globally.10 The impacts of indoor air pollution are not limited to the household. As the air escapes the home, indoor pollution is also one of the most important sources of _outdoor_ air pollution, which kills millions more every year. We discuss this in [our entry on outdoor air pollution](https://ourworldindata.org/outdoor-air-pollution). ## History: Our ancestors have been suffering from indoor air pollution since prehistoric times Humanity suffered and died from indoor air pollution for thousands of years. As the name ‘traditional’ fuels implies, these were the sources that our ancestors in premodern days relied on. The use of fire by humans goes back one and a half million years.11 It kept our ancestors warm and protected; it allowed them to hunt and cook. But it also always had the negative side-effect of polluting the air that they breathed. The impact of manmade air pollution is documented in the remains of hunter-gatherers that lived in caves (close to modern-day Tel Aviv) about 400,000 years ago.12 The archeological research suggests that it came from the smoke of indoor fires used to roast meat. High levels of air pollution have also been documented in the preserved lung tissue of Egyptian mummies.13 Accounts of air pollution – indoors and outdoors – are common in the ancient world. The residents of ancient Rome referred to the periods in which their city was cloaked in thick smoke as _gravioris caeli_ (“heavy heaven”). After leaving Rome the philosopher and statesman Seneca wrote in a letter in the year 61: _“I expect you’re keen to hear what effect it had on my health, this decision of mine to leave?_ _Well, no sooner had I left behind the oppressive atmosphere of the city and that reek of smoking cookers which pour out, along with a cloud of ashes, all the poisonous fumes they’ve accumulated in their interiors whenever they’re started up, than I noticed the change in my condition at once._ _You can imagine how much stronger I felt after reaching my vineyards! I fairly waded into my food – talk about animals just turned out on to spring grass! So by now I am quite my old self again._ _That feeling of listlessness, being bodily ill at ease and mentally inefficient, didn’t last. I’m beginning to get down to some whole-hearted work.”_14 ## Billions still live in energy poverty The premodern energy systems that bothered Seneca are a thing of the past for those who live in rich countries today. But as the ‘energy ladder’ suggests, billions in low- and middle-income countries still do not have access to clean fuels. The two charts here show this. I’m showing two charts here so we can compare what these two different measures of energy poverty tell us about the world. If you compare the data country-by-country you find that the share that has access to electricity is generally much higher than the share that has access to clean cooking fuels. We can use electricity for cooking, so why would having access to electricity not automatically mean that people have access to clean cooking technology? It tells us that the cutoff for what it means to have ‘access to electricity’ is very low in these international statistics.15 Having access to electricity means that a household can use it for basic purposes – such as some light at night or for charging a mobile phone – but might not be able to afford electricity for energy intensive purposes, such as cooking. A family that is able to charge their mobile phones often still relies on cheaper fuels, especially wood, for cooking. The same was true in today’s richest countries in the past. In pre-war London, 65% of households had access to electricity, but only 11% used it for cooking; the majority still relied on wood and coal.16 Globally 40% do not have access to clean fuels for cooking. Four out of ten people – that’s _3 billion people _– do not have access to clean, modern energy for cooking today. ## The reliance on wood as a source of energy contributes to environmental destruction The use of wood as a source of energy also has a large environmental impact. Globally about _half_ of all wood extracted from forests is used to produce energy, mostly for cooking and heating.17 On the African continent the reliance on wood as fuel is the single most important driver of forest degradation.18 In addition to the destruction of the natural environment, the reliance on fuelwood also contributes between 2 and 7% of global greenhouse gas emissions.19 The fact that poor people have to rely on wood as a source of energy is one of the key reasons that deforestation is so rapid in poor countries – and why, on the other hand, forests in richer countries tend to [expand in size](https://ourworldindata.org/deforestation).20 The modernization of the energy system – the transition to [safe, low-carbon sources](https://ourworldindata.org/safest-sources-of-energy) – is not only key to improving the health of billions of people in the world, but also to protecting the environment around us. ## How can the world make progress against energy poverty and indoor air pollution? Indoor air pollution is a global problem that is very much solvable. The benefits are especially large for women, who not only suffer the largest health consequences but are also mostly responsible for collecting and carrying the wood and biomass to their homes.21 The world is solving this problem. We see this in the chart. Strong economic growth made people around the world richer, and the death rate from indoor air pollution declined. Globally the death toll from indoor air pollution has declined by 40% since 1990.22 Yet it’s still a massive problem. The map next to it makes this clear. In many countries this very solvable problem is still responsible for over 5% of all deaths. This is one of the many reasons why growth and electrification are so important for people’s wellbeing and health. But economic growth is often slow and with 3 billion people in energy poverty it is still a _very_ long way to go. Based on past trends, the _International Energy Agency_ expects that by 2030 there will still be 2.4 billion people without access to clean cooking facilities.23 Is there anything that can be done in the meantime to improve this? Yes, even at lower incomes it is possible to move away from the most polluting fuel sources.24 China has focused on replacing the coal cookstoves that many relied on until recently and has achieved [dramatic reductions](https://ourworldindata.org/grapher/death-rates-from-indoor-air-pollution-vs-gdp-per-capita?zoomToSelection=true&minPopulationFilter=1000000&time=1990..2017&country=~CHN) in household air pollution. India [achieved progress](https://ourworldindata.org/grapher/death-rates-from-indoor-air-pollution-vs-gdp-per-capita?zoomToSelection=true&minPopulationFilter=1000000&time=1990..2017&country=~IND) by expanding access to cleaner fuels – especially liquefied petroleum gas. For many who live in places where modern fuels are not yet available, so-called ‘improved cook stoves’ can be an interim step towards clean cooking. Good stoves burn the fuel more efficiently and are therefore both more environmentally friendly and keep the air in the household cleaner. Berkouwer and Dean (2019) studied the use of such stoves in Kenya in a randomized control trial to understand how to increase their adoption.25 The research documented that these stoves save households significant sums of money. They reduced fuel costs by $120 per year, equivalent to around one month of income for the average household. As the stove's market price is only $40 this implies a rate of return of 300% per year. Poor households are aware of the benefits of these stoves. The problem, the researchers found, is that they cannot afford the upfront cost of $40 to purchase them. This makes a strong case for subsidizing this technology. On the basis of this study, the _Our World in Data_ team decided to offset our carbon emissions by subsidizing these cooking stoves. We cause greenhouse gas emissions from travel and to power the servers that keep the website running. A key argument for our decision to offset our emissions in this way was that these stoves have additional benefits beyond the emissions reduction. They reduce deforestation and biodiversity losses, provide economic benefits to the households, and reduce indoor air pollution. ## Conclusion Air pollution in the household is a problem that goes back hundreds of thousands of years. Millions of people lost their lives to it over the course of history. Energy poverty is still the reality for around 3 billion people in the world today. What’s different from the past is that it’s now a solvable problem. What was unavoidable for the ancient Romans or the hunter-gatherers is entirely avoidable today. The millions that die today do not have to, if we find ways to end energy poverty and provide clean modern energy to everyone. Children who want to do their homework after sunset do not have to sit under street lamps at night. Key for making progress against energy poverty in the coming years are [economic growth](https://ourworldindata.org/what-is-economic-growth), increased production of clean energy, and electrification. Access to energy and clean cooking technologies would mean large benefits for environmental protection, gender equality, and for health. Indoor air pollution is a problem as old as humanity itself. It is now possible to end it within our lifetimes. If you want to follow us and support improved cook stoves, you can do so here: [burnstoves.com/carbon-credits](https://burnstoves.com/carbon-credits) --- **Acknowledgements** I would like to thank Hannah Ritchie for reading drafts of this text and for her very helpful comments and ideas. For more information on the relevant research see [the final section of our entry on indoor air pollution](https://ourworldindata.org/indoor-air-pollution#how-are-deaths-caused-by-pollution-estimated) And for an overview of the health consequences of indoor air pollution see Bruce, N., Perez-Padilla, R., & Albalak, R. (2000) – [Indoor air pollution in developing countries: a major environmental and public health challenge](https://www.who.int/bulletin/archives/78(9)1078.pdf). In _Bulletin of the World Health Organization_, 78(9), 1078–1092. Fouquet, R. (2011) – Long run trends in energy-related external costs. Ecological Economics, 70(12), 2380–2389. [https://www.sciencedirect.com/science/article/pii/S0301421512003734](https://www.sciencedirect.com/science/article/pii/S0301421512003734) This practice is called ‘fuel stacking’. There are many reasons for that, a large one is that cooking is very energy-intensive and that poor households do have access to sufficient electrical energy to rely on it for such energy-intensive uses. There can also be other practical considerations, for example that a modern stove cannot accommodate a pot that is large enough to cook for the whole family. Or that people prefer the taste of food cooked on a wood or a charcoal stove. These are the latest estimates at the time of writing in June 2021. In 2018 (the latest available WHO publication) the WHO estimated 3.8 million deaths. WHO (2018) –[ Fact Sheet - Household air pollution and health](https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health). This data refers to 2016 (as [indicated here](https://www.who.int/gho/phe/indoor_air_pollution/en/)). The results of the Global Burden of Disease study by the IHME can be found in their scientific publications (usually published in The Lancet) and the specific results on the impact of air pollution can also be found in the annual report ‘State of Global Air’. This report is published jointly by the IHME and the Health Effects Institute.Health Effects Institute (2020) – [State of Global Air 2020](https://www.stateofglobalair.org/). Special Report. Boston, MA. * 1990: 4,358,000 deaths due to household air pollution from solid fuels * 2019: 2,314,000 deaths due to household air pollution from solid fuels Because the IHME data is more recent and updated annually we rely mostly on IHME data in our work on indoor air pollution. We are unfortunately not aware of any detailed explanation for the large discrepancy between these two estimates. FAO and UNEP. 2020. The State of the World’s Forests 2020. Forests, biodiversity and people. Rome. [https://doi.org/10.4060/ca8642en](https://doi.org/10.4060/ca8642en) The same report also reports that an estimated 880 million people worldwide are collecting fuelwood or producing charcoal with it This visualization is based on the Energy Ladder presented in WHO (2006) – Fuel for life: household energy and health. Online [here](http://apps.who.int/iris/handle/10665/43421). Clean fuels are those that do not cause harmful levels of emissions within the household. Among those fuels that are considered in the ‘Energy Ladder’ these are all fuels except the solid fuels and biomass. On clean fuels see: * WHO, IEA, GACC, UNDP and World Bank (2018) – [Achieving Universal Access to Clean and Modern Cooking Fuels and Technologies](https://sustainabledevelopment.un.org/content/documents/17465PB_2_Draft.pdf) * [WHO (2014) – WHO indoor air quality guidelines: household fuel combustion](https://www.iea.org/articles/defining-energy-access-2020-methodology). * IEA (2020) – [Defining energy access: 2020 methodology](https://www.iea.org/articles/defining-energy-access-2020-methodology). It would be possible to add specific technologies (rather than just fuels) to the Energy Ladder, in such an extended version one might consider to include improved biomass cookstoves (ICS) and solar cookers. An improved biomass cookstove (ICS) describes a stove with higher efficiency or lower emissions than a traditional stove. The WHO however cautions:  “Most ICS models do not meet WHO Guidelines, but offer some benefits and can be used as transitional solutions.Further innovation, research and investment may indeed produce affordable and widely available biomass stoves that meet the WHO Guidelines levels.” Further below in this text I discuss some of the benefits of high-quality ICS.Solar cookers are not as widely adopted as the fuels considered here and are not included in the Energy Ladder published by the WHO. On the impact of indoor air pollution on **adverse pregnancy outcomes (including stillbirth)** see Pope DP, Mishra V, Thompson L, Siddiqui AR, Rehfuess EA, Weber Met al. (2010) – Risk of low birth weight and stillbirth associated with indoor air pollution from solid fuel use in developing countries. Epidemiol Rev 32:70-8120378629. For an overview see Gowlett, J. a. J. (2016) – The discovery of fire by humans: A long and convoluted process. _Philosophical Transactions of the Royal Society B: Biological Sciences_, _371_(1696), 20150164.[ https://doi.org/10.1098/rstb.2015.0164](https://doi.org/10.1098/rstb.2015.0164) WHO (2018) – [Fact Sheet - Household air pollution and health](https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health) Junfeng (Jim) Zhang, Kirk R Smith (2003) – Indoor air pollution: a global health concern, British Medical Bulletin, Volume 68, Issue 1, December 2003, Pages 209–225, [https://doi.org/10.1093/bmb/ldg029](https://doi.org/10.1093/bmb/ldg029) The pollutants encompass a wide range of different compounds, the most important ones being fine particulate matter (PM2.5), black carbon, and carbon monoxide. WHO 2006) reports: _“Burning solid fuels produces extremely high levels of indoor air pollution: typical 24-hour levels of PM10 in biomass-using homes in Africa, Asia or Latin America range from 300 to 3000 micrograms per cubic metre (µg/m3). Peaks during cooking may be as high as 10 000 µg/m3. By comparison, the United States Environmental Protection Agency has set the standard for annual mean PM10 levels in outdoor air at 50 µg/m3; the annual mean PM10 limit agreed by the European Union is 40 µg/m3.” –– _WHO (2006) – Fuel for life: household energy and health. Online [here](http://apps.who.int/iris/handle/10665/43421). Delhi is one of the cities with the worst air quality in recent years. Wikipedia has an overview of measurements on their site [Air pollution in Delhi](https://en.wikipedia.org/wiki/Air_pollution_in_Delhi) and daily data on the air quality in Delhi can be found [here](https://aqicn.org/city/delhi/). For a comparison of indoor air pollution levels from different energy sources see Shupler, M., Hystad, P., Birch, A., Miller-Lionberg, D., Jeronimo, M., Arku, R. E., Chu, Y. L., Mushtaha, M., Heenan, L., Rangarajan, S., Seron, P., Lanas, F., Cazor, F., Lopez-Jaramillo, P., Camacho, P. A., Perez, M., Yeates, K., West, N., Ncube, T., … Brauer, M. (2020) – Household and personal air pollution exposure measurements from 120 communities in eight countries: Results from the PURE-AIR study. In _The Lancet Planetary Health_, _4_(10), e451–e462.[ https://doi.org/10.1016/S2542-5196(20)30197-2](https://doi.org/10.1016/S2542-5196(20)30197-2) See[ Raising Global Energy Ambitions: The 1,000 kWh Modern Energy Minimum](https://www.energyforgrowth.org/report/modern-energy-minimum/) and IEA (2020) –[ Defining energy access: 2020 methodology,](https://www.iea.org/articles/defining-energy-access-2020-methodology) IEA, Paris. This is a composite image of Europe, Africa, and the Middle East assembled from data acquired by the Suomi NPP satellite in April and October 2012. It was[ ](https://earthobservatory.nasa.gov/images/79793/city-lights-of-africa-europe-and-the-middle-east)[published by NASA](https://earthobservatory.nasa.gov/images/79793/city-lights-of-africa-europe-and-the-middle-east). The earth at night – from all angles – can be explored via[ ](https://earth.google.com/web/@3.34855185,23.23626372,-1364.21907436a,13565199.94435787d,35y,0h,0t,0r/data=CiQSIhIgMGY3ZTJkYzdlOGExMTFlNjk5MGQ2ZjgxOGQ2OWE2ZTc)[NASA’s Black Marble Project in Google Earth](https://earth.google.com/web/@3.34855185,23.23626372,-1364.21907436a,13565199.94435787d,35y,0h,0t,0r/data=CiQSIhIgMGY3ZTJkYzdlOGExMTFlNjk5MGQ2ZjgxOGQ2OWE2ZTc) (as of 2021 Google Earth is showing images of the earth at night in 2016. Cooking on an open fire or with simple, poorly designed stoves is a leading cause of burns, especially among women and children in poorer countries. See World Health Organization (2018) – [Fact sheets: Burns](https://www.who.int/mediacentre/factsheets/fs365/en/) FAO (2017) – [The Charcoal Transition](http://www.fao.org/3/i6934e/i6934e.pdf). In [FAO – Wood Energy - Basic Knowledge](http://www.fao.org/sustainable-forest-management/toolbox/modules/wood-energy/basic-knowledge/en/?type=111) the authors write “The annual removal of wood worldwide was estimated at about 3.7 billion m3, of which 1.87 billion m3 was used as fuel. Indoor air pollution increases the risk of cataracts. See: Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H et al. (2012) – A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380:2224-226023245609. Seneca – Letters from a Stoic: Epistulae Morales Ad Lucilium. In Letter CIV. Partly online [here](https://books.google.co.uk/books?id=hWlZTVgLmAkC&pg=PT172&lpg=PT172&dq#v=onepage&q&f=false). Owen Jarus – [Egyptian Mummies Hold Clues of Ancient Air Pollution](https://www.livescience.com/14420-ancient-egyptian-mummies-lung-disease-pollution.html). In LiveScience. According to the 2017 FAO publication [The Charcoal Transition](http://www.fao.org/3/i6934e/i6934e.pdf) the use of firewood and charcoal contributes between 1-2.4 gigatons of CO2-equivalent greenhouse gases annually, which is 2-7% of global anthropogenic emissions. Berkouwer, S. B., & Dean, J. T. (2019). Credit and attention in the adoption of profitable energy efficient technologies in Kenya. UC Berkeley CEGA Working Paper. Available at [http://www.susannaberkouwer.com/research.html](http://www.susannaberkouwer.com/research.html) A short post about this study by one of the authors is published [on the World Bank blog](https://blogs.worldbank.org/impactevaluations/what-causes-under-adoption-profitable-energy-efficient-technologies-kenya-guest). WHO (2018) – [Fact Sheet - Household air pollution and health](https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health) Dasgupta, Susmita; Huq, Mainul; Khaliquzzaman, M.; Pandey, Kiran; Wheeler, David (2004) – [Who Suffers from Indoor Air Pollution? Evidence from Bangladesh](https://openknowledge.worldbank.org/handle/10986/14229). Policy Research Working Paper; No.3428. World Bank. Shupler et al (2020) on the other hand, report only small differences in the exposure between men and women.Shupler, M., Hystad, P., Birch, A., Miller-Lionberg, D., Jeronimo, M., Arku, R. E., Chu, Y. L., Mushtaha, M., Heenan, L., Rangarajan, S., Seron, P., Lanas, F., Cazor, F., Lopez-Jaramillo, P., Camacho, P. A., Perez, M., Yeates, K., West, N., Ncube, T., … Brauer, M. (2020) – Household and personal air pollution exposure measurements from 120 communities in eight countries: Results from the PURE-AIR study. In _The Lancet Planetary Health_, _4_(10), e451–e462.[ https://doi.org/10.1016/S2542-5196(20)30197-2](https://doi.org/10.1016/S2542-5196(20)30197-2) On this see the linked work on Our World in Data on deforestation and also Rufus D Edwards, Smith KR, Zhang J, Ma Y. (2004) – [Implications of changes in household stoves and fuel use in China](https://www.cleancookingalliance.org/binary-data/RESOURCE/file/000/000/27-1.pdf). In Energy Policy 32:395-411. See Health Effects Institute (2020) – [State of Global Air 2020](https://www.stateofglobalair.org/). Special Report. Boston, MA. This is according to their Stated Policies Scenario, or STEPS, scenario which is taking into account current and announced policies. It is also taking into account a reversal in progress in 2020 and 2021 due to the pandemic. The photo was taken by Rebecca Blackwell for the Associated Press. It was published by the New York Times[ ](https://web.archive.org/web/20201109025203/https://thelede.blogs.nytimes.com/2007/07/20/squinting-at-the-future-in-an-airport-parking-lot/)[here](https://web.archive.org/web/20201109025203/https://thelede.blogs.nytimes.com/2007/07/20/squinting-at-the-future-in-an-airport-parking-lot/). Ran Barkai, Jordi Rosell, Ruth Blasco, and Avi Gopher (2017) – Fire for a Reason: Barbecue at Middle Pleistocene Qesem Cave, Israel. In Current Anthropology; Volume 58, Number S16, August 2017, Fire and the Genus Homo. An overview of the team’s research in Qesem cave can be found in [400,000-year-old dental tartar provides earliest evidence of manmade pollution](https://www.eurekalert.org/pub_releases/2015-06/afot-4dt061715.php) in EurekAlert. This is according to the figures from the IHME. In 1990 – just a generation ago – 2.7 million people died from indoor air pollution. Since then the number of deaths has fallen to 1.6 million. These are rounded numbers for 2017 from the IHME. For details see [our entry on causes of death](https://ourworldindata.org/causes-of-death).",Energy poverty and indoor air pollution: a problem as old as humanity that we can end within our lifetime 1veRlw3GvP0J6De85R6_X4cPCLfQhXJp1k-zKkzPwIfo,ozone-layer,topic-page,"{""toc"": [], ""body"": [{""type"": ""topic-page-intro"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The ozone layer plays a vital role in making the planet habitable for us and other species. 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"", ""spanType"": ""span-simple-text""}, {""url"": ""https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017GL074830"", ""children"": [{""text"": ""Decline in Antarctic ozone depletion and lower stratospheric chlorine determined from Aura Microwave Limb Sounder observations"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Geophysical Research Letters, 45(1), 382-390."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Hegglin, M. I. et al. (2015). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.wmo.int/pages/prog/arep/gaw/ozone_2014/documents/2014%20Twenty%20Questions_Final.pdf"", ""children"": [{""text"": ""Twenty Questions and Answers about the Ozone Layer 2014 Update: Scientific Assessment of Ozone Depletion 2014"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". World Meteorological Organisation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""6861fecf6282c89716c60a71bc886ee1899cf761"": {""id"": ""6861fecf6282c89716c60a71bc886ee1899cf761"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Ross J. Salawitch (Lead Author), David W. Fahey, Michaela I. Hegglin, Laura A. McBride, Walter R. Tribett, Sarah J. Doherty, Twenty Questions and Answers About the Ozone Layer: 2018 Update, Scientific Assessment of Ozone Depletion: 2018, 84 pp., World Meteorological Organization, Geneva, Switzerland, 2019."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""topic-page"", ""title"": ""Ozone Layer"", ""authors"": [""Hannah Ritchie"", ""Lucas Rodés-Guirao"", ""Max Roser""], ""excerpt"": ""Humans were emitting large amounts of gases that depleted the ozone layer. But in the 1980s the world came together to tackle the problem. Emissions have fallen by more than 99%."", ""dateline"": ""April 5, 2023"", ""subtitle"": ""Humans were emitting large amounts of gases that depleted the ozone layer. But in the 1980s the world came together to tackle the problem. Emissions have fallen by more than 99%."", ""sticky-nav"": [{""text"": ""Introduction"", ""target"": ""#introduction""}, {""text"": ""Key Insights"", ""target"": ""#key-insights""}, {""text"": ""Research & Writing"", ""target"": ""#research-writing""}, {""text"": ""Charts"", ""target"": ""#all-charts""}, {""text"": ""Endnotes"", ""target"": ""#article-endnotes""}, {""text"": ""Cite This Work"", ""target"": ""#article-citation""}, {""text"": ""Reuse This Work"", ""target"": ""#article-licence""}], ""sidebar-toc"": false, ""featured-image"": ""Ozone-Layer.png""}",1,2024-06-12 14:54:27,2023-04-05 14:55:40,1970-01-01 00:00:00,unlisted,ALBJ4LspmnX_gL0D9vo41Y3_jRYNHmPTM2pGHckLuYVWvuOp_j1oiX2zJ3GjzUDmkixjwL3QEETpqjmxEkBxqA,,,Ozone Layer 1vb0BMBIbn8XvTgQcsiVEkGU8O61hMzbafLfQH8D1G-c,sanitation,article,"{""toc"": [{""slug"": ""unsafe-sanitation-is-a-leading-risk-factor-for-death"", ""text"": ""Unsafe sanitation is a leading risk factor for death"", ""title"": ""Unsafe sanitation is a leading risk factor for death"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""unsafe-sanitation-is-responsible-for-hundreds-of-thousands-of-deaths-each-year"", ""text"": ""Unsafe sanitation is responsible for hundreds of thousands of deaths each year"", ""title"": ""Unsafe sanitation is responsible for hundreds of thousands of deaths each year"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""the-global-distribution-of-deaths-from-sanitation"", ""text"": ""The global distribution of deaths from sanitation"", ""title"": ""The global distribution of deaths from sanitation"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""in-low-income-countries-poor-sanitation-accounts-for-a-larger-share-of-deaths"", ""text"": ""In low-income countries, poor sanitation accounts for a larger share of deaths"", ""title"": ""In low-income countries, poor sanitation accounts for a larger share of deaths"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""death-rates-are-much-higher-in-low-income-countries"", ""text"": ""Death rates are much higher in low-income countries"", ""title"": ""Death rates are much higher in low-income countries"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""usage-of-safe-sanitation"", ""text"": ""Usage of safe sanitation"", ""title"": ""Usage of safe sanitation"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""nearly-half-of-the-world-does-not-use-safely-managed-sanitation"", ""text"": ""Nearly half of the world does not use safely managed sanitation"", ""title"": ""Nearly half of the world does not use safely managed sanitation"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""use-of-improved-sanitation"", ""text"": ""Use of improved sanitation"", ""title"": ""Use of improved sanitation"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-share-of-people-do-not-use-improved-sanitation"", ""text"": ""What share of people do not use improved sanitation?"", ""title"": ""What share of people do not use improved sanitation?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""how-many-people-do-not-use-improved-sanitation"", ""text"": ""How many people do not use improved sanitation?"", ""title"": ""How many people do not use improved sanitation?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""open-defecation"", ""text"": ""Open defecation"", ""title"": ""Open defecation"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-share-of-people-practice-open-defecation"", ""text"": ""What share of people practice open defecation?"", ""title"": ""What share of people practice open defecation?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""open-defecation-is-mainly-a-rural-issue"", ""text"": ""Open defecation is mainly a rural issue"", ""title"": ""Open defecation is mainly a rural issue"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""what-determines-levels-of-sanitation-usage"", ""text"": ""What determines levels of sanitation usage?"", ""title"": ""What determines levels of sanitation usage?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""usage-of-better-sanitation-facilities-increases-with-income"", ""text"": ""Usage of better sanitation facilities increases with income"", ""title"": ""Usage of better sanitation facilities increases with income"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""other-health-impacts-of-poor-sanitation"", ""text"": ""Other health impacts of poor sanitation"", ""title"": ""Other health impacts of poor sanitation"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""stunting-is-higher-where-usage-of-improved-sanitation-is-low"", ""text"": ""Stunting is higher where usage of improved sanitation is low"", ""title"": ""Stunting is higher where usage of improved sanitation is low"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""definitions"", ""text"": ""Definitions"", ""title"": ""Definitions"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""categories-of-sanitation-facilities"", ""text"": ""Categories of sanitation facilities:"", ""title"": ""Categories of sanitation facilities:"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""additional-relevant-definitions"", ""text"": ""Additional relevant definitions:"", ""title"": ""Additional relevant definitions:"", ""supertitle"": """", ""isSubheading"": true}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Having access to and being able to use safe sanitation facilities is one of our most basic human needs."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Nevertheless, due to a range of barriers, such as lack of availability, affordability, or cultural norms, around 40% of the world’s population "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-using-safely-managed-sanitation?tab=chart&time=earliest..latest&country=~OWID_WRL"", ""children"": [{""text"": ""do not use safe sanitation facilities"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". This is a major health risk. Unsafe sanitation is responsible for hundreds of thousands of deaths each year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this article, we give an overview of global and national data on the usage of sanitation facilities and its impact on health outcomes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Unsafe sanitation is a leading risk factor for death"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Unsafe sanitation is responsible for hundreds of thousands of deaths each year"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Unsafe sanitation is one of the world's largest health and environmental problems – particularly for the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/improved-sanitation-facilities-vs-gdp-per-capita"", ""children"": [{""text"": ""poorest in the world"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These estimates of the annual number of deaths attributed to a wide range of risk factors are shown here."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Unsafe sanitation is a leading risk factor for infectious diseases, including cholera, diarrhea, dysentery, hepatitis A, typhoid, and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/polio"", ""children"": [{""text"": ""polio"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It also exacerbates malnutrition and, in particular, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/prevalence-of-stunting-vs-improved-sanitation-facilities"", ""children"": [{""text"": ""childhood stunting"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". In the chart, we see that it ranks as significant risk factor for death globally."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/number-of-deaths-by-risk-factor"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The global distribution of deaths from sanitation"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""In low-income countries, poor sanitation accounts for a larger share of deaths"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the map we see the share of annual deaths attributed to unsafe sanitation across the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The share of deaths attributed to unsafe sanitation is higher in Sub-Saharan Africa and in some areas south and southeastern Asia."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When we compare the share of deaths attributed to unsafe sanitation either over time or between countries, we are not only comparing the extent of unsafe sanitation but its severity "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""in the context"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of other risk factors for death."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Sanitation's share does not only depend on how many die prematurely from it but what else people are dying from and how this is changing."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-deaths-unsafe-sanitation?country=OWID_WRL~Low+Income+%28WB%29~High+Income+%28WB%29~Middle+Income+%28WB%29~Lower+Middle+Income+%28WB%29"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Death rates are much higher in low-income countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Death rates from unsafe sanitation give us an accurate comparison of differences in mortality impacts between countries and over time. In contrast to the share of deaths, death rates are not influenced by how other causes or risk factors for death are changing."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this map, we see death rates from unsafe sanitation across the world. Death rates measure the number of deaths per 100,000 people in a given country or region."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/death-rates-sanitation"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What becomes clear is the large differences in death rates between countries: rates are high in lower-income countries, particularly across Sub-Saharan Africa and Asia."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Rates can be more than 1,000 times higher in many low-income countries than in rich countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see this relationship clearly when we "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/death-rates-from-unsafe-sanitation-vs-gdp-per-capita"", ""children"": [{""text"": ""plot death rates from unsafe sanitation versus income"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", with both indicators on a log scale."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Usage of safe sanitation"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Nearly half of the world does not use safely managed sanitation"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/sdgs/clean-water-sanitation#target-6-2-end-open-defecation-and-provide-access-to-sanitation-and-hygiene"", ""children"": [{""text"": ""SDG Target 6.2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" is to: “achieve access to adequate and equitable sanitation and hygiene for all and end open defecation” by 2030."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In recent years, just over half of the world's population has been using "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/sanitation#definitions"", ""children"": [{""text"": ""safely managed sanitation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". It is shocking that nearly one in two don’t. Many people do not have "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""any"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" sanitation facilities at all and instead have to practice open defecation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world has made progress in recent years. But this has been far too slow. Much of the world’s population still does not use safely managed sanitation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-using-safely-managed-sanitation?tab=chart&time=earliest..latest&country=~OWID_WRL"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the next chart, we see the breakdown of sanitation facilities usage globally and across regions and income groups."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see that in countries with the lowest incomes, only around one in five people use safe sanitation, much like safe drinking water; most live in Sub-Saharan Africa."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-the-population-with-access-to-sanitation-facilities"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the map shown, we see the share of people across the world who use safely managed sanitation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-using-safely-managed-sanitation"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How many people do not use safe sanitation?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the map shown, we see the number of people across the world who "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""do not"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" use safely managed sanitation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/safe-sanitation-without"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Use of improved sanitation"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""What share of people do not use improved sanitation?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""'Improved' sanitation is defined as facilities that ensure hygienic separation of human excreta from human contact. This includes facilities such as flush/pour flush (to piped sewer system, septic tank, pit latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and a composting toilet."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the map shown, we see the share of people across the world who "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""do not"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" use improved sanitation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-without-improved-sanitation"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""How many people do not use improved sanitation?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the map shown, we see the number of people across the world who "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""do not"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" have access to improved sanitation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/number-without-access-to-improved-sanitation"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Open defecation"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""What share of people practice open defecation?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Open defecation refers to the practice of defecation in fields, forests, bushes, bodies of water, or other open spaces."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Open defecation has a number of negative health and social impacts, including the spread of infectious diseases, diarrhea (especially in children), adverse health outcomes in pregnancy, malnutrition, as well as increased vulnerability to violence — particularly for women and girls."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The map shows the share of people practicing open defecation across the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/people-practicing-open-defecation-of-population"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Open defecation is mainly a rural issue"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, we see the prevalence of open defecation in rural areas versus urban areas. For the majority of countries, the share of the population practicing open defecation in urban areas is typically quite low."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For rural populations, however, the share of the population practicing open defecation tends to be higher and varies markedly between countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Although open defecation in urban areas is still a pressing issue in many countries, the problem is much more prevalent in rural areas."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/open-defecation-in-rural-areas-vs-urban-areas"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""What determines levels of sanitation usage?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Usage of better sanitation facilities increases with income"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The provision of better sanitation facilities tends to increase with income. 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The elimination of open defecation and its adverse health effects: a moral imperative for governments and development professionals. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Journal of Water Sanitation and Hygiene for Development"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(1), 1-12. Available "", ""spanType"": ""span-simple-text""}, {""url"": ""http://washdev.iwaponline.com/content/7/1/1"", ""children"": [{""text"": ""online"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""eadb2e15b629359c86e19dfd826d881623c8b9cd"": {""id"": ""eadb2e15b629359c86e19dfd826d881623c8b9cd"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""World Health Organization (2014). WHA Global Nutrition Targets 2025: Stunting Policy Brief. Available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20240114060147/https://www.who.int/publications/i/item/WHO-NMH-NHD-14.3"", ""children"": [{""text"": ""online"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Sanitation"", ""authors"": [""Hannah Ritchie"", ""Fiona Spooner"", ""Max Roser""], ""excerpt"": ""Safe sanitation is essential to reduce deaths from infectious diseases, prevent malnutrition, and provide dignity."", ""dateline"": ""This article was first published in September 2019. It was revised in January 2024."", ""subtitle"": ""Safe sanitation is essential to reduce deaths from infectious diseases, prevent malnutrition, and provide dignity."", ""featured-image"": ""Sanitation-thumbnail.png""}",1,2023-09-29 08:17:03,2019-09-25 16:00:31,2024-01-17 13:18:49,unlisted,ALBJ4LvHK02ew01HevXvcJaCVMjnu_3uh2VxkopDCfybJEV9aOq8AfF4X8okkQ3FBhUvhzlkY703bDCOT4N7dQ,"[{""href"": ""/clean-water-sanitation"", ""label"": ""Clean Water and Sanitation""}, {""label"": ""Sanitation""}]","Having access to and being able to use safe sanitation facilities is one of our most basic human needs. Nevertheless, due to a range of barriers, such as lack of availability, affordability, or cultural norms, around 40% of the world’s population [do not use safe sanitation facilities](https://ourworldindata.org/grapher/share-using-safely-managed-sanitation?tab=chart&time=earliest..latest&country=~OWID_WRL). This is a major health risk. Unsafe sanitation is responsible for hundreds of thousands of deaths each year. In this article, we give an overview of global and national data on the usage of sanitation facilities and its impact on health outcomes. ## Unsafe sanitation is a leading risk factor for death ### Unsafe sanitation is responsible for hundreds of thousands of deaths each year Unsafe sanitation is one of the world's largest health and environmental problems – particularly for the [poorest in the world](https://ourworldindata.org/grapher/improved-sanitation-facilities-vs-gdp-per-capita). These estimates of the annual number of deaths attributed to a wide range of risk factors are shown here. Unsafe sanitation is a leading risk factor for infectious diseases, including cholera, diarrhea, dysentery, hepatitis A, typhoid, and [polio](https://ourworldindata.org/polio).1 It also exacerbates malnutrition and, in particular, [childhood stunting](https://ourworldindata.org/grapher/prevalence-of-stunting-vs-improved-sanitation-facilities). In the chart, we see that it ranks as significant risk factor for death globally. ## The global distribution of deaths from sanitation ### In low-income countries, poor sanitation accounts for a larger share of deaths In the map we see the share of annual deaths attributed to unsafe sanitation across the world. The share of deaths attributed to unsafe sanitation is higher in Sub-Saharan Africa and in some areas south and southeastern Asia. When we compare the share of deaths attributed to unsafe sanitation either over time or between countries, we are not only comparing the extent of unsafe sanitation but its severity _in the context_ of other risk factors for death. Sanitation's share does not only depend on how many die prematurely from it but what else people are dying from and how this is changing. ### Death rates are much higher in low-income countries Death rates from unsafe sanitation give us an accurate comparison of differences in mortality impacts between countries and over time. In contrast to the share of deaths, death rates are not influenced by how other causes or risk factors for death are changing. In this map, we see death rates from unsafe sanitation across the world. Death rates measure the number of deaths per 100,000 people in a given country or region. What becomes clear is the large differences in death rates between countries: rates are high in lower-income countries, particularly across Sub-Saharan Africa and Asia. Rates can be more than 1,000 times higher in many low-income countries than in rich countries. We see this relationship clearly when we [plot death rates from unsafe sanitation versus income](https://ourworldindata.org/grapher/death-rates-from-unsafe-sanitation-vs-gdp-per-capita), with both indicators on a log scale. ## Usage of safe sanitation ### Nearly half of the world does not use safely managed sanitation [SDG Target 6.2](https://ourworldindata.org/sdgs/clean-water-sanitation#target-6-2-end-open-defecation-and-provide-access-to-sanitation-and-hygiene) is to: “achieve access to adequate and equitable sanitation and hygiene for all and end open defecation” by 2030. In recent years, just over half of the world's population has been using [safely managed sanitation](http://ourworldindata.org/sanitation#definitions). It is shocking that nearly one in two don’t. Many people do not have _any_ sanitation facilities at all and instead have to practice open defecation. The world has made progress in recent years. But this has been far too slow. Much of the world’s population still does not use safely managed sanitation. In the next chart, we see the breakdown of sanitation facilities usage globally and across regions and income groups. We see that in countries with the lowest incomes, only around one in five people use safe sanitation, much like safe drinking water; most live in Sub-Saharan Africa. In the map shown, we see the share of people across the world who use safely managed sanitation. How many people do not use safe sanitation? In the map shown, we see the number of people across the world who _do not_ use safely managed sanitation. ## Use of improved sanitation ### What share of people do not use improved sanitation? 'Improved' sanitation is defined as facilities that ensure hygienic separation of human excreta from human contact. This includes facilities such as flush/pour flush (to piped sewer system, septic tank, pit latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and a composting toilet. In the map shown, we see the share of people across the world who _do not_ use improved sanitation. ### How many people do not use improved sanitation? In the map shown, we see the number of people across the world who _do not_ have access to improved sanitation. ## Open defecation ### What share of people practice open defecation? Open defecation refers to the practice of defecation in fields, forests, bushes, bodies of water, or other open spaces. Open defecation has a number of negative health and social impacts, including the spread of infectious diseases, diarrhea (especially in children), adverse health outcomes in pregnancy, malnutrition, as well as increased vulnerability to violence — particularly for women and girls.2 The map shows the share of people practicing open defecation across the world. ### Open defecation is mainly a rural issue In the chart, we see the prevalence of open defecation in rural areas versus urban areas. For the majority of countries, the share of the population practicing open defecation in urban areas is typically quite low. For rural populations, however, the share of the population practicing open defecation tends to be higher and varies markedly between countries. Although open defecation in urban areas is still a pressing issue in many countries, the problem is much more prevalent in rural areas. ## What determines levels of sanitation usage? ### Usage of better sanitation facilities increases with income The provision of better sanitation facilities tends to increase with income. In the chart, we see the share of the population using improved sanitation versus gross domestic product (GDP) per capita. Overall, we see that using improved sanitation increases as countries get richer. ## Other health impacts of poor sanitation ### Stunting is higher where usage of improved sanitation is low Stunting — determined as having a height-for-age more than two standard deviations below the WHO Child Growth Standards median — is a sign of [chronic malnutrition](https://ourworldindata.org/grapher/share-of-children-younger-than-5-who-suffer-from-stunting).3 Although linked to poor nutritional intake (which we cover in our topic page on [hunger and undernourishment](https://ourworldindata.org/hunger-and-undernourishment/)), it is also linked to a range of compounding factors, including the recurrence of infectious diseases, childhood diarrhea, and poor sanitation & hygiene. In the chart, we see the prevalence of stunting in children under five years old versus the share of the population using improved sanitation facilities. Overall, we see a negative correlation: rates of childhood stunting are typically higher in countries with lower usage of improved sanitation facilities. ## Definitions ### Categories of sanitation facilities: **Safely managed sanitation facilities**: Improved sanitation facilities that are not shared with other households and where: * excreta is safely disposed of in situ or * excreta is transported and treated off-site. **Basic service**: Private improved facility which separates excreta from human contact; **Limited service**: Improved facility shared with other households; **Unimproved service**: Unimproved facility that does not separate excreta from human contact; **No service**: open defecation. ### Additional relevant definitions: **Improved sanitation facilities**: Facilities that hygienically separate human excreta from human contact. They include flush/pour flush (to piped sewer system, septic tank, pit latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and composting toilet. Improved sanitation facilities range from simple but protected pit latrines to flush toilets with a sewerage connection. To be effective, facilities must be correctly constructed and properly maintained. 4 Improved sanitation facilities is an umbrella term for the ""safely managed,"" ""basic,"" and ""limited"" services listed above. **Open defecation**: People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forests, bushes, open bodies of water, on beaches, in other open spaces, or disposed of with solid waste. WHO (2023) – Fact sheet – Drinking water. Updated October 2023. Online [here](https://www.who.int/news-room/fact-sheets/detail/sanitation). Mara, D. (2017). The elimination of open defecation and its adverse health effects: a moral imperative for governments and development professionals. _Journal of Water Sanitation and Hygiene for Development_, _7_(1), 1-12. Available [online](http://washdev.iwaponline.com/content/7/1/1). World Health Organization (2014). WHA Global Nutrition Targets 2025: Stunting Policy Brief. Available [online](https://web.archive.org/web/20240114060147/https://www.who.int/publications/i/item/WHO-NMH-NHD-14.3). World Bank & WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation. World Development Indicators Metadata. 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This means that every day of infancy is safer than in the past."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/child-mortality-in-the-past"", ""children"": [{""text"": ""Read more about the decline in child mortality"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" →"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""data-insight"", ""title"": ""Every day of infancy is safer than in the past"", ""authors"": [""Saloni Dattani""], ""approved-by"": ""Ed"", ""grapher-url"": """"}",1,2024-07-10 09:23:34,2024-07-31 05:00:00,2024-07-29 09:35:43,unlisted,ALBJ4LuH61MB6S_8F70qKQMoSoR-sPq2fLnLtj-c-756T-sQhkh6PuVzRdUZg5V8J4yYoiWca9NSi19NjCo6kw,," This chart shows death rates across the first year of a baby’s life and how they have been reduced over time. The data spans 1921 to 2021 and comes from the Office for National Statistics in England & Wales. On the left-hand side of the chart, you can see that death rates are much higher on the first day of life. They then drop sharply over the following days and continue declining gradually over the rest of the year. But you can also see that over decades, the entire curve has shifted downwards. This means that every day of infancy is safer than in the past. [Read more about the decline in child mortality](https://ourworldindata.org/child-mortality-in-the-past) →",Every day of infancy is safer than in the past 1vMsru_zjboUD_W5aBXoMKMMxCxqyJ4oYVCfchYBzDQM,democracies-measurement,article,"{""toc"": [{""slug"": ""how-do-approaches-work-to-make-assessments-valid"", ""text"": ""How do approaches work to make assessments valid?"", ""title"": ""How do approaches work to make assessments valid?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-do-approaches-work-to-make-assessments-precise"", ""text"": ""How do approaches work to make assessments precise?"", ""title"": ""How do approaches work to make assessments precise?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-do-approaches-work-to-make-assessments-comparable"", ""text"": ""How do approaches work to make assessments comparable?"", ""title"": ""How do approaches work to make assessments comparable?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-are-remaining-differences-dealt-with"", ""text"": ""How are remaining differences dealt with?"", ""title"": ""How are remaining differences dealt with?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-do-approaches-work-to-make-data-accessible-and-transparent"", ""text"": ""How do approaches work to make data accessible and transparent?"", ""title"": ""How do approaches work to make data accessible and transparent?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""keep-reading-at-our-world-in-data"", ""text"": ""Keep reading at Our World in Data"", ""title"": ""Keep reading at Our World in Data"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""acknowledgements"", ""text"": ""Acknowledgements"", ""title"": ""Acknowledgements"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""varieties-of-democracy"", ""text"": ""Varieties of Democracy"", ""title"": ""Varieties of Democracy"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""spectrums"", ""text"": ""Spectrums"", ""title"": ""Spectrums"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""regimes-of-the-world"", ""text"": ""Regimes of the World"", ""title"": ""Regimes of the World"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""classification"", ""text"": ""Classification"", ""title"": ""Classification"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""lexical-index-of-electoral-democracy"", ""text"": ""Lexical Index of Electoral Democracy"", ""title"": ""Lexical Index of Electoral Democracy"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""spectrum-and-classification"", ""text"": ""Spectrum and classification"", ""title"": ""Spectrum and classification"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""boix-miller-rosato-bmr"", ""text"": ""Boix-Miller-Rosato (BMR)"", ""title"": ""Boix-Miller-Rosato (BMR)"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""classifications"", ""text"": ""Classifications"", ""title"": ""Classifications"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""polity"", ""text"": ""Polity"", ""title"": ""Polity"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""spectrum-and-classification"", ""text"": ""Spectrum and classification"", ""title"": ""Spectrum and classification"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""freedom-in-the-world"", ""text"": ""Freedom in the World"", ""title"": ""Freedom in the World"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""classification-1"", ""text"": ""Classification 1"", ""title"": ""Classification 1"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""classification-2"", ""text"": ""Classification 2"", ""title"": ""Classification 2"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""bertelsmann-transformation-index-bti"", ""text"": ""Bertelsmann Transformation Index (BTI)"", ""title"": ""Bertelsmann Transformation Index (BTI)"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""spectrum"", ""text"": ""Spectrum"", ""title"": ""Spectrum"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""classification"", ""text"": ""Classification"", ""title"": ""Classification"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""economist-intelligence-unit-eiu"", ""text"": ""Economist Intelligence Unit (EIU)"", ""title"": ""Economist Intelligence Unit (EIU)"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""spectrum"", ""text"": ""Spectrum:"", ""title"": ""Spectrum:"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""classification"", ""text"": ""Classification"", ""title"": ""Classification"", ""supertitle"": """", ""isSubheading"": true}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Measuring the state of democracy across the world helps us understand the extent to which people have political rights and freedoms."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But measuring how democratic a country is, comes with many challenges. People do not always agree on what characteristics define a democracy. These characteristics — such as whether an election was free and fair — even once defined, are difficult to assess. The judgement of experts is to some degree subjective and they may disagree; either about a specific characteristic, or how several characteristics can be reduced into a single measure of democracy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""So how do researchers address these challenges and identify which countries are democratic and undemocratic?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In our work on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/democracy"", ""children"": [{""text"": ""Democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", we provide data from eight leading approaches of measuring democracy:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""url"": ""https://www.v-dem.net/vdemds.html"", ""children"": [{""text"": ""Varieties of Democracy (V-Dem)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" by the V-Dem project"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://www.v-dem.net/vdemds.html"", ""children"": [{""text"": ""Regimes of the World (RoW)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" by Lührmann et al. (2018)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "", which use V-Dem data"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/WPKNIT"", ""children"": [{""text"": ""Lexical Index of Electoral Democracy (LIED)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" by Skaaning et al. (2015)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://sites.google.com/site/mkmtwo/data?authuser=0"", ""children"": [{""text"": ""Boix-Miller-Rosato"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" by Boix et al. (2013, BMR)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://www.systemicpeace.org/inscrdata.html"", ""children"": [{""text"": ""Polity"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" by the Center for Systemic Peace"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Freedom House’s (FH) "", ""spanType"": ""span-simple-text""}, {""url"": ""https://freedomhouse.org/report/freedom-world"", ""children"": [{""text"": ""Freedom in the World"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://bti-project.org/en/downloads"", ""children"": [{""text"": ""Bertelsmann Transformation Index (BTI)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" by the Bertelsmann Foundation"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Economist Intelligence Unit’s (EIU) "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.eiu.com/n/campaigns/democracy-index-2021/?utm_source=eiu-website&utm_medium=blog&utm_campaign=democracy-index-2021"", ""children"": [{""text"": ""Democracy Index"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These approaches all measure democracy (or a closely related aspect), they cover many countries and years, and are commonly used by researchers and policymakers."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can delve into their data — the main democracy measures, indicators of specific characteristics, and global and regional overviews —  in our "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/democracy"", ""children"": [{""text"": ""Democracy Data Explorer"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Reassuringly, the approaches typically agree about big differences in countries’ political institutions: they readily distinguish between highly democratic countries, such as Chile and Norway, and highly undemocratic countries, such as North Korea and Saudi Arabia."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But they do not always agree. They come to different assessments about which of the two highly democratic countries – Chile and Norway – is more democratic, and whether Chile is more or less democratic than it was ten years ago. At times they come to strikingly different conclusions about countries that are neither highly democratic nor highly undemocratic, such as Nigeria today or the United States in the 19th century."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Why do these measures sometimes reach such different conclusions? In this article I summarize the key similarities and differences of these approaches, and discuss when each source is best."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""How is democracy characterized?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this and the following tables I summarize how each approach defines and scores democracy, and what coverage each approach provides."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""html"", ""value"": ""
Varieties of Democracy
  • Narrow and broader: electoral, liberal, participatory, deliberative, or egalitarian democracy
Regimes of the World
  • Narrow: electoral or liberal democracy
Lexical Index
  • Narrow: electoral (or liberal) democracy
Boix-Miller-Rosato
  • Narrow: electoral democracy
Polity
  • \tNarrow: electoral and liberal democracy
Freedom House
  • Narrow: electoral or liberal democracy
Bertelsmann Transformation Index
  • Broad: electoral, liberal, participatory, deliberative, and effective democracy
Economist Intelligence Unit
  • Broad: electoral, liberal, participatory, deliberative, and effective democracy
"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see that the approaches share a basic principle of democracy: a democracy is an electoral political system in which citizens get to participate in free and fair elections. The approaches also mostly agree that democracies are liberal political systems, in which citizens have additional civil rights and are protected from the state by constraining it."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Some approaches stop there, and stick to these narrower conceptions of democracy. Others characterize democracy in broader terms, and also see it as a participatory and deliberative (citizens engage in elections, civil society, and public discourse) as well as an effective (governments can act on citizens’ behalf) political system."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Varieties of Democracy — true to its name — offers both narrow and broader characterizations, by separately adding liberal, participatory, deliberative, as well as egalitarian (economic and social resources are equally distributed) political institutions to electoral democracy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""How is democracy scored?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The approaches also differ in how they "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""score"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" democracy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""html"", ""value"": ""
Varieties of Democracy
  • On a spectrum: 0 to 1, highly undemocratic to highly democratic
Regimes of the World
  • As a classification: closed autocracy < electoral autocracy < electoral democracy < liberal democracy
Lexical Index
  • As a classification: non-electoral autocracy < one-party autocracy < multi-party autocracy without elected executive < multi-party autocracy < exclusive democracy < male democracy < electoral democracy < polyarchy
Boix-Miller-Rosato
  • As a classification: non-democracy < democracy
Polity
  • On a spectrum: -10 to 10, hereditary monarchy to consolidated democracy
  • classification: autocracy < anocracy < democracy
Freedom House
  • As a classification; classification 1: not free < partly free < free
  • classification 2: non-democracy < electoral democracy
Bertelsmann Transformation Index
  • On a spectrum: 1 to 10, highly undemocratic to highly democratic
  • classification: hard-line autocracy < moderate autocracy < very defective democracy < defective democracy < consolidating democracy
Economist Intelligence Unit
  • On a spectrum: 0 to 10, highly undemocratic to 10 highly democratic
  • classification: authoritarian regime < hybrid regime < flawed democracy < full democracy
"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""V-Dem treats democracy as a "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""spectrum"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "", with some countries being scored as more democratic than others."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Other approaches instead treat democracy as a binary, and "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""classify"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" a country as either a democracy or not."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A final group "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""does both"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "", using a spectrum of countries being more or less democratic, and setting thresholds above which a country is considered a democracy overall."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Approaches that classify countries into democracies and non-democracies further differ in whether all countries that are not democracies are considered autocracies or authoritarian regimes, or whether there are some countries that do not clearly belong in either group."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And while Freedom in the World identifies which countries are electoral democracies in recent years, its main classification distinguishes between free, partly-free, and not-free countries (which many treat as a proxy for liberal democracy)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Beyond these broad similarities in how the approaches characterize and score democracy, their exact definitions differ in smaller ways, too. If you are interested in the details, you can take a closer look at the specific defining characteristics at the end of this article."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""What differences are captured?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How the approaches score democracy affects what differences in democracy they can capture."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""html"", ""value"": ""
Varieties of Democracy
  • \tBig to very small differences
Regimes of the World
  • Big differences, with clear meaning
Lexical Index
  • Big to medium differences, with very clear meaning
Boix-Miller-Rosato
  • Big differences, with clear meaning
Polity
  • Big to medium differences
Freedom House
  • Big differences
Bertelsmann Transformation Index
  • Big to small differences
Economist Intelligence Unit
  • Big to small differences
"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Classifications tend to be coarser, and therefore cover big to medium differences in democracy: they reduce the complexity of political systems a lot and distinguish between broad types, such as the democracies of Chile and Norway on the one hand, and the non-democracies of North Korea and Saudi Arabia, on the other."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The fine-grained spectrums of other approaches meanwhile reduce political systems’ complexity a bit less, and capture both big and small differences in democracy, such as the difference in democratic quality between the democracies Chile and Norway, and the difference between autocracies North Korea and Saudi Arabia. Spectrums can also better capture small changes within political systems over time, towards or away from democracy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While some approaches use their classifications exclusively to reduce the complexity of their spectrums, others also use theirs to clearly define what features characterize each category."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""What years and countries are covered?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The approaches also differ in what years and countries they cover."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""html"", ""value"": ""
Varieties of Democracy
  • Years since 1789
  • 202 countries, also non-independent
Regimes of the World
  • Years since 1789
  • 202 countries, also non-independent
Lexical Index
  • Years since 1789
  • 242 countries, also non-independent and microstates
Boix-Miller-Rosato
  • Years since 1800
  • 218 countries, also microstates
Polity
  • Years 1800 — 2018
  • 192 countries
Freedom House
  • Years since 1972
  • 229 countries and territories, also microentities
Bertelsmann Transformation Index
  • Years since 2005
  • 138 countries and territories, no consolidated democracies
Economist Intelligence Unit
  • Years since 2006
  • 167 countries
"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All approaches cover the recent past, but differ in how far they go back in time. BTI and EIU begin in the mid-2000s. Freedom in the World starts in the early 1970s. The other approaches go back to the beginning of the 19th century or even the late 18th century. The Regimes of the World data we ourselves extended "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/regimes-of-the-world-data"", ""children"": [{""text"": ""back from 1900"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All approaches cover most countries in the world. They differ in how comprehensive their coverage is: BTI excludes long-term members of the OECD (which it considers consolidated democracies), while all other approaches assess them. Some approaches also include very small states and territories, and some also assess many non-independent countries, usually colonies."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""How are democracy’s characteristics assessed?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The approaches also differ in how they go about assessing the characteristics of democracy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""html"", ""value"": ""
Varieties of Democracy
  • Mostly through evaluations by experts; some easy-to-observe characteristics assessed by own researchers
  • Then weighting, adding, and multiplying scores for (sub)characteristics
Regimes of the World
  • Mostly through evaluations by experts; some easy-to-observe characteristics assessed by own researchers
  • Then evaluating whether necessary characteristics are (not) present
  • Then weighting, adding, and multiplying scores for a few characteristics
Lexical Index
  • Mostly with easy-to-observe characteristics, few evaluations by own researchers based on academic research
  • Then evaluating whether necessary characteristics are present or not
Boix-Miller-Rosato
  • Mostly with easy-to-observe characteristics, few evaluations by own researchers based on academic literature
  • Then evaluating whether necessary characteristics are present or not
Polity
  • Mostly through evaluations by own researchers based on academic literature and news reports
  • Then weighting and adding scores for characteristics
Freedom House
  • Mostly through evaluations by country and regional experts and own researchers based on different types of sources
  • Free countries: then adding scores for (sub)characteristics
  • Electoral democracies: then adding scores and evaluating whether necessary characteristics are present or not
Bertelsmann Transformation Index
  • Mostly through evaluations by country, regional, and general experts, some evaluations by representative surveys of regular citizens
  • Spectrum: then averaging of scores for (sub)characteristics
  • Classification: then averaging and evaluating whether necessary characteristics are present or not
Economist Intelligence Unit
  • Mostly through evaluations by own country experts, some evaluations by representative surveys of regular citizens
  • Then averaging and minor weighting of scores for (sub)characteristics
"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Many rely on evaluations to assess democratic characteristics that are difficult to observe, such as whether elections were competitive and people were free to express their views."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Some rely on evaluations by country experts to assess whether, or to which extent, democracy’s characteristics are present (or not) in any given country and year. Others depend on evaluations by their own researchers reviewing the academic literature and news reports. And many use both country experts and their own teams."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A few additionally incorporate some representative surveys of regular citizens."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Lexical Index and the Boix-Miller-Rosato data meanwhile work to avoid difficult evaluations by either experts or researchers, and mostly have their own teams assess easy-to-observe characteristics — such as whether regular elections are held and several parties compete in them — to identify (non-)democracies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Depending on whether they score democracy as a spectrum or classification, the approaches then aggregate the scores for specific characteristics: some average, add, and/or weigh the scores, others assess whether necessary characteristics are present, and a few do both."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How do approaches work to make assessments valid?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The next tables summarize how the approaches address the challenges that come with measuring democracy. The first challenge is to make their assessments valid — to actually measure what they want to capture."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""html"", ""value"": ""
Varieties of Democracy
  • Experts (often nationals or residents) know country and characteristics well, own researchers know measurement procedures well
Regimes of the World
  • Experts (often nationals or residents) know country and characteristics well, own researchers know measurement procedures well
Lexical Index
  • Own researchers know measurement procedures well
Boix-Miller-Rosato
  • Own researchers know measurement procedures well
Polity
  • Own researchers know measurement well
Freedom House
  • Experts know country or region well, own researchers know measurement well
Bertelsmann Transformation Index
  • Experts (about half of them local) know country well, regular citizens know their own experiences well
Economist Intelligence Unit
  • Experts know country or region well, regular citizens know their own experiences well
"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The approaches go about measuring democracy differently because they weigh the challenges of measurement differently."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For those mostly relying on experts, the priority is that democracy’s characteristics are evaluated by people that know the country well. For those relying on their own researchers, the priority is that the coders know the approach’s characterization of democracy and the measurement procedures well. And for those relying on representative surveys, capturing the difficult-to-observe lived realities of regular citizens is especially important."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How do approaches work to make assessments precise?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The approaches are also concerned with making their assessments in a precise and reliable manner."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""html"", ""value"": ""
Varieties of Democracy
  • Several experts per country, year, and characteristic used (usually 5 or more since 1900, often 25 per country)
Regimes of the World
  • Several experts per country, year, and characteristic used (usually 5 or more since 1900, often 25 per country)
Lexical Index
  • Characteristics easy to understand and observe; subjective evaluation therefore mostly unnecessary
Boix-Miller-Rosato
  • \tCharacteristics easy to understand and observe; subjective evaluation therefore mostly unnecessary
Polity
  • Several researchers used
Freedom House
  • More than 100 experts and researchers used in total; Experts and researchers rely on academic research, news and NGO reports, personal conversations, and on-the-ground research
Bertelsmann Transformation Index
  • Two experts per country and year used
Economist Intelligence Unit
  • One or two experts per country and year used
"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Expert-based approaches therefore often recruit many experts in total, several experts per country, or even several to many experts per country, year and characteristic."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Own-researcher-based approaches instead either focus more on making difficult subjective evaluation mostly unnecessary, or encourage their teams to rely on many different secondary sources, such as country-specific academic research, news reports, and personal conversations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How do approaches work to make assessments comparable?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The approaches also face the challenge of how to make the coders’ respective assessments comparable across countries and time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""html"", ""value"": ""
Varieties of Democracy
  • Experts answer very specific questions about sub-characteristics on completely explained scale
  • Experts also code hypothetical countries and many code several countries, denote own uncertainty and personal demographic information
  • Project investigated expert biases and found them to be limited
Regimes of the World
  • Experts answer very specific questions about sub-characteristics on completely explained scale
  • Experts also code hypothetical examples and many code several countries, denote own uncertainty and personal attributes
  • Project investigated expert biases and found them to be limited
Lexical Index
  • Researchers answer specific questions about characteristics on explained scale
  • Same researcher assesses all countries and years
Boix-Miller-Rosato
  • Same researcher assesses all countries and years
Polity
  • Experts answer specific questions about characteristics on completely explained scale
Freedom House
  • Experts answer questions about characteristics separately
Bertelsmann Transformation Index
  • Experts answer specific questions about sub-characteristics on explained scale
Economist Intelligence Unit
  • Experts answer specific questions about sub-characteristics on completely explained scale
"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The surveys therefore ask the experts questions about specific characteristics of democracy, such as the presence or absence of election fraud, instead of making them rely on their broad impressions. They also explain the scales on which the characteristics are scored, and often all of the scales’ values."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Measuring many specific low-level characteristics also helps users understand why a country received a specific score, and it allows them to create new measures tailored to their own interests."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How are remaining differences dealt with?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The approaches then all work to address any remaining differences between coders, even if they do so differently."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""html"", ""value"": ""
Varieties of Democracy
  • Measurement model uses main and additional information and provides estimates of remaining measurement uncertainty
Regimes of the World
  • Measurement model uses main and additional information and provides estimates of remaining measurement uncertainty
Lexical Index
  • One primary coder, so no differences between coders to be reconciled
  • Second researcher for some countries reproduced most assessments
Boix-Miller-Rosato
  • One primary coder, so no differences between coders to be reconciled
  • For recent years discussions among researchers reconcile different standards across coders, countries, and years
Polity
  • Discussions among researchers reconcile different standards across coders, countries, and years
  • Separate researcher teams for some countries and years reproduced most assessments
Freedom House
  • Discussions among experts and researchers reconcile different standards across coders, countries, and years
Bertelsmann Transformation Index
  • Discussions among regional and general experts and own researchers reconcile different standards across coders, countries, and years
Economist Intelligence Unit
  • Discussions among experts and researchers reconcile different standards across coders, countries, and years
"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""V-Dem and RoW work with a statistical model which uses the experts’ ratings of actual countries and hypothetical country examples, as well as the experts’ stated uncertainties and personal demographics to produce both best and upper- and lower-bound estimates of many characteristics."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""They thereby avoid forcing themselves to eliminate all uncertainty and thereby possibly biasing their scores, and acknowledge that its coders make errors. This also recognizes that small differences in democracy on fine-grained spectrums may actually not exist, or be reversed, because measurement is uncertain."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most other approaches go about it differently, and have researchers and experts discuss differing scores to reconcile them. This adds an additional step to make the assessments comparable across coders, countries, and years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And while it uses discussions, Freedom in the World still acknowledges that it refined its approach over time, which makes its scores not as readily comparable: they work best for comparing different countries at the same time, or comparing the same country over the course of a few years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Lexical Index and Polity meanwhile do not have several coders per country and year, but they still worked to assess coding differences by once having its researchers rate some countries independently and compare their results. Reassuringly, they found that they came to similar conclusions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How do approaches work to make data accessible and transparent?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Finally, the approaches all take steps to make their data accessible and the underlying measurement transparent."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""html"", ""value"": ""
Varieties of Democracy
  • Provides data for sub-indices and several hundred specific questions by country-year, country-date, and coder
  • Detailed questions and coding procedures are available and easy to access
  • Justifies democracy characteristics and their combination in detail
Regimes of the World
  • Provides data for sub-indices and several hundred specific questions by country-year, country-date, and coder
  • Detailed questions and coding procedures are available and easy to access
  • Justifies democracy characteristics and their combination
Lexical Index
  • Provides disaggregated data for specific questions by country-year
  • Questions and coding procedures are available and easy to access
  • Justifies in detail democracy characteristics and their combination
Boix-Miller-Rosato
  • Provides data by country-year
  • Questions and coding procedures are available and easy to access
  • Justifies democracy characteristics and their combination
Polity
  • Provides disaggregated data for sub-indices and specific questions by country-year
  • Detailed questions and coding procedures are available and easy to access
  • Explains scores with country reports
Freedom House
  • Provides recent disaggregated data for sub-indices and specific questions by country-year
  • Questions and coding procedures are available and easy to access
  • Justifies democracy characteristics
  • Explains scores with country reports
Bertelsmann Transformation Index
  • Provides disaggregated data for sub-indices and specific questions by country-year
  • Detailed questions and coding procedures are available and easy to access
  • Justifies democracy characteristics and their combination
  • Explains scores with country reports
Economist Intelligence Unit
  • Provides disaggregated data for sub-indices by country-year
  • Questions and coding procedures are available
  • Justifies democracy characteristics
"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All approaches publicly release their data and almost all make the data straightforward to download and use. Most approaches release not only the overall classification and scores, but also the underlying (sub)characteristics. V-Dem even releases the data coded by each (anonymous) expert."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Almost all release descriptions of how they characterize democracy, as well as the questions and coding procedures guiding the experts and researchers. V-Dem again stands out here for its very detailed descriptions that also discuss why it weighs, adds, and multiplies the scores for specific characteristics."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Polity, Freedom in the World, and BTI meanwhile provide additional helpful information by explaining their quantitative scores in country reports that discuss influential events."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""The best democracy measure depends on your questions"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There is no single ‘best’ approach to measuring democracy. Conceptions of democracy are too different, and the challenges of measurement are too diverse for that. All of the approaches put a lot of effort into measuring democracy in ways that are useful to researchers, policymakers, and interested citizens."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The most appropriate democracy measure depends on what question you want to answer. It is the one that captures the characteristics of democracy and the countries and years you are interested in."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you are interested in big and small differences in varieties of democracy, far into the past, and want to use country experts to measure characteristics of political systems that are difficult to observe, the Varieties of Democracy data is best."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you are instead interested in big differences in political regimes over the last two hundred years, and want to use the knowledge of country experts, the Regimes of the World data is best."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you rather want to explore medium differences in political regimes, especially in the 19th and earlier 20th century, and want to rely more on characteristics that are easier to observe, the Lexical Index ist best."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And if you want to study big differences in political regimes, drawing on easier-to-observe features of political systems, the Boix-Miller-Rosato data is best."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you instead want to explore the source that was researchers’ go-to for democracy for a long time, and are fine with its less precise data, Polity is best."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you especially care about the political and civil freedoms that democracy grants, Freedom House is best."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you value a broad understanding of democracy that encompasses its electoral, liberal, participatory, deliberative, and effective dimensions, then the Bertelsmann Transformation Index is best."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And if you want to use a broad understanding of democracy to study it both in countries where it is older and those in which it is young or absent, then the Economist Intelligence Unit is best."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Even if you have a preferred source, it can still be useful to see what other sources show and where they agree and differ."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This means that having several approaches to measuring democracy is not a flaw, but a strength: it gives us different tools to understand the past spread, current state, and possible future of democracy around the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you want to explore the data that each of these datasets produce, you can do so in our "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/democracy"", ""children"": [{""text"": ""Democracy Data Explorer"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And if you want to compare the sources directly, you can do so in these charts:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""left"": [{""url"": ""https://ourworldindata.org/grapher/democracy-index-by-source"", ""type"": ""chart"", ""parseErrors"": []}], ""type"": ""side-by-side"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/political-regime-classification-by-source"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Keep reading at "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Our World in Data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/explorers/democracy"", ""type"": ""prominent-link"", ""title"": ""Democracy Data Explorer"", ""thumbnail"": ""democracy-data-how-do-reasearchers-measure-democracy-featured-image.png"", ""description"": ""Explore the world’s political systems with the leading approaches of measuring democracy."", ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1xO9wHbwM5LKlGFbHyet3CLmiqnI3ZOHS8V3P_81Itf4/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/14zTYMg-mkPcMC68DqgQNb1eAe8VBwoD93yzyKGIeguw/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/14ThFloGyQv4uSGdEeZROWq_2yvHC9ggnAFij_9Oyz6g/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""text"": [{""text"": ""Acknowledgements"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I thank Daniel Bachler, Hauke Hartmann, Joan Hoey, Staffan Lindberg, Michael K. Miller, Hannah Ritchie, Max Roser, and Svend-Erik Skaaning for reading drafts of this text and their very helpful comments."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""expandable-paragraph"", ""items"": [{""text"": [{""text"": ""What are democracy's specific characteristics?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Varieties of Democracy"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Spectrums"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""electoral democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": extent to which political leaders are elected in free and fair elections under comprehensive voting rights and freedoms of association and expression"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""liberal democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": electoral democracy and extent to which citizens have individual and minority rights, are equal before the law, and the actions of the executive are constrained by the legislative and the courts"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""participatory democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": electoral democracy and extent to which citizens can engage in civil society organizations and direct democracy"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""deliberative democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": electoral democracy and extent to which citizens and leaders discuss different views and seek public good"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""egalitarian democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": electoral democracy and extent to which economic and social resources are distributed equally"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Regimes of the World"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Classification"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""closed autocracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": citizens do not have the right to choose either the chief executive of the government or the legislature through multi-party elections"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""electoral autocracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": citizens have the right to choose the chief executive and the legislature through multi-party elections; but they lack some freedoms, such as the freedoms of association or expression that make the elections meaningful, free, and fair"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""electoral democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": citizens have the right to choose the chief executive and the legislature in meaningful, free and fair, and multi-party elections"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""liberal democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": electoral democracy and citizens enjoy individual and minority rights, are equal before the law, and the actions of the executive are constrained by the legislative and the courts"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Lexical Index of Electoral Democracy"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Spectrum and classification"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""non-electoral autocracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": citizens do not have the right to elect the chief executive or the legislature"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""one-party autocracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": some citizens have the right to choose the chief executive or the legislature, but only have one choice"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""multiparty autocracy without elected executive"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": some citizens have the right to choose the legislature and have more than one choice, but chief executive not elected"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""multiparty autocracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": some citizens have the right to choose the chief executive and the legislature and have more than one choice, but election outcome is certain"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""exclusive democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": citizens have the right to choose the chief executive and the legislature in multi-party, uncertain elections, but suffrage is restricted"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""male democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": citizens have the right to choose the chief executive and the legislature in multi-party, uncertain elections, but suffrage is restricted to men"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""electoral democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": citizens have the right to choose the chief executive and the legislature in multi-party, uncertain elections"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""polyarchy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": citizens have the right to choose the chief executive and the legislature in multi-party, uncertain elections, and enjoy freedoms of expression, assembly, and association"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Boix-Miller-Rosato (BMR)"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Classifications"", 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"", ""spanType"": ""span-simple-text""}, {""url"": ""https://bti-project.org/en"", ""children"": [{""text"": ""Bertelsmann Transformation Index 2022"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d4124c4d79f641e6d5f582f6dc4887dfedbab809"": {""id"": ""d4124c4d79f641e6d5f582f6dc4887dfedbab809"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Cecilia Borella, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Oskar Rydén, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2024. \"""", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Coppedge%2C+Michael%2C+John+Gerring%2C+Carl+Henrik+Knutsen%2C+Staffan+I.+Lindberg%2C+Jan+Teorell%2C+David+Altman%2C+Fabio+Angiolillo%2C+Michael+Bernhard%2C+Cecilia+Borella%2C+Agnes+Cornell%2C+M.+Steven+Fish%2C+Linnea+Fox%2C+Lisa+Gastaldi%2C+Haakon+Gjerl%C3%B8w%2C+Adam+Glynn%2C+Ana+Good+God%2C+Sandra+Grahn%2C+Allen+Hicken%2C+Katrin+Kinzelbach%2C+Joshua+Krusell%2C+Kyle+L.+Marquardt%2C+Kelly+McMann%2C+Valeriya+Mechkova%2C+Juraj+Medzihorsky%2C+Natalia+Natsika%2C+Anja+Neundorf%2C+Pamela+Paxton%2C+Daniel+Pemstein%2C+Josefine+Pernes%2C+Oskar+Ryd%C3%A9n%2C+Johannes+von+R%C3%B6mer%2C+Brigitte+Seim%2C+Rachel+Sigman%2C+Svend-Erik+Skaaning%2C+Jeffrey+Staton%2C+Aksel+Sundstr%C3%B6m%2C+Eitan+Tzelgov%2C+Yi-ting+Wang%2C+Tore+Wig%2C+Steven+Wilson+and+Daniel+Ziblatt.+2024.+%22V-Dem+Country-Year+Dataset+v14%22+Varieties+of+Democracy+%28V-Dem%29+Project.+https%3A%2F%2Fdoi.org%2F10.23696%2Fmcwt-fr58&btnG="", ""children"": [{""text"": ""V-Dem Country-Year Dataset v14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""\"" Varieties of Democracy (V-Dem) Project. https://doi.org/10.23696/mcwt-fr58"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Pemstein, Daniel, Kyle L. 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University of Gothenburg: Varieties of Democracy Institute."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""fc29ef02a8112cf544a7007baac240c4774c1b12"": {""id"": ""fc29ef02a8112cf544a7007baac240c4774c1b12"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Skaaning, Svend-Erik, John Gerring, and Henrikas Bartusevičius. 2015. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Skaaning%2C+Svend-Erik%2C+John+Gerring%2C+and+Henrikas+Bartusevi%C4%8Dius.+2015.+A+Lexical+Index+of+Electoral+Democracy.+Comparative+Political+Studies+48%2812%29%3A+1491-1525&btnG="", ""children"": [{""text"": ""A Lexical Index of Electoral Democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Comparative Political Studies 48(12): 1491-1525."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Democracy data: how sources differ and when to use which one"", ""authors"": [""Bastian Herre""], ""excerpt"": ""There are many ways to classify and measure political systems. What approaches do different sources take? And when is which approach best?"", ""dateline"": ""originally published on June 17, 2022 (last updated in April 2024)"", ""subtitle"": ""There are many ways to classify and measure political systems. What approaches do different sources take? And when is which approach best?"", ""featured-image"": ""democracy-data-how-do-reasearchers-measure-democracy-featured-image.png""}",1,2023-07-21 16:10:29,2022-06-17 07:24:25,2024-02-27 15:15:07,unlisted,ALBJ4LtV3LLmyT-uK-nRvj8NHdMzsCJGoFRsxj3DYNYtDTuqjNYn-UFIjQKlVhXVIrITGWpnE9yno6GCySTMGA,,"Measuring the state of democracy across the world helps us understand the extent to which people have political rights and freedoms. But measuring how democratic a country is, comes with many challenges. People do not always agree on what characteristics define a democracy. These characteristics — such as whether an election was free and fair — even once defined, are difficult to assess. The judgement of experts is to some degree subjective and they may disagree; either about a specific characteristic, or how several characteristics can be reduced into a single measure of democracy. So how do researchers address these challenges and identify which countries are democratic and undemocratic? In our work on [Democracy](https://ourworldindata.org/democracy), we provide data from eight leading approaches of measuring democracy: * [Varieties of Democracy (V-Dem)](https://www.v-dem.net/vdemds.html) by the V-Dem project1 * [Regimes of the World (RoW)](https://www.v-dem.net/vdemds.html) by Lührmann et al. (2018)2, which use V-Dem data * [Lexical Index of Electoral Democracy (LIED)](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/WPKNIT) by Skaaning et al. (2015)3 * [Boix-Miller-Rosato](https://sites.google.com/site/mkmtwo/data?authuser=0) by Boix et al. (2013, BMR)4 * [Polity](https://www.systemicpeace.org/inscrdata.html) by the Center for Systemic Peace5 * Freedom House’s (FH) [Freedom in the World](https://freedomhouse.org/report/freedom-world)6 * [Bertelsmann Transformation Index (BTI)](https://bti-project.org/en/downloads) by the Bertelsmann Foundation7 * Economist Intelligence Unit’s (EIU) [Democracy Index](https://www.eiu.com/n/campaigns/democracy-index-2021/?utm_source=eiu-website&utm_medium=blog&utm_campaign=democracy-index-2021)8 These approaches all measure democracy (or a closely related aspect), they cover many countries and years, and are commonly used by researchers and policymakers. You can delve into their data — the main democracy measures, indicators of specific characteristics, and global and regional overviews —  in our [Democracy Data Explorer](https://ourworldindata.org/explorers/democracy). Reassuringly, the approaches typically agree about big differences in countries’ political institutions: they readily distinguish between highly democratic countries, such as Chile and Norway, and highly undemocratic countries, such as North Korea and Saudi Arabia. But they do not always agree. They come to different assessments about which of the two highly democratic countries – Chile and Norway – is more democratic, and whether Chile is more or less democratic than it was ten years ago. At times they come to strikingly different conclusions about countries that are neither highly democratic nor highly undemocratic, such as Nigeria today or the United States in the 19th century. Why do these measures sometimes reach such different conclusions? In this article I summarize the key similarities and differences of these approaches, and discuss when each source is best. --- # How is democracy characterized? In this and the following tables I summarize how each approach defines and scores democracy, and what coverage each approach provides.9
Varieties of Democracy
  • Narrow and broader: electoral, liberal, participatory, deliberative, or egalitarian democracy
Regimes of the World
  • Narrow: electoral or liberal democracy
Lexical Index
  • Narrow: electoral (or liberal) democracy
Boix-Miller-Rosato
  • Narrow: electoral democracy
Polity
  • Narrow: electoral and liberal democracy
Freedom House
  • Narrow: electoral or liberal democracy
Bertelsmann Transformation Index
  • Broad: electoral, liberal, participatory, deliberative, and effective democracy
Economist Intelligence Unit
  • Broad: electoral, liberal, participatory, deliberative, and effective democracy
We see that the approaches share a basic principle of democracy: a democracy is an electoral political system in which citizens get to participate in free and fair elections. The approaches also mostly agree that democracies are liberal political systems, in which citizens have additional civil rights and are protected from the state by constraining it. Some approaches stop there, and stick to these narrower conceptions of democracy. Others characterize democracy in broader terms, and also see it as a participatory and deliberative (citizens engage in elections, civil society, and public discourse) as well as an effective (governments can act on citizens’ behalf) political system. Varieties of Democracy — true to its name — offers both narrow and broader characterizations, by separately adding liberal, participatory, deliberative, as well as egalitarian (economic and social resources are equally distributed) political institutions to electoral democracy. --- # How is democracy scored? The approaches also differ in how they _score_ democracy.
Varieties of Democracy
  • On a spectrum: 0 to 1, highly undemocratic to highly democratic
Regimes of the World
  • As a classification: closed autocracy < electoral autocracy < electoral democracy < liberal democracy
Lexical Index
  • As a classification: non-electoral autocracy < one-party autocracy < multi-party autocracy without elected executive < multi-party autocracy < exclusive democracy < male democracy < electoral democracy < polyarchy
Boix-Miller-Rosato
  • As a classification: non-democracy < democracy
Polity
  • On a spectrum: -10 to 10, hereditary monarchy to consolidated democracy
  • classification: autocracy < anocracy < democracy
Freedom House
  • As a classification; classification 1: not free < partly free < free
  • classification 2: non-democracy < electoral democracy
Bertelsmann Transformation Index
  • On a spectrum: 1 to 10, highly undemocratic to highly democratic
  • classification: hard-line autocracy < moderate autocracy < very defective democracy < defective democracy < consolidating democracy
Economist Intelligence Unit
  • On a spectrum: 0 to 10, highly undemocratic to 10 highly democratic
  • classification: authoritarian regime < hybrid regime < flawed democracy < full democracy
V-Dem treats democracy as a **spectrum**, with some countries being scored as more democratic than others. Other approaches instead treat democracy as a binary, and **classify** a country as either a democracy or not. A final group **does both**, using a spectrum of countries being more or less democratic, and setting thresholds above which a country is considered a democracy overall. Approaches that classify countries into democracies and non-democracies further differ in whether all countries that are not democracies are considered autocracies or authoritarian regimes, or whether there are some countries that do not clearly belong in either group. And while Freedom in the World identifies which countries are electoral democracies in recent years, its main classification distinguishes between free, partly-free, and not-free countries (which many treat as a proxy for liberal democracy). Beyond these broad similarities in how the approaches characterize and score democracy, their exact definitions differ in smaller ways, too. If you are interested in the details, you can take a closer look at the specific defining characteristics at the end of this article. --- # What differences are captured? How the approaches score democracy affects what differences in democracy they can capture.
Varieties of Democracy
  • Big to very small differences
Regimes of the World
  • Big differences, with clear meaning
Lexical Index
  • Big to medium differences, with very clear meaning
Boix-Miller-Rosato
  • Big differences, with clear meaning
Polity
  • Big to medium differences
Freedom House
  • Big differences
Bertelsmann Transformation Index
  • Big to small differences
Economist Intelligence Unit
  • Big to small differences
Classifications tend to be coarser, and therefore cover big to medium differences in democracy: they reduce the complexity of political systems a lot and distinguish between broad types, such as the democracies of Chile and Norway on the one hand, and the non-democracies of North Korea and Saudi Arabia, on the other. The fine-grained spectrums of other approaches meanwhile reduce political systems’ complexity a bit less, and capture both big and small differences in democracy, such as the difference in democratic quality between the democracies Chile and Norway, and the difference between autocracies North Korea and Saudi Arabia. Spectrums can also better capture small changes within political systems over time, towards or away from democracy. While some approaches use their classifications exclusively to reduce the complexity of their spectrums, others also use theirs to clearly define what features characterize each category. --- # What years and countries are covered? The approaches also differ in what years and countries they cover.
Varieties of Democracy
  • Years since 1789
  • 202 countries, also non-independent
Regimes of the World
  • Years since 1789
  • 202 countries, also non-independent
Lexical Index
  • Years since 1789
  • 242 countries, also non-independent and microstates
Boix-Miller-Rosato
  • Years since 1800
  • 218 countries, also microstates
Polity
  • Years 1800 — 2018
  • 192 countries
Freedom House
  • Years since 1972
  • 229 countries and territories, also microentities
Bertelsmann Transformation Index
  • Years since 2005
  • 138 countries and territories, no consolidated democracies
Economist Intelligence Unit
  • Years since 2006
  • 167 countries
All approaches cover the recent past, but differ in how far they go back in time. BTI and EIU begin in the mid-2000s. Freedom in the World starts in the early 1970s. The other approaches go back to the beginning of the 19th century or even the late 18th century. The Regimes of the World data we ourselves extended [back from 1900](https://ourworldindata.org/regimes-of-the-world-data). All approaches cover most countries in the world. They differ in how comprehensive their coverage is: BTI excludes long-term members of the OECD (which it considers consolidated democracies), while all other approaches assess them. Some approaches also include very small states and territories, and some also assess many non-independent countries, usually colonies.10 --- # How are democracy’s characteristics assessed? The approaches also differ in how they go about assessing the characteristics of democracy.
Varieties of Democracy
  • Mostly through evaluations by experts; some easy-to-observe characteristics assessed by own researchers
  • Then weighting, adding, and multiplying scores for (sub)characteristics
Regimes of the World
  • Mostly through evaluations by experts; some easy-to-observe characteristics assessed by own researchers
  • Then evaluating whether necessary characteristics are (not) present
  • Then weighting, adding, and multiplying scores for a few characteristics
Lexical Index
  • Mostly with easy-to-observe characteristics, few evaluations by own researchers based on academic research
  • Then evaluating whether necessary characteristics are present or not
Boix-Miller-Rosato
  • Mostly with easy-to-observe characteristics, few evaluations by own researchers based on academic literature
  • Then evaluating whether necessary characteristics are present or not
Polity
  • Mostly through evaluations by own researchers based on academic literature and news reports
  • Then weighting and adding scores for characteristics
Freedom House
  • Mostly through evaluations by country and regional experts and own researchers based on different types of sources
  • Free countries: then adding scores for (sub)characteristics
  • Electoral democracies: then adding scores and evaluating whether necessary characteristics are present or not
Bertelsmann Transformation Index
  • Mostly through evaluations by country, regional, and general experts, some evaluations by representative surveys of regular citizens
  • Spectrum: then averaging of scores for (sub)characteristics
  • Classification: then averaging and evaluating whether necessary characteristics are present or not
Economist Intelligence Unit
  • Mostly through evaluations by own country experts, some evaluations by representative surveys of regular citizens
  • Then averaging and minor weighting of scores for (sub)characteristics
Many rely on evaluations to assess democratic characteristics that are difficult to observe, such as whether elections were competitive and people were free to express their views. Some rely on evaluations by country experts to assess whether, or to which extent, democracy’s characteristics are present (or not) in any given country and year. Others depend on evaluations by their own researchers reviewing the academic literature and news reports. And many use both country experts and their own teams. A few additionally incorporate some representative surveys of regular citizens. The Lexical Index and the Boix-Miller-Rosato data meanwhile work to avoid difficult evaluations by either experts or researchers, and mostly have their own teams assess easy-to-observe characteristics — such as whether regular elections are held and several parties compete in them — to identify (non-)democracies. Depending on whether they score democracy as a spectrum or classification, the approaches then aggregate the scores for specific characteristics: some average, add, and/or weigh the scores, others assess whether necessary characteristics are present, and a few do both. ## How do approaches work to make assessments valid? The next tables summarize how the approaches address the challenges that come with measuring democracy. The first challenge is to make their assessments valid — to actually measure what they want to capture.
Varieties of Democracy
  • Experts (often nationals or residents) know country and characteristics well, own researchers know measurement procedures well
Regimes of the World
  • Experts (often nationals or residents) know country and characteristics well, own researchers know measurement procedures well
Lexical Index
  • Own researchers know measurement procedures well
Boix-Miller-Rosato
  • Own researchers know measurement procedures well
Polity
  • Own researchers know measurement well
Freedom House
  • Experts know country or region well, own researchers know measurement well
Bertelsmann Transformation Index
  • Experts (about half of them local) know country well, regular citizens know their own experiences well
Economist Intelligence Unit
  • Experts know country or region well, regular citizens know their own experiences well
The approaches go about measuring democracy differently because they weigh the challenges of measurement differently. For those mostly relying on experts, the priority is that democracy’s characteristics are evaluated by people that know the country well. For those relying on their own researchers, the priority is that the coders know the approach’s characterization of democracy and the measurement procedures well. And for those relying on representative surveys, capturing the difficult-to-observe lived realities of regular citizens is especially important. ## How do approaches work to make assessments precise? The approaches are also concerned with making their assessments in a precise and reliable manner.
Varieties of Democracy
  • Several experts per country, year, and characteristic used (usually 5 or more since 1900, often 25 per country)
Regimes of the World
  • Several experts per country, year, and characteristic used (usually 5 or more since 1900, often 25 per country)
Lexical Index
  • Characteristics easy to understand and observe; subjective evaluation therefore mostly unnecessary
Boix-Miller-Rosato
  • Characteristics easy to understand and observe; subjective evaluation therefore mostly unnecessary
Polity
  • Several researchers used
Freedom House
  • More than 100 experts and researchers used in total; Experts and researchers rely on academic research, news and NGO reports, personal conversations, and on-the-ground research
Bertelsmann Transformation Index
  • Two experts per country and year used
Economist Intelligence Unit
  • One or two experts per country and year used
Expert-based approaches therefore often recruit many experts in total, several experts per country, or even several to many experts per country, year and characteristic. Own-researcher-based approaches instead either focus more on making difficult subjective evaluation mostly unnecessary, or encourage their teams to rely on many different secondary sources, such as country-specific academic research, news reports, and personal conversations. ## How do approaches work to make assessments comparable? The approaches also face the challenge of how to make the coders’ respective assessments comparable across countries and time.
Varieties of Democracy
  • Experts answer very specific questions about sub-characteristics on completely explained scale
  • Experts also code hypothetical countries and many code several countries, denote own uncertainty and personal demographic information
  • Project investigated expert biases and found them to be limited
Regimes of the World
  • Experts answer very specific questions about sub-characteristics on completely explained scale
  • Experts also code hypothetical examples and many code several countries, denote own uncertainty and personal attributes
  • Project investigated expert biases and found them to be limited
Lexical Index
  • Researchers answer specific questions about characteristics on explained scale
  • Same researcher assesses all countries and years
Boix-Miller-Rosato
  • Same researcher assesses all countries and years
Polity
  • Experts answer specific questions about characteristics on completely explained scale
Freedom House
  • Experts answer questions about characteristics separately
Bertelsmann Transformation Index
  • Experts answer specific questions about sub-characteristics on explained scale
Economist Intelligence Unit
  • Experts answer specific questions about sub-characteristics on completely explained scale
The surveys therefore ask the experts questions about specific characteristics of democracy, such as the presence or absence of election fraud, instead of making them rely on their broad impressions. They also explain the scales on which the characteristics are scored, and often all of the scales’ values. Measuring many specific low-level characteristics also helps users understand why a country received a specific score, and it allows them to create new measures tailored to their own interests. ## How are remaining differences dealt with? The approaches then all work to address any remaining differences between coders, even if they do so differently.
Varieties of Democracy
  • Measurement model uses main and additional information and provides estimates of remaining measurement uncertainty
Regimes of the World
  • Measurement model uses main and additional information and provides estimates of remaining measurement uncertainty
Lexical Index
  • One primary coder, so no differences between coders to be reconciled
  • Second researcher for some countries reproduced most assessments
Boix-Miller-Rosato
  • One primary coder, so no differences between coders to be reconciled
  • For recent years discussions among researchers reconcile different standards across coders, countries, and years
Polity
  • Discussions among researchers reconcile different standards across coders, countries, and years
  • Separate researcher teams for some countries and years reproduced most assessments
Freedom House
  • Discussions among experts and researchers reconcile different standards across coders, countries, and years
Bertelsmann Transformation Index
  • Discussions among regional and general experts and own researchers reconcile different standards across coders, countries, and years
Economist Intelligence Unit
  • Discussions among experts and researchers reconcile different standards across coders, countries, and years
V-Dem and RoW work with a statistical model which uses the experts’ ratings of actual countries and hypothetical country examples, as well as the experts’ stated uncertainties and personal demographics to produce both best and upper- and lower-bound estimates of many characteristics. They thereby avoid forcing themselves to eliminate all uncertainty and thereby possibly biasing their scores, and acknowledge that its coders make errors. This also recognizes that small differences in democracy on fine-grained spectrums may actually not exist, or be reversed, because measurement is uncertain. Most other approaches go about it differently, and have researchers and experts discuss differing scores to reconcile them. This adds an additional step to make the assessments comparable across coders, countries, and years. And while it uses discussions, Freedom in the World still acknowledges that it refined its approach over time, which makes its scores not as readily comparable: they work best for comparing different countries at the same time, or comparing the same country over the course of a few years. The Lexical Index and Polity meanwhile do not have several coders per country and year, but they still worked to assess coding differences by once having its researchers rate some countries independently and compare their results. Reassuringly, they found that they came to similar conclusions. ## How do approaches work to make data accessible and transparent? Finally, the approaches all take steps to make their data accessible and the underlying measurement transparent.
Varieties of Democracy
  • Provides data for sub-indices and several hundred specific questions by country-year, country-date, and coder
  • Detailed questions and coding procedures are available and easy to access
  • Justifies democracy characteristics and their combination in detail
Regimes of the World
  • Provides data for sub-indices and several hundred specific questions by country-year, country-date, and coder
  • Detailed questions and coding procedures are available and easy to access
  • Justifies democracy characteristics and their combination
Lexical Index
  • Provides disaggregated data for specific questions by country-year
  • Questions and coding procedures are available and easy to access
  • Justifies in detail democracy characteristics and their combination
Boix-Miller-Rosato
  • Provides data by country-year
  • Questions and coding procedures are available and easy to access
  • Justifies democracy characteristics and their combination
Polity
  • Provides disaggregated data for sub-indices and specific questions by country-year
  • Detailed questions and coding procedures are available and easy to access
  • Explains scores with country reports
Freedom House
  • Provides recent disaggregated data for sub-indices and specific questions by country-year
  • Questions and coding procedures are available and easy to access
  • Justifies democracy characteristics
  • Explains scores with country reports
Bertelsmann Transformation Index
  • Provides disaggregated data for sub-indices and specific questions by country-year
  • Detailed questions and coding procedures are available and easy to access
  • Justifies democracy characteristics and their combination
  • Explains scores with country reports
Economist Intelligence Unit
  • Provides disaggregated data for sub-indices by country-year
  • Questions and coding procedures are available
  • Justifies democracy characteristics
All approaches publicly release their data and almost all make the data straightforward to download and use. Most approaches release not only the overall classification and scores, but also the underlying (sub)characteristics. V-Dem even releases the data coded by each (anonymous) expert. Almost all release descriptions of how they characterize democracy, as well as the questions and coding procedures guiding the experts and researchers. V-Dem again stands out here for its very detailed descriptions that also discuss why it weighs, adds, and multiplies the scores for specific characteristics. Polity, Freedom in the World, and BTI meanwhile provide additional helpful information by explaining their quantitative scores in country reports that discuss influential events. --- # The best democracy measure depends on your questions There is no single ‘best’ approach to measuring democracy. Conceptions of democracy are too different, and the challenges of measurement are too diverse for that. All of the approaches put a lot of effort into measuring democracy in ways that are useful to researchers, policymakers, and interested citizens. The most appropriate democracy measure depends on what question you want to answer. It is the one that captures the characteristics of democracy and the countries and years you are interested in. This means that having several approaches to measuring democracy is not a flaw, but a strength: it gives us different tools to understand the past spread, current state, and possible future of democracy around the world. If you are interested in big and small differences in varieties of democracy, far into the past, and want to use country experts to measure characteristics of political systems that are difficult to observe, the Varieties of Democracy data is best. If you are instead interested in big differences in political regimes over the last two hundred years, and want to use the knowledge of country experts, the Regimes of the World data is best. If you rather want to explore medium differences in political regimes, especially in the 19th and earlier 20th century, and want to rely more on characteristics that are easier to observe, the Lexical Index ist best. And if you want to study big differences in political regimes, drawing on easier-to-observe features of political systems, the Boix-Miller-Rosato data is best. If you instead want to explore the source that was researchers’ go-to for democracy for a long time, and are fine with its less precise data, Polity is best. If you especially care about the political and civil freedoms that democracy grants, Freedom House is best. If you value a broad understanding of democracy that encompasses its electoral, liberal, participatory, deliberative, and effective dimensions, then the Bertelsmann Transformation Index is best. And if you want to use a broad understanding of democracy to study it both in countries where it is older and those in which it is young or absent, then the Economist Intelligence Unit is best. If you want to explore the data that each of these datasets produce, you can do so in our [Democracy Data Explorer](https://ourworldindata.org/explorers/democracy). And if you want to compare the sources directly, you can do so in these charts: ## Keep reading at _Our World in Data_ ### Democracy Data Explorer Explore the world’s political systems with the leading approaches of measuring democracy. https://ourworldindata.org/explorers/democracy ### undefined undefined https://docs.google.com/document/d/1xO9wHbwM5LKlGFbHyet3CLmiqnI3ZOHS8V3P_81Itf4/edit ### undefined undefined https://docs.google.com/document/d/14zTYMg-mkPcMC68DqgQNb1eAe8VBwoD93yzyKGIeguw/edit ### undefined undefined https://docs.google.com/document/d/14ThFloGyQv4uSGdEeZROWq_2yvHC9ggnAFij_9Oyz6g/edit ## Acknowledgements I thank Daniel Bachler, Hauke Hartmann, Joan Hoey, Staffan Lindberg, Michael K. Miller, Hannah Ritchie, Max Roser, and Svend-Erik Skaaning for reading drafts of this text and their very helpful comments. # What are democracy's specific characteristics? ## Varieties of Democracy ### Spectrums * **electoral democracy**: extent to which political leaders are elected in free and fair elections under comprehensive voting rights and freedoms of association and expression * **liberal democracy**: electoral democracy and extent to which citizens have individual and minority rights, are equal before the law, and the actions of the executive are constrained by the legislative and the courts * **participatory democracy**: electoral democracy and extent to which citizens can engage in civil society organizations and direct democracy * **deliberative democracy**: electoral democracy and extent to which citizens and leaders discuss different views and seek public good * **egalitarian democracy**: electoral democracy and extent to which economic and social resources are distributed equally ## Regimes of the World ### Classification * **closed autocracy**: citizens do not have the right to choose either the chief executive of the government or the legislature through multi-party elections * **electoral autocracy**: citizens have the right to choose the chief executive and the legislature through multi-party elections; but they lack some freedoms, such as the freedoms of association or expression that make the elections meaningful, free, and fair * **electoral democracy**: citizens have the right to choose the chief executive and the legislature in meaningful, free and fair, and multi-party elections * **liberal democracy**: electoral democracy and citizens enjoy individual and minority rights, are equal before the law, and the actions of the executive are constrained by the legislative and the courts ## Lexical Index of Electoral Democracy ### Spectrum and classification * **non-electoral autocracy**: citizens do not have the right to elect the chief executive or the legislature * **one-party autocracy**: some citizens have the right to choose the chief executive or the legislature, but only have one choice * **multiparty autocracy without elected executive**: some citizens have the right to choose the legislature and have more than one choice, but chief executive not elected * **multiparty autocracy**: some citizens have the right to choose the chief executive and the legislature and have more than one choice, but election outcome is certain * **exclusive democracy**: citizens have the right to choose the chief executive and the legislature in multi-party, uncertain elections, but suffrage is restricted * **male democracy**: citizens have the right to choose the chief executive and the legislature in multi-party, uncertain elections, but suffrage is restricted to men * **electoral democracy**: citizens have the right to choose the chief executive and the legislature in multi-party, uncertain elections * **polyarchy**: citizens have the right to choose the chief executive and the legislature in multi-party, uncertain elections, and enjoy freedoms of expression, assembly, and association ## Boix-Miller-Rosato (BMR) ### Classifications * **democracy**: a majority of adult men have the right to choose the chief executive and the legislature in free and fair elections * **democracy with women's suffrage**: a majority of adult men and women have the right to choose the chief executive and the legislature in free and fair elections ## Polity ### Spectrum and classification * **full/consolidated democracy**: open, multi-party, and competitive elections choose chief executive, who faces comprehensive institutional constraints, and political participation is competitive * **democracy**: mostly democratic characteristics * **anocracy**: neither clearly democratic nor autocratic characteristics * **autocracy**: mostly autocratic characteristics * **full autocracy/hereditary monarchy**: hereditary succession chooses chief executive who faces no institutional constraints, and political participation is restricted and suppressed ## Freedom in the World ### Classification 1 * **free country**: citizens have many political rights (free and fair elections, political pluralism and participation, functioning government) and civil liberties (freedoms of expression and association, rule of law, personal autonomy) * **partly free country**: citizens have some political rights and civil liberties * **not free country**: citizens have few political rights and civil liberties ### Classification 2 * **electoral democracy**: citizens have the right to choose chief executive and legislature in broadly free and fair elections and have substantial other political rights and civil liberties ## Bertelsmann Transformation Index (BTI) ### Spectrum * **democratic features**: extent of political participation, the rule of law, stable democratic institutions, political and social integration, and a capable state ### Classification * **consolidating democracy**: comprehensive democratic features and minimum democratic characteristics (citizens can choose political leaders in free and fair elections and enjoy freedoms of association, expression and some further civil liberties, political power is separated, and leaders can effectively govern a state that fulfils basic functions) * **defective democracy**: minimum democratic characteristics, but limited other democratic features * **very defective democracy**: minimum democratic characteristics, but very limited other democratic features * **moderate autocracy**: no minimum democratic characteristics, but possibly other broadly democratic features * **hard-line autocracy**: no minimum democratic characteristics, and few other democratic features ## Economist Intelligence Unit (EIU) ### Spectrum: * **democracy**: extent to which citizens can choose their political leaders in free and fair elections, enjoy civil liberties, prefer democracy over other political systems, can and do participate in politics, and have a functioning government that acts on their behalf ### Classification * **full democracy**: comprehensive extent of democracy, few weaknesses * **flawed democracy**: some weaknesses in democratic institutions and culture * **hybrid regime**: serious weaknesses in democratic institutions and culture * **authoritarian regime**: few democratic institutions and little democratic culture Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Oskar Rydén, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson and Daniel Ziblatt. 2023. [V-Dem [Country-Year/Country-Date] Dataset v13.](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Coppedge%2C+Michael%2C+John+Gerring%2C+Carl+Henrik+Knutsen%2C+Staffan+I.+Lindberg%2C+Jan+Teorell%2C+David+Altman%2C+Michael+Bernhard%2C+Agnes+Cornell%2C+M.+Steven+Fish%2C+Lisa+Gastaldi%2C+Haakon+Gjerl%C3%B8w%2C+Adam+Glynn%2C+Ana+Good+God%2C+Sandra+Grahn%2C+Allen+Hicken%2C+Katrin+Kinzelbach%2C+Joshua+Krusell%2C+Kyle+L.+Marquardt%2C+Kelly+McMann%2C+Valeriya+Mechkova%2C+Juraj+Medzihorsky%2C+Natalia+Natsika%2C+Anja+Neundorf%2C+Pamela+Paxton%2C+Daniel+Pemstein%2C+Josefine+Pernes%2C+Oskar+Ryd%C3%A9n%2C+Johannes+von+R%C3%B6mer%2C+Brigitte+Seim%2C+Rachel+Sigman%2C+Svend-Erik+Skaaning%2C+Jeffrey+Staton%2C+Aksel+Sundstr%C3%B6m%2C+Eitan+Tzelgov%2C+Yi-ting+Wang%2C+Tore+Wig%2C+Steven+Wilson+and+Daniel+Ziblatt.+2023.+V-Dem+%5BCountry-Year%2FCountry-Date%5D+Dataset+v13.+Varieties+of+Democracy+%28V-Dem%29+Project.&btnG=) Varieties of Democracy (V-Dem) Project. Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2023. [The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data.](https://v-dem.net/media/publications/Working_Paper_21_z5BldB1.pdf) V-Dem Working Paper No. 21. University of Gothenburg: Varieties of Democracy Institute. Lührmann, Anna, Marcus Tannnberg, and Staffan Lindberg. 2018. Regimes of the World (RoW): [Opening New Avenues for the Comparative Study of Political Regimes](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=L%C3%BChrmann%2C+Anna%2C+Marcus+Tannnberg%2C+and+Staffan+Lindberg.+2018.+Regimes+of+the+World+%28RoW%29%3A+Opening+New+Avenues+for+the+Comparative+Study+of+Political+Regimes.+Politics+and+Governance+6%281%29%3A+60-77.%7B%2Fref%7D&btnG=). Politics and Governance 6(1): 60-77. Skaaning, Svend-Erik, John Gerring, and Henrikas Bartusevičius. 2015. [A Lexical Index of Electoral Democracy](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Skaaning%2C+Svend-Erik%2C+John+Gerring%2C+and+Henrikas+Bartusevi%C4%8Dius.+2015.+A+Lexical+Index+of+Electoral+Democracy.+Comparative+Political+Studies+48%2812%29%3A+1491-1525&btnG=). Comparative Political Studies 48(12): 1491-1525. Boix, Carles, Michael Miller, and Sebastian Rosato. [A Complete Data Set of Political Regimes, 1800–2007](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Boix%2C+Carles%2C+Michael+Miller%2C+and+Sebastian+Rosato.+A+Complete+Data+Set+of+Political+Regimes%2C+1800%E2%80%932007.+Comparative+Political+Studies+46%2812%29%3A1523-1554.&btnG=). Comparative Political Studies 46(12):1523-1554. Marshall, Monty G. and Ted Robert Gurr. 2021. [Polity 5: Political Regime Characteristics and Transitions, 1800-2018](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Marshall%2C+Monty+G.+and+Ted+Robert+Gurr.+2021.+Polity+5%3A+Political+Regime+Characteristics+and+Transitions%2C+1800-2018.+Center+for+Systemic+Peace&btnG=). Center for Systemic Peace. Freedom House. 2022. [Freedom in the world 2022](https://freedomhouse.org/report/freedom-world). Bertelsmann Foundation. 2022. [Bertelsmann Transformation Index 2022](https://bti-project.org/en). Economist Intelligence Unit. 2023. [Democracy Index 2022: Frontline democracy and the battle for Ukraine.](https://www.eiu.com/n/campaigns/democracy-index-2022/) This article draws on several very helpful other articles summarizing and reviewing some of the datasets, as well as the datasets’ own codebooks and descriptions: Bertelsmann Foundation. 2022. [BTI Codebook for Stata](http://web.archive.org/web/20220404134021/https://bti-project.org/fileadmin/api/content/en/downloads/data/BTI_2022_Codebook_for_Stata.pdf). Boese, Vanessa. 2019. [How (not) to measure democracy](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Boese%2C+Vanessa.+2019.+How+%28not%29+to+measure+democracy.+International+Area+Studies+Review+22%282%29%3A+95-127&btnG=). International Area Studies Review 22(2): 95-127. Boix, Carles, Michael K. Miller, and Sebastian Rosato. 2022. [Boix-Miller-Rosato (BMR) Dichotomous Coding of Democracy, Version 4.0 (1800-2020) Codebook](https://sites.google.com/site/mkmtwo/data?authuser=0). Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Eitan Tzelgov, Luca Uberti, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2022. [V-Dem Codebook v12](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Coppedge%2C+Michael%2C+John+Gerring%2C+Carl+Henrik+Knutsen%2C+Staffan+I.+Lindberg%2C+Jan+Teorell%2C+David+Altman%2C+Michael+Bernhard%2C+Agnes+Cornell%2C+M.+Steven+Fish%2C+Lisa+Gastaldi%2C+Haakon+Gjerl%C3%B8w%2C+Adam+Glynn%2C+Sandra+Grahn%2C+Allen+Hicken%2C+Katrin+Kinzelbach%2C+Kyle+L.+Marquardt%2C+Kelly+McMann%2C+Valeriya+Mechkova%2C+Pamela+Paxton%2C+Daniel+Pemstein%2C+Johannes+von+R%C3%B6mer%2C+Brigitte+Seim%2C+Rachel+Sigman%2C+Svend-Erik+Skaaning%2C+Jeffrey+Staton%2C+Eitan+Tzelgov%2C+Luca+Uberti%2C+Yi-ting+Wang%2C+Tore+Wig%2C+and+Daniel+Ziblatt.+2022.+V-Dem+Codebook+v12.+Varieties+of+Democracy+%28V-Dem%29+Project.&btnG=). Varieties of Democracy (V-Dem) Project. Coppedge, Michael, John Gerring, Staffan I. Lindberg, Svend-Erik Skaaning, and Jan Teorell. 2017. [V-Dem Comparisons and Contrasts with Other Measurement Projects](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Coppedge%2C+Michael%2C+John+Gerring%2C+Staffan+I.+Lindberg%2C+Svend-Erik+Skaaning%2C+and+Jan+Teorell.+2017.+V-Dem+Comparisons+and+Contrasts+with+Other+Measurement+Projects.+V-Dem+Working+Paper+45.&btnG=). V-Dem Working Paper 45. Economist Intelligence Unit. 2023. [Democracy Index 2022: Frontline democracy and the battle for Ukraine.](https://www.eiu.com/n/campaigns/democracy-index-2022/) Elff, Martin, and Sebastian Ziaja. 2018. [Method Factors in Democracy Indicators](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Elff%2C+Martin%2C+and+Sebastian+Ziaja.+2018.+Method+Factors+in+Democracy+Indicators.+Politics+and+Governance+6%281%29%3A+105-116.&btnG=). Politics and Governance 6(1): 105-116. Freedom House. 2022. [Freedom in the World 2022 Methodology](http://web.archive.org/web/20220510113257/https://freedomhouse.org/sites/default/files/2022-02/FIW_2022_Methodology_For_Web.pdf). Marshall, Monty G. and Ted Robert Gurr. 2020. [Polity 5: Political Regime Characteristics and Transitions, 1800-2018 Dataset Users’ Manual](https://web.archive.org/web/20220420174443/http://www.systemicpeace.org/inscr/p5manualv2018.pdf). Center for Systemic Peace. McMann, Kelly, Daniel Pemstein, Brigitte Seim, Jan Teorell, and Staffan Lindberg. 2021. [Assessing Data Quality: An Approach and An Application](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C14&q=McMann%2C+Kelly%2C+Daniel+Pemstein%2C+Brigitte+Seim%2C+Jan+Teorell%2C+and+Staffan+Lindberg.+2021.+Assessing+Data+Quality%3A+An+Approach+and+An+Application.+Political+Analysis.&btnG=). _Political Analysis_. Møller, Jørgen and Svend-Erik Skaaning. 2021. [Varieties of Measurement: A Comparative Assessment of Relatively New Democracy Ratings based on Original Data](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=M%C3%B8ller%2C+J%C3%B8rgen+and+Svend-Erik+Skaaning.+2021.+Varieties+of+Measurement%3A+A+Comparative+Assessment+of+Relatively+New+Democracy+Ratings+based+on+Original+Data.+V-Dem+Working+Paper+123.&btnG=). V-Dem Working Paper 123. Skaaning, Svend-Erik. 2018. [Different Types of Data and the Validity of Democracy Measures](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Skaaning%2C+Svend-Erik.+2018.+Different+Types+of+Data+and+the+Validity+of+Democracy+Measures.+Politics+and+Governance+6%281%29%3A+105-116.&btnG=). Politics and Governance 6(1): 105-116.Skaaning, Svend-Erik. 2021. [The Lexical Index of Electoral Democracy (LIED) Dataset (v6.0) Codebook](https://dataverse.harvard.edu/file.xhtml?fileId=4571029&version=1.0). To cover even more of today’s countries when they were still non-sovereign territories we further identified for V-Dem, RoW and slightly for Boix-Miller-Rosato the historical entity the territories were a part of and used that regime’s data whenever available.",Democracy data: how sources differ and when to use which one 1v-HjyDcR2jBAekDJrYpzVtlvv1D24lA5Y2vddu26150,extreme-poverty-in-china-has-been-almost-eliminated-first-in-urban-then-in-rural-regions,data-insight,"{""body"": [{""size"": ""narrow"", ""type"": ""image"", ""filename"": ""china-share-of-population-in-extreme-poverty-desktop.png"", ""parseErrors"": [], ""smallFilename"": ""china-share-of-population-in-extreme-poverty-mobile.png""}, {""type"": ""text"", ""value"": [{""text"": ""In 1981, 97% of people in the Chinese countryside lived in extreme poverty. 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Even in cities, it was more than 70%. Since then, large economic growth has made it possible for hundreds of millions of people in China to leave extreme poverty behind, first in cities and then in the countryside. By 2020, the share of people living in extreme poverty in both urban and rural areas was below 1%. [Explore this data](https://ourworldindata.org/grapher/share-of-population-in-extreme-poverty?tab=chart&country=China+%28rural%29~China+%28urban%29) →","Extreme poverty in China has been almost eliminated — first in urban, then in rural regions" 1urxTFCg1BXBSMSjP4OmiG_iRHJuz5Fp5RSBvq53LtbM,food-ghg-emissions,article,"{""toc"": [{""slug"": ""undefined-livestock-fisheries-account-for-31-of-food-emissions"", ""text"": ""Livestock & fisheries account for 31% of food emissions."", ""title"": ""Livestock & fisheries account for 31% of food emissions."", ""isSubheading"": true}, {""slug"": ""undefined-crop-production-accounts-for-27-of-food-emissions"", ""text"": ""Crop production accounts for 27% of food emissions."", ""title"": ""Crop production accounts for 27% of food emissions."", ""isSubheading"": true}, {""slug"": ""undefined-land-use-accounts-for-24-of-food-emissions"", ""text"": ""Land use accounts for 24% of food emissions."", ""title"": ""Land use accounts for 24% of food emissions."", ""isSubheading"": true}, {""slug"": ""undefined-supply-chains-account-for-18-of-food-emissions"", ""text"": ""Supply chains account for 18% of food emissions."", ""title"": ""Supply chains account for 18% of food emissions."", ""isSubheading"": true}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""When it comes to tackling climate change, the focus tends to be on ‘clean energy’ solutions – the deployment of "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/renewable-energy"", ""children"": [{""text"": ""renewable"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" or nuclear energy; improvements in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/energy-production-and-changing-energy-sources#energy-intensity-of-economies"", ""children"": [{""text"": ""energy efficiency"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""; or transition to low-carbon transport. Indeed, energy, whether in the form of electricity, heat, transport or industrial processes, account for the majority – 76% – of greenhouse gas (GHG) emissions."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But the global food system, which encompasses production, and post-farm process such as processing, and distribution is also a key contributor to emissions. And it’s a problem for which we don’t yet have viable technological solutions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The visualization shown here – based on data from the meta-analysis by Joseph Poore and Thomas Nemecek (2018), published in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Science"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" – summarizes food’s share of total emissions and breaks it down by source."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Food is responsible for approximately 26% of global GHG emissions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are four key elements to consider when trying to quantify food GHG emissions. These are shown by category in the visualization:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""How-much-of-GHGs-come-from-food.png"", ""parseErrors"": []}, {""text"": [{""children"": [{""text"": ""Livestock & fisheries account for 31% of food emissions"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Livestock – animals raised for meat, dairy, eggs and seafood production – contribute to emissions in several ways. Ruminant livestock – mainly cattle – for example, produce methane through their digestive processes (in a process known as ‘enteric fermentation’). Manure management, pasture management, and fuel consumption from fishing vessels also fall into this category. This 31% of emissions relates to on-farm ‘production’ emissions only: it does not include land use change or supply chain emissions from the production of crops for animal feed: these figures are included separately in the other categories."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Crop production accounts for 27% of food emissions."", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""21% of food’s emissions comes from crop production for direct human consumption, and 6% comes from the production of animal feed. They are the direct emissions which result from agricultural production – this includes elements such as the release of nitrous oxide from the application of fertilizers and manure; methane emissions from rice production; and carbon dioxide from agricultural machinery."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Land use accounts for 24% of food emissions."", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Twice as many emissions result from land use for livestock (16%) as for crops for human consumption (8%)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": ""Agricultural expansion results in the conversion of forests, grasslands and other carbon ‘sinks’ into cropland or pasture resulting in carbon dioxide emissions. ‘Land use’ here is the sum of land use change, savannah burning and organic soil cultivation (plowing and overturning of soils)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""children"": [{""text"": ""Supply chains account for 18% of food emissions"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Food processing (converting produce from the farm into final products), transport, packaging and retail all require energy and resource inputs. Many assume that eating local is key to a low-carbon diet, however, transport emissions are often a very small percentage of food’s total emissions – only 6% globally. Whilst supply chain emissions may seem high, at 18%, it’s essential for "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""reducing"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" emissions by preventing food waste. Food waste emissions are large: one-quarter of emissions (3.3 billion tonnes of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": ""eq) from food production ends up as wastage either from supply chain losses or consumers. Durable packaging, refrigeration and food processing can all help to prevent food waste. For example, wastage of processed fruit and vegetables is ~14% lower than fresh, and 8% lower for seafood."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Reducing emissions from food production will be one of our greatest challenges in the coming decades. Unlike many aspects of "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/energy-production-and-changing-energy-sources"", ""children"": [{""text"": ""energy production"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" where viable opportunities for upscaling low-carbon energy –  "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/renewable-energy"", ""children"": [{""text"": ""renewable"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" or nuclear energy –  are available, the ways in which we can decarbonize agriculture are less clear. We need inputs such as fertilizers to meet growing food demands, and we can’t stop cattle from producing methane. We will need a menu of solutions: changes to diets; food waste reduction; improvements in agricultural efficiency; and technologies that make low-carbon food alternatives scalable and affordable."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""07f6e9ed566c49151d4bf50c373c4bc7b09dcc27"": {""id"": ""07f6e9ed566c49151d4bf50c373c4bc7b09dcc27"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""IPCC, 2014: "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://www.ipcc.ch/report/ar5/syr/"", ""children"": [{""text"": ""Climate Change 2014: Synthesis Report"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""children"": [{""text"": "". 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"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Science"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", 360(6392), 987-992."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""2618566afd8a825019e5602549f742d89dd68846"": {""id"": ""2618566afd8a825019e5602549f742d89dd68846"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Gustavsson, G., Cederberg, C., Sonesson, U., Emanuelsson, A. (2013). The methodology of the FAO study: ‘Global food losses and food waste—extent, causes and prevention’ - "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""FAO, 2011. Swedish Institute for Food and Biotechnology (SIK) report 857, SIK"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""51b5593295088024823d3e1aad16cf21ee55748a"": {""id"": ""51b5593295088024823d3e1aad16cf21ee55748a"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""6% of land use change results from conversion from food for human consumption, and 12% for the production of animal feed. Savannah burning (2% of food emissions) is largely burning of bush land in Africa to allow animal grazing. Emissions from cultivated organic soils (4%) are split between human food and animal feed. This is where very high carbon soils are used for cropland, and this releases carbon. It’s a major issue in palm plantations and also in some Northern Hemisphere countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This means food for direct human consumption is equal to 6% (land use change) + 2% cultivated soils = 8%Livestock is equal to 12% (land use change) + 2% savannah burning + 2% cultivated soils = 16%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Food production is responsible for one-quarter of the world’s greenhouse gas emissions"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""One-quarter of the world's greenhouse gas emissions result from food and agriculture. What are the main contributors to food's emissions?"", ""dateline"": ""November 6, 2019"", ""subtitle"": ""One-quarter of the world's greenhouse gas emissions result from food and agriculture. What are the main contributors to food's emissions?"", ""featured-image"": ""Food-GHG-emissions-thumbnail.png""}",1,2023-09-01 15:56:17,2019-11-06 17:00:25,2024-03-18 15:41:59,listed,ALBJ4LsMTPMhBwUp6uJXtQ6FIypJbEI8A3eBxy4Ey1TvC115tO-SE6FLo1xj1OQPKSC22PPPXeDO4BUTLZG8EA,,"When it comes to tackling climate change, the focus tends to be on ‘clean energy’ solutions – the deployment of [renewable](https://ourworldindata.org/renewable-energy) or nuclear energy; improvements in [energy efficiency](https://ourworldindata.org/energy-production-and-changing-energy-sources#energy-intensity-of-economies); or transition to low-carbon transport. Indeed, energy, whether in the form of electricity, heat, transport or industrial processes, account for the majority – 76% – of greenhouse gas (GHG) emissions.1 But the global food system, which encompasses production, and post-farm process such as processing, and distribution is also a key contributor to emissions. And it’s a problem for which we don’t yet have viable technological solutions. The visualization shown here – based on data from the meta-analysis by Joseph Poore and Thomas Nemecek (2018), published in _Science_ – summarizes food’s share of total emissions and breaks it down by source.2 Food is responsible for approximately 26% of global GHG emissions. There are four key elements to consider when trying to quantify food GHG emissions. These are shown by category in the visualization: ### **Livestock & fisheries account for 31% of food emissions**. Livestock – animals raised for meat, dairy, eggs and seafood production – contribute to emissions in several ways. Ruminant livestock – mainly cattle – for example, produce methane through their digestive processes (in a process known as ‘enteric fermentation’). Manure management, pasture management, and fuel consumption from fishing vessels also fall into this category. This 31% of emissions relates to on-farm ‘production’ emissions only: it does not include land use change or supply chain emissions from the production of crops for animal feed: these figures are included separately in the other categories. ### Crop production accounts for 27% of food emissions. 21% of food’s emissions comes from crop production for direct human consumption, and 6% comes from the production of animal feed. They are the direct emissions which result from agricultural production – this includes elements such as the release of nitrous oxide from the application of fertilizers and manure; methane emissions from rice production; and carbon dioxide from agricultural machinery. ### Land use accounts for 24% of food emissions. Twice as many emissions result from land use for livestock (16%) as for crops for human consumption (8%).3Agricultural expansion results in the conversion of forests, grasslands and other carbon ‘sinks’ into cropland or pasture resulting in carbon dioxide emissions. ‘Land use’ here is the sum of land use change, savannah burning and organic soil cultivation (plowing and overturning of soils). ### **Supply chains account for 18% of food emissions**. Food processing (converting produce from the farm into final products), transport, packaging and retail all require energy and resource inputs. Many assume that eating local is key to a low-carbon diet, however, transport emissions are often a very small percentage of food’s total emissions – only 6% globally. Whilst supply chain emissions may seem high, at 18%, it’s essential for _reducing_ emissions by preventing food waste. Food waste emissions are large: one-quarter of emissions (3.3 billion tonnes of CO2eq) from food production ends up as wastage either from supply chain losses or consumers. Durable packaging, refrigeration and food processing can all help to prevent food waste. For example, wastage of processed fruit and vegetables is ~14% lower than fresh, and 8% lower for seafood.4 Reducing emissions from food production will be one of our greatest challenges in the coming decades. Unlike many aspects of [energy production](https://ourworldindata.org/energy-production-and-changing-energy-sources) where viable opportunities for upscaling low-carbon energy –  [renewable](https://ourworldindata.org/renewable-energy) or nuclear energy –  are available, the ways in which we can decarbonize agriculture are less clear. We need inputs such as fertilizers to meet growing food demands, and we can’t stop cattle from producing methane. We will need a menu of solutions: changes to diets; food waste reduction; improvements in agricultural efficiency; and technologies that make low-carbon food alternatives scalable and affordable. IPCC, 2014: _[Climate Change 2014: Synthesis Report](https://www.ipcc.ch/report/ar5/syr/)__. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change_ [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp. Poore, J., & Nemecek, T. (2018). [Reducing food’s environmental impacts through producers and consumers](https://science.sciencemag.org/content/360/6392/987). _Science_, 360(6392), 987-992. Gustavsson, G., Cederberg, C., Sonesson, U., Emanuelsson, A. (2013). The methodology of the FAO study: ‘Global food losses and food waste—extent, causes and prevention’ - _FAO, 2011. Swedish Institute for Food and Biotechnology (SIK) report 857, SIK_. 6% of land use change results from conversion from food for human consumption, and 12% for the production of animal feed. Savannah burning (2% of food emissions) is largely burning of bush land in Africa to allow animal grazing. Emissions from cultivated organic soils (4%) are split between human food and animal feed. This is where very high carbon soils are used for cropland, and this releases carbon. It’s a major issue in palm plantations and also in some Northern Hemisphere countries. This means food for direct human consumption is equal to 6% (land use change) + 2% cultivated soils = 8%Livestock is equal to 12% (land use change) + 2% savannah burning + 2% cultivated soils = 16%.",Food production is responsible for one-quarter of the world’s greenhouse gas emissions 1udPwyCST-n8kvDW-bSaqruKowHnx1nUjKvlj5_526dw,childhood-vaccination-policies,article,"{""toc"": [], ""body"": [{""text"": [{""type"": ""text"", ""value"": [{""text"": ""This is a guest post by "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Tatjana Marks"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Samantha Vanderslott"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "", from the Oxford Vaccine Group and Oxford Martin School, University of Oxford, Centre for Clinical Vaccinology and Tropical Medicine (CCVTM)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": """", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""With the widespread rollout of "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/covid-vaccinations"", ""children"": [{""text"": ""COVID-19 vaccines"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" globally, some countries have started to consider mandatory vaccination, although no country has yet to make vaccines mandatory for its population."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" While COVID-19 has resurfaced the debate on vaccination policies, it has been an important topic for many other diseases. The World Health Organization (WHO) estimates that vaccines save two to three million lives each year (excluding COVID). The development of vaccines against vaccine-preventable childhood diseases has been a key driver in the decline of "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/child-mortality"", ""children"": [{""text"": ""child mortality"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Despite it being such an important topic, it is surprising that information about which countries have mandatory vaccine policy is lacking, and it is childhood vaccines under a country’s national immunization schedules that are most commonly made mandatory."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this article we present a new global dataset which looks at childhood vaccination policies across the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How do childhood vaccination policies vary across the world?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We recently charted mandatory childhood vaccine policies worldwide as they are becoming an increasingly important policy intervention for governments trying to address low vaccination rates."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The term ‘mandatory’ and ‘mandates’ are taken to mean quite different things across countries. Whilst the term is commonly used it is poorly defined."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Mandates require vaccination for a certain purpose, most commonly related to school entry for children. While definitional disagreements still persist, it remains important to better understand what policies are in place across countries and the reasons driving changes in policy over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Our list indicates whether a country has a mandatory vaccination policy for one or more vaccine and the strictness of the mandate on a scale ranging across three levels: mandatory, mandatory for school entry, or recommended. The childhood vaccines include the vaccines that protect from "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/measles-vaccine-coverage-worldwide-vs-measles-cases-worldwide"", ""children"": [{""text"": ""measles,"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" mumps, rubella, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/immunization-coverage-against-diphtheria-tetanus-and-pertussis-dtp3-vs-gdp-per-capita"", ""children"": [{""text"": ""diphtheria, tetanus, pertussis"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/polio-rate-of-cases-vs-vaccination-coverage"", ""children"": [{""text"": ""polio"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", rabies, hepatitis B, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/rotavirus-vaccine"", ""children"": [{""text"": ""rotavirus"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/hib-vaccine"", ""children"": [{""text"": ""haemophilus influenzae type B"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/bcg-immunization-coverage-for-tb-among-1-year-olds?country=~OWID_WRL"", ""children"": [{""text"": ""tuberculosis"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – some of which are administered as combined vaccines. We have classified a country as having a mandatory policy if they mandate for at least one vaccine."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The differences in vaccination policy across the world are shown in the map. By covering 149 countries we could identify some trends around where and why vaccines are mandatory today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/mandatory-childhood-vaccination"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""This is a live dataset that relies on crowdsourcing to note policy changes. If you are aware of any new policies or policy changes for any country please do get in touch at: samantha.vanderslott@paediatrics.ox.ac.uk."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Note"", ""parseErrors"": []}, {""text"": [{""text"": ""How do mandatory vaccination policies vary by region?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We found that assessing policies across WHO regions – European, Americas, Western Pacific, African, and Eastern Mediterranean – was a useful way to break down our analysis of policies worldwide."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart you see a breakdown of the number of countries with a given policy mandate. You can view this by region by using the \""Change region\"" toggle on the interactive chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/number-countries-mandatory-vaccination?country=~OWID_WRL"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Europe has a mixture of mandatory and recommended policies. But most European countries ­– 16 out of 28 – do not have mandatory vaccination. European countries were among the first to introduce mandatory vaccination for smallpox in the early 19"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""th"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}, {""text"": "" century, which also led to early push-back. The early introduction and early push-back, along with present-day approaches to foster mutual trust and responsibility between citizens and the health authorities, may be part of the reason why vaccination is often recommended rather than mandated in many European countries."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Countries of the former-USSR (Union of Soviet Socialist Republics) or under the influence of the Eastern Bloc previously had mandatory vaccination, and many kept this policy in the post-USSR era."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most countries in the Americas – 29 out of 35 – have mandatory vaccinations. In the USA, vaccination is regulated by individual states though it is mandatory for school entry in all of them. In Canada, only three provinces have legislated mandatory vaccination policies that apply to children enrolling in school."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the Western Pacific region, changes to mandatory policies have gone in both directions. Some countries such as Singapore have moved towards mandatory vaccinations whilst others, such as South Korea transitioned away from mandatory to recommended vaccines in 1999. For other countries, mandatory policies are more ambiguous. In China, there is no evidence of specific legislation mandating vaccines, despite many references to such claims in the literature."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It was difficult to obtain information about vaccination policies across African countries, suggesting the absence of specific policies in the region. However, the policies we did find were for mandatory vaccination."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most countries in the Eastern Mediterranean region have mandatory vaccines. Israel is an exception in only recommending vaccination, based on a vaccination schedule outlined by the National Immunization Technical Advisory Group."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are a number of countries in South-East Asia with mandatory vaccination. India is similar to other large countries, with varying policies on mandatory vaccination at the state level. Finally, we note that there are some countries that have mandatory policies at a sub-country level such as in Canadian provinces and Indian and Australian states but we have classified these countries as to the national policy and the policies cover most of the country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What drives the introduction of mandatory vaccinations?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Overall, we found that the occurrence of recent outbreaks is a major factor in the introduction of mandatory vaccination, particularly for high and upper-middle-income countries in Europe. 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Nonetheless, many have still missed their target vaccination rates due to problems with vaccine supply, delivery, and access. In Guyana for example, vaccination is mandatory, yet vaccination coverage is hindered by the management of the supply chain in keeping storage temperatures consistent and the distribution of freeze-sensitive vaccines."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In Nigeria, vaccination is mandatory, and several states have enacted legislation criminalising vaccine refusal. Yet as Onyemelukwe (2016) argues, there are structural, logistical, political, systemic, religious and cultural obstacles to the effective distribution and uptake of vaccines, ranging from cold chain issues, to corruption and security issues."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" There is thus often variation between vaccination in policy compared to in practice."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These findings will be useful to inform policymakers considering the merits of mandatory vaccination:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""1) In the past, an outbreak of a disease (such as measles) has led to introduction of mandatory vaccines even in countries where previously all vaccinations were recommended;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""2) Making a vaccine mandatory should not be the only policy tool but needs to be combined with strong access and availability of vaccines;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""3) It is not just a matter of whether to mandate a vaccine, but how this mandate will be enforced, whether people will comply, and the impact on state-citizen relations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In summary, mandatory vaccination must be considered with caution. 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Reaktion Books."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""cd58c29158f2ca8dbaf899344c7b4d967ecc54ae"": {""id"": ""cd58c29158f2ca8dbaf899344c7b4d967ecc54ae"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Pejin LS. Tightening measures for compliance with vaccination in Serbia ESPN Flash Report. European Commission; 2016."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d31006164700ec13bc1a2c34ba76c124ffc685fb"": {""id"": ""d31006164700ec13bc1a2c34ba76c124ffc685fb"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Attwell, K. and C. Navin, M. (2019) ‘Childhood Vaccination Mandates: Scope, Sanctions, Severity, Selectivity, and Salience’, "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""The Milbank Quarterly"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", 97(4), pp. 978–1014. doi: 10.1111/1468-0009.12417."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Which countries have mandatory childhood vaccination policies?"", ""authors"": [""Tatjana Marks"", ""Samantha Vanderslott""], ""excerpt"": ""How do policies on childhood vaccinations vary across the world?"", ""dateline"": ""June 11, 2021"", ""subtitle"": ""How do policies on childhood vaccinations vary across the world?"", ""sidebar-toc"": false, ""featured-image"": ""child-vaccine-thumbnail.png""}",1,2024-02-08 15:59:17,2021-06-11 10:00:00,2024-03-18 15:41:59,listed,ALBJ4LtNL9kAj-2E6uyqL5vCD0cMsIllvvb76s2Mjluq1itCYUfnB3w5LI54xV7Wzh8HaFBOe4z25tXzpZfXvw,," With the widespread rollout of [COVID-19 vaccines](http://ourworldindata.org/covid-vaccinations) globally, some countries have started to consider mandatory vaccination, although no country has yet to make vaccines mandatory for its population.1 While COVID-19 has resurfaced the debate on vaccination policies, it has been an important topic for many other diseases. The World Health Organization (WHO) estimates that vaccines save two to three million lives each year (excluding COVID). The development of vaccines against vaccine-preventable childhood diseases has been a key driver in the decline of [child mortality](http://ourworldindata.org/child-mortality). Despite it being such an important topic, it is surprising that information about which countries have mandatory vaccine policy is lacking, and it is childhood vaccines under a country’s national immunization schedules that are most commonly made mandatory. In this article we present a new global dataset which looks at childhood vaccination policies across the world. # How do childhood vaccination policies vary across the world? We recently charted mandatory childhood vaccine policies worldwide as they are becoming an increasingly important policy intervention for governments trying to address low vaccination rates.2 The term ‘mandatory’ and ‘mandates’ are taken to mean quite different things across countries. Whilst the term is commonly used it is poorly defined.3 Mandates require vaccination for a certain purpose, most commonly related to school entry for children. While definitional disagreements still persist, it remains important to better understand what policies are in place across countries and the reasons driving changes in policy over time. Our list indicates whether a country has a mandatory vaccination policy for one or more vaccine and the strictness of the mandate on a scale ranging across three levels: mandatory, mandatory for school entry, or recommended. The childhood vaccines include the vaccines that protect from [measles,](https://ourworldindata.org/grapher/measles-vaccine-coverage-worldwide-vs-measles-cases-worldwide) mumps, rubella, [diphtheria, tetanus, pertussis](https://ourworldindata.org/grapher/immunization-coverage-against-diphtheria-tetanus-and-pertussis-dtp3-vs-gdp-per-capita), [polio](https://ourworldindata.org/grapher/polio-rate-of-cases-vs-vaccination-coverage), rabies, hepatitis B, [rotavirus](https://ourworldindata.org/rotavirus-vaccine), [haemophilus influenzae type B](https://ourworldindata.org/grapher/hib-vaccine), and [tuberculosis](https://ourworldindata.org/grapher/bcg-immunization-coverage-for-tb-among-1-year-olds?country=~OWID_WRL) – some of which are administered as combined vaccines. We have classified a country as having a mandatory policy if they mandate for at least one vaccine. The differences in vaccination policy across the world are shown in the map. By covering 149 countries we could identify some trends around where and why vaccines are mandatory today. # How do mandatory vaccination policies vary by region? We found that assessing policies across WHO regions – European, Americas, Western Pacific, African, and Eastern Mediterranean – was a useful way to break down our analysis of policies worldwide. In the chart you see a breakdown of the number of countries with a given policy mandate. You can view this by region by using the ""Change region"" toggle on the interactive chart. Europe has a mixture of mandatory and recommended policies. But most European countries ­– 16 out of 28 – do not have mandatory vaccination. European countries were among the first to introduce mandatory vaccination for smallpox in the early 19th century, which also led to early push-back. The early introduction and early push-back, along with present-day approaches to foster mutual trust and responsibility between citizens and the health authorities, may be part of the reason why vaccination is often recommended rather than mandated in many European countries.4 Countries of the former-USSR (Union of Soviet Socialist Republics) or under the influence of the Eastern Bloc previously had mandatory vaccination, and many kept this policy in the post-USSR era. Most countries in the Americas – 29 out of 35 – have mandatory vaccinations. In the USA, vaccination is regulated by individual states though it is mandatory for school entry in all of them. In Canada, only three provinces have legislated mandatory vaccination policies that apply to children enrolling in school. In the Western Pacific region, changes to mandatory policies have gone in both directions. Some countries such as Singapore have moved towards mandatory vaccinations whilst others, such as South Korea transitioned away from mandatory to recommended vaccines in 1999. For other countries, mandatory policies are more ambiguous. In China, there is no evidence of specific legislation mandating vaccines, despite many references to such claims in the literature.5 It was difficult to obtain information about vaccination policies across African countries, suggesting the absence of specific policies in the region. However, the policies we did find were for mandatory vaccination. Most countries in the Eastern Mediterranean region have mandatory vaccines. Israel is an exception in only recommending vaccination, based on a vaccination schedule outlined by the National Immunization Technical Advisory Group. There are a number of countries in South-East Asia with mandatory vaccination. India is similar to other large countries, with varying policies on mandatory vaccination at the state level. Finally, we note that there are some countries that have mandatory policies at a sub-country level such as in Canadian provinces and Indian and Australian states but we have classified these countries as to the national policy and the policies cover most of the country. # What drives the introduction of mandatory vaccinations? Overall, we found that the occurrence of recent outbreaks is a major factor in the introduction of mandatory vaccination, particularly for high and upper-middle-income countries in Europe. Germany, for example, made measles vaccination mandatory for school and day-care attendance in 2020 following large outbreaks.6 Similarly, Serbia tightened mandatory vaccination laws following a measles outbreak in 2014 to 2015 by introducing harsher penalties.7 Trends of reported cases of measles can be explored in detail [here](https://ourworldindata.org/grapher/reported-cases-of-measles?country=~DEU). Secondly, many low- and lower-middle-income countries have resorted to mandatory vaccination policies because of a lack of other policy options. Nonetheless, many have still missed their target vaccination rates due to problems with vaccine supply, delivery, and access. In Guyana for example, vaccination is mandatory, yet vaccination coverage is hindered by the management of the supply chain in keeping storage temperatures consistent and the distribution of freeze-sensitive vaccines.8 In Nigeria, vaccination is mandatory, and several states have enacted legislation criminalising vaccine refusal. Yet as Onyemelukwe (2016) argues, there are structural, logistical, political, systemic, religious and cultural obstacles to the effective distribution and uptake of vaccines, ranging from cold chain issues, to corruption and security issues.9 There is thus often variation between vaccination in policy compared to in practice. These findings will be useful to inform policymakers considering the merits of mandatory vaccination: 1) In the past, an outbreak of a disease (such as measles) has led to introduction of mandatory vaccines even in countries where previously all vaccinations were recommended; 2) Making a vaccine mandatory should not be the only policy tool but needs to be combined with strong access and availability of vaccines; 3) It is not just a matter of whether to mandate a vaccine, but how this mandate will be enforced, whether people will comply, and the impact on state-citizen relations. In summary, mandatory vaccination must be considered with caution. A country’s past experience with mandates, vaccination services, ability for enforcement, public attitudes, and the current state of disease outbreaks will all play a part in whether mandatory vaccination should be introduced. Gostin, L. O., Salmon, D. A. and Larson, H. J. (2021) ‘Mandating COVID-19 Vaccines’, _JAMA_. American Medical Association, 325(6), p. 532. doi: 10.1001/jama.2020.26553. Vanderslott, S., & Marks, T. (2021). [Charting mandatory vaccination policies worldwide](https://www.sciencedirect.com/science/article/pii/S0264410X21005478). _Vaccine_. Attwell, K. and C. Navin, M. (2019) ‘Childhood Vaccination Mandates: Scope, Sanctions, Severity, Selectivity, and Salience’, _The Milbank Quarterly_, 97(4), pp. 978–1014. doi: 10.1111/1468-0009.12417. Stuart Blume (2017) _Immunization: How Vaccines became Controversial - Stuart Blume - Google Books_. Reaktion Books. Attwell, K., Drislane, S. and Leask, J. (2019) ‘Mandatory vaccination and no fault vaccine injury compensation schemes: An identification of country-level policies’, _Vaccine_. Elsevier Ltd, 37(21), pp. 2843–2848. doi: 10.1016/j.vaccine.2019.03.065. Rezza, G. (2019) ‘Mandatory vaccination for infants and children: the Italian experience’, _Pathogens and Global Health_. Taylor and Francis Ltd., pp. 291–296. doi: 10.1080/20477724.2019.1705021. Pejin LS. Tightening measures for compliance with vaccination in Serbia ESPN Flash Report. European Commission; 2016. UNICEF. Guyana Situation Analysis of Children and Women; 2016. Available at: [https://www.unicef.org/sitan/](https://www.unicef.org/sitan/) [accessed: 10 April 2020]. Onyemelukwe, C. (2016) ‘Can legislation mandating vaccination solve the challenges of routine childhood immunisation in Nigeria?’, _Oxford University Commonwealth Law Journal_. 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In countries such as Nigeria, Mexico, and Indonesia, people put a high value on both these aspects of their lives."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/grapher/how-important-leisure-is-to-people-in-life"", ""children"": [{""text"": ""Explore this data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" →"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""data-insight"", ""title"": ""How much do people value leisure?"", ""authors"": [""Bastian Herre""], ""approved-by"": ""Max Roser"", ""grapher-url"": ""https://ourworldindata.org/grapher/how-important-leisure-is-to-people-in-life""}",1,2024-05-31 11:46:10,2024-07-09 05:00:00,2024-06-04 09:59:43,unlisted,ALBJ4LuLy5tILL-v6sOgZawtMYzs80K2IGyFb8qKw_nh45Bqv76Zg1LVWY7s6LoWW1XrGtBN-8TQ0Ds5KhuuSA,," Free time is important to most people around the world, according to data from the European Values Study and World Values Survey. The chart shows that in most countries, leisure is important to more than 80% of people. However, the percentage of people who find leisure very important in life varies more. In some countries, it is the majority, while in others, it is less than a third. Valuing leisure a lot doesn’t mean that people [value work](https://ourworldindata.org/grapher/how-important-work-is-to-people-in-life) less or [work](https://ourworldindata.org/grapher/annual-working-hours-per-worker?time=latest&country=GBR~DEU~USA~NGA~MEX~IDN~JPN~RUS~BRA~CHN) fewer hours. [Work is important to most people](https://ourworldindata.org/grapher/how-important-work-is-to-people-in-life) globally; but people also value enjoying free time, such as with their [family](https://ourworldindata.org/grapher/how-important-family-is-to-people-in-life) and [friends](https://ourworldindata.org/grapher/how-important-friends-are-to-people-in-life?country=GBR~JPN~USA~DEU~RUS~BRA~CHN~EGY~MEX~IDN~ETH~NGA). [Explore this data](https://ourworldindata.org/grapher/how-important-leisure-is-to-people-in-life) →",How much do people value leisure? 1uEsCtbphuJiGo_Pg8D0rCLnDiy-ApV9gH0aT9ZINpbw,internet,topic-page,"{""toc"": [], ""body"": [{""type"": ""topic-page-intro"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Many of us cannot imagine our lives without the Internet — but the technology "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/internet-history-just-begun"", ""children"": [{""text"": ""is still young"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Only 63% of the world’s population "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-individuals-using-the-internet"", ""children"": [{""text"": ""was online"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in 2023."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Internet provides an almost endless list of services: it allows us to communicate and collaborate worldwide, send money internationally (including "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/personal-remittances-oda"", ""children"": [{""text"": ""remittances"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""), "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/global-education"", ""children"": [{""text"": ""learn and educate"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" others, form cross-border "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/social-connections-and-loneliness"", ""children"": [{""text"": ""social connections"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", share news, and many others."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On this page, you can find all our data, visualizations, and writing relating to the Internet."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": [], ""relatedTopics"": [{""url"": ""https://ourworldindata.org/technological-change"", ""text"": ""Technological Change"", ""type"": ""topic-page-intro-related-topic""}, {""url"": ""https://ourworldindata.org/global-education"", ""text"": ""Global Education"", ""type"": ""topic-page-intro-related-topic""}, {""url"": ""https://ourworldindata.org/social-connections-and-loneliness"", ""text"": ""Loneliness and Social Connections"", ""type"": ""topic-page-intro-related-topic""}, {""url"": ""https://ourworldindata.org/space-exploration-satellites"", ""text"": ""Space Exploration and Satellites"", ""type"": ""topic-page-intro-related-topic""}]}, {""rows"": [], ""type"": ""research-and-writing"", ""primary"": [{""value"": {""url"": ""https://docs.google.com/document/d/1lrN8zZo1tKXiCCI2US1CMkIf0tFSx37d3PU_D89u6Xo/edit""}}], ""secondary"": [{""value"": {""url"": ""https://docs.google.com/document/d/1whkiE8G_Ja1VdRvjyU1L0ptYksxSLnkBajQ5Q_KlNpE/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1oUt6cIxmOGlbZcCbY0846sGXc60AQBO-0LMDJZSUcF8/edit""}}], ""parseErrors"": [], ""hide-authors"": false}, {""top"": [], ""type"": ""all-charts"", ""heading"": ""Interactive Charts on the Internet"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""topic-page"", ""title"": ""Internet"", ""authors"": [""Hannah Ritchie"", ""Edouard Mathieu"", ""Max Roser"", ""Esteban Ortiz-Ospina""], ""excerpt"": ""More than half of the world is online, but the Internet is still young."", ""dateline"": ""April 13, 2023"", ""sticky-nav"": [{""text"": ""Introduction"", ""target"": ""#introduction""}, {""text"": ""Research & Writing"", ""target"": ""#research-writing""}, {""text"": ""Charts"", ""target"": ""#all-charts""}, {""text"": ""Cite This Work"", ""target"": ""#article-citation""}, {""text"": ""Reuse This Work"", ""target"": ""#article-licence""}], ""featured-image"": ""Internet.png""}",1,2024-06-04 07:46:59,2023-04-13 09:10:38,2024-06-04 09:00:18,unlisted,ALBJ4LtkvQ-_bICIfTwhEYgis2nWJNM9DsMcU4UBJny86bHof85ymwmntoSFlltCFL92BAlG9ppwIH_7CBdRXw,,"Many of us cannot imagine our lives without the Internet — but the technology [is still young](https://ourworldindata.org/internet-history-just-begun). Only 63% of the world’s population [was online](https://ourworldindata.org/grapher/share-of-individuals-using-the-internet) in 2023. The Internet provides an almost endless list of services: it allows us to communicate and collaborate worldwide, send money internationally (including [remittances](https://ourworldindata.org/grapher/personal-remittances-oda)), [learn and educate](https://ourworldindata.org/global-education) others, form cross-border [social connections](https://ourworldindata.org/social-connections-and-loneliness), share news, and many others. On this page, you can find all our data, visualizations, and writing relating to the Internet. ## Research & Writing * https://docs.google.com/document/d/1lrN8zZo1tKXiCCI2US1CMkIf0tFSx37d3PU_D89u6Xo/edit ,* https://docs.google.com/document/d/1whkiE8G_Ja1VdRvjyU1L0ptYksxSLnkBajQ5Q_KlNpE/edit ,* https://docs.google.com/document/d/1oUt6cIxmOGlbZcCbY0846sGXc60AQBO-0LMDJZSUcF8/edit ",Internet 1u3-YqXJbBhBxKok2j6sNWSBd0uII2JBnf4pnKhMDEMY,effective-policies-reducing-environmental-impacts-agriculture,article,"{""toc"": [{""slug"": ""if-national-policies-can-have-global-consequences-policymakers-need-to-look-at-global-data"", ""text"": ""If national policies can have global consequences, policymakers need to look at global data"", ""title"": ""If national policies can have global consequences, policymakers need to look at global data"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Agriculture is a difficult problem to solve. It feeds 8 billion people but is also one of the world’s most environmentally damaging sectors. It’s the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/environmental-impacts-of-food"", ""children"": [{""text"": ""leading driver"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of deforestation, biodiversity loss, land use, freshwater withdrawals, and water pollution."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world will need effective governmental policies — called agro-environmental policies — and innovations in sustainable food technologies if we want to reduce these impacts while feeding "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography?facet=none&Metric=Population&Sex=Both+sexes&Age+group=Total&Projection+Scenario=Medium&country=~OWID_WRL"", ""children"": [{""text"": ""9 or 10 billion people"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You might think, then, that the obvious thing to do is to have more and more policies focused on reducing its environmental impacts. But this assumes that all policies are effective and don’t impose trade-offs with food production or socioeconomic outcomes. This is not always the case."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Sri Lanka is a particularly dramatic case showing how rash and poorly designed policies can lead to tragic consequences. In mid-2021, the government "", ""spanType"": ""span-simple-text""}, {""url"": ""https://blogs.lse.ac.uk/businessreview/2022/07/19/what-lies-behind-sri-lankas-collapse/"", ""children"": [{""text"": ""abruptly banned"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" the import of chemical fertilizers. On an agri-environmental policy scorecard, this might have looked good. Fertilizer use — which can cause pollution — plummeted."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But it caused dramatic losses in the country’s food supplies. Rice production "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/global-food?tab=chart&facet=none&Food=Rice&Metric=Production&Per+Capita=false&country=~LKA"", ""children"": [{""text"": ""fell by"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" almost 40% from 2021 to 2022. The production of key export crops, such as tea and rubber, also fell significantly. The country spiraled into an economic crisis. While this crisis is not entirely the result of its fertilizer ban — the import ban was partly in response to economic problems — it made things worse."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The lack of planning or foresight made this policy so damaging. Farmers had no time to find nutrient alternatives or learn how to optimize organic production. It illustrates clearly that just because a country "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""has"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" a policy in place doesn’t mean it produces good outcomes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I’ve written previously about how different national priorities are when it comes to food production. Farmers in most low-income countries "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/africa-yields-problem"", ""children"": [{""text"": ""don’t have access"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" to fertilizers, pesticides, irrigation, or other vital inputs, and their yields suffer as a result. In middle- and high-income countries, farmers often "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/excess-fertilizer"", ""children"": [{""text"": ""overuse fertilizers"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and pesticides, causing lots of water pollution."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Effective policies must consider trade-offs and priorities, not just in terms of national outcomes but also the global environmental and socioeconomic impacts."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this article, I look at global data on agricultural policies, some success stories, and what policymakers need to consider to prevent environmental damage from being offshored to other countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How are national agri-environmental policies distributed across the world?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Agri-environmental policies can target a range of outcomes: fertilizers, pesticides, soil health, forests, and biodiversity, to name a few. They can also be enacted in different ways: as legislation, regulation, payment schemes, or monitoring frameworks."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""David Wuepper and his colleagues collected data on agri-environmental policies in 200 countries from 1960 to 2022. This database was published "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.nature.com/articles/s43016-024-00945-8"", ""children"": [{""text"": ""in a new paper"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Nature Food"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Unsurprisingly, the number of policies has increased over time, with the majority coming into action in the 2000s and 2010s."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the map below, we see the number of policies by country."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Countries in the European Union tend to have the largest number of policies. Countries across Sub-Saharan Africa and some parts of Asia have far fewer. Most countries in the EU have more than 90 policies, compared to less than 20 across most of Africa."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/number-agri-environmental-policies"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""number"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of policies doesn’t tell us how strictly they’re enforced. A single well-implemented policy could be far more effective than 10 poor ones."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To account for this, the researchers developed an intensity-weighted metric. This weighs the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""number"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of policies by the levels of policy stringency and enforcement in a country and the levels of corruption they face."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This intensity-weighted metric is shown in the map below. The overall distribution is similar, which is not surprising. If anything, adjusting for intensity widens the gap between countries. That’s because the metrics are positively correlated, according to the researchers: countries with more policies also tend to have stronger enforcement and lower levels of corruption."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/agri-environmental-policies-intensity"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Richer countries tend to have more agri-environmental policies"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Both metrics — the number and intensity of policies — tend to be higher in richer countries. The chart below shows the number of policies measured against gross domestic product (GDP) per capita."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most countries with large numbers of policies have a high average income. But being rich doesn’t "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""guarantee"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" that a country will put many policies in place. Some — such as Qatar, the United Arab Emirates, and Bahrain — have only a handful. You might think this is because their agricultural sector is small: farming makes up just a "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/agriculture-share-gdp"", ""children"": [{""text"": ""few percent of their GDP"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". But this is also true for most European countries. In the United Kingdom and Germany, it’s less than 1%. In France and Italy, less than 2%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/agri-environmental-policies-gdp"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It’s worth noting that richer countries also "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/fertilizer-consumption-per-hectare-vs-gdp-per-capita"", ""children"": [{""text"": ""tend to use"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" more inputs such as fertilizers and pesticides, which are some of the most regulated parts of agriculture. It makes sense that they would have more targets and legislation to reduce it."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The European Union and the United States use about five times as much fertilizer per hectare as the African average. Within Africa, many countries use much less than this — as little as a few kilograms per hectare. Some lower-income countries use almost no fertilizers or pesticides and have little to regulate. This is shown in the chart below."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/fertilizer-use-per-hectare-of-cropland?tab=chart&time=latest&country=European+Union+%2827%29+%28FAO%29~Africa+%28FAO%29~NGA~USA~UGA~ETH"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Are policies effective in reducing environmental impacts?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We’ll discuss some policy “failures” later. But let’s first examine some clear examples of policies that have effectively achieved their goals."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Countries across Europe have implemented policies to reduce fertilizer use. Many have been successful. As I mentioned earlier, a blanket ban on fertilizers, or even reduction policies in countries where farmers use very little, is likely detrimental. This is not the case in Europe: many farmers still overapply fertilizers, often with little benefit to yields."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart below, you can see the change in total fertilizer consumption since 1990."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Fertilizer use "", ""spanType"": ""span-simple-text""}, {""url"": ""https://agriculture.ec.europa.eu/system/files/2019-07/market-brief-fertilisers_june2019_en_0.pdf"", ""children"": [{""text"": ""dropped steeply"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in the 1990s and the first decade of the 2000s due to reforms in the EU’s Common Agricultural Policy. Consumption dropped by at least one-third, with some countries seeing over 40% reductions. That’s despite most countries "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/cereal-yield?tab=chart&country=FRA~GBR~Western+Europe+%28FAO%29~DEU~NLD"", ""children"": [{""text"": ""having seen an increase or maintenance"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of crop yields over the same period."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/fertilizer-consumption-usda?stackMode=relative&time=1990..latest&country=GBR~FRA~NLD~DEU~Western+Europe"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""China has also managed to turn the tide on fertilizer use. As you can see in the chart below, fertilizer consumption was growing rapidly from the 1960s to the early 2000s. But it peaked around 2015 and is now falling."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""China has focused on using fertilizers more efficiently. To achieve this, it introduced various subsidy programs for farmers. The result has been a reduction in nutrient inputs while yields "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.google.com/url?q=https://ourworldindata.org/explorers/crop-yields?facet%3Dnone%26country%3D~CHN%26Crop%3DWheat%26Metric%3DActual%2Byield&sa=D&source=docs&ust=1712146409922507&usg=AOvVaw0waGHVzr_S8bNFEaKKJlRr"", ""children"": [{""text"": ""continued to increase"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Various scientific papers expect that these policies and the reduction of subsidies played a pivotal role."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/fertilizer-use-per-hectare-of-cropland?tab=chart&country=~CHN"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 2015, China also introduced a “zero-growth pesticide policy” to reduce the overconsumption of pesticides. Again — as you can see from the chart below — it has achieved its goal. What’s impressive is how quickly this happened: use fell immediately, and in less than five years, it has seen an impressive drop. Various monitoring programs across the country have already noticed a reduction in the concentration of pesticides in rivers."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/pesticide-use-tonnes?tab=chart&time=earliest..2019&country=~CHN"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I’ve picked only a few examples, but other cross-country studies find that environmental policies and practices matter. Researchers David Wuepper, Fiona Tang, and Robert Finger find that a third of the differences in pesticide pollution risk between countries can be attributed to their agricultural policies."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When looking at the number and stringency of policies, they find that more than 40% of the differences in soil erosion rates across country borders can be explained by differences in policies."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Policies also explain many differences in forest loss and restoration rates."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Agri-environmental policies do matter. They can make the difference between unsustainable versus efficient use of fertilizers and pesticides, cut rates of soil erosion, and transform countries from net losers to net gainers of forest."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On the other hand, poorly designed policies can backfire when they don’t consider trade-offs with other environmental or socioeconomic problems."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Agri-environmental policies can have spillover impacts to other countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Earlier, we saw the damaging effects of Sri Lanka's abrupt ban on chemical fertilizers. That’s just one example of where policy design — or lack thereof — can go wrong."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Policies can have negative impacts in a few ways. First, they might not consider trade-offs with other environmental issues. As David Wuepper and colleagues note in their study on pesticide reductions, promoting organic farming "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""does"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" lower the risk of pesticide pollution but can also reduce crop yields."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" That means farmers must use more land or displace food production to other countries that might use even more pesticides."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""That brings us to the second point: policies in one country can have negative impacts that spill over to others."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In a study published in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Nature Communications"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", researchers looked at what would happen to greenhouse gas emissions from agriculture if England and Wales went fully organic."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Domestic emissions would fall; in this regard, it would be a policy “win”. But there would also be a significant shortfall in food supplies, requiring the two countries to import more food from elsewhere. When these agricultural emissions are included, total emissions would "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""increase"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". What appears to be a “win” when only considering England and Wales is, in fact, a “loss” for the world — and climate — as a whole."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can imagine similar examples for measures such as land use or forestry. Countries could reduce their farmland area and increase their forest cover while driving more land use and forest loss in other countries. And it’s not just about environmental spillovers: poor policies can also impact food prices, access, and security. Researchers note, for example, that a rise in organic farming in rich countries could raise food prices for consumers in poorer ones."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""If national policies can have global consequences, policymakers need to look at global data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Environmental impacts are mostly offshored when there are large differences in policies across countries. The European Union or the United States can’t offshore deforestation to Brazil if the latter has a zero-deforestation mandate. The United Kingdom can’t displace water pollution to India if it has tight controls on fertilizer and pesticide runoff. If "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""every"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" country had strict environmental policies, it would be hard for any country to offshore its burden elsewhere. The problem is that — as we saw earlier — there "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""are"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" vast differences in the number and enforcement of policies between countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What can richer countries — with stricter domestic policies — do to prevent their impacts from “leaking” elsewhere?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""First, they can’t force other countries to adopt the same policies. In some cases, they would cause a lot of harm. Restricting fertilizer and pesticide use for farmers who can only afford small amounts can ruin their harvests and livelihoods for very little environmental benefit."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What they can do is measure the full impact of their policies and account for any spillovers to other countries. Researchers already do this for carbon dioxide (CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "") emissions from fossil fuels: they estimate “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/production-vs-consumption-co2-emissions"", ""children"": [{""text"": ""consumption-based emissions"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” which adjust for the CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/consumption-based-co2"", ""children"": [{""text"": ""embedded in the goods and services"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" they import. This tells us how much of their emissions are being offshored and whether they are reducing their emissions when this is considered."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is harder for metrics such as deforestation, land use, fertilizer, or pesticide consumption, but many organizations are making progress. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://trase.earth/"", ""children"": [{""text"": ""Trase Earth"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", for example, uses trade and geospatial data to map deforestation in the supply chains of products such as beef, palm oil, soy, and cocoa. Countries and companies can then see where their products are coming from and whether they’ve increased the risk of deforestation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Some countries are starting to implement policies that tackle international impacts. In 2018, for example, France launched its "", ""spanType"": ""span-simple-text""}, {""url"": ""https://trase.earth/insights/supporting-france-s-plan-to-increase-transparency-over-deforestation"", ""children"": [{""text"": ""National Strategy against Imported Deforestation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", which commits it to ending the imports of unsustainable products by 2030. Next year, the EU "", ""spanType"": ""span-simple-text""}, {""url"": ""https://environment.ec.europa.eu/topics/forests/deforestation/regulation-deforestation-free-products_en"", ""children"": [{""text"": ""will ban the sale"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of seven imported commodities — beef, soy, palm oil, wood, cocoa, coffee, and rubber — if grown on recently deforested land. Even then, the total impacts are not straightforward: this could reduce environmental pressures but negatively impact smallholder farmers in other countries if they lose some of their export markets."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Effective commitments – that provide support for farmers who need to adapt to new policies – will be crucial to ensure that countries are not only improving the environment at home, but also contributing to more sustainable practices for the world as a whole."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""Many thanks to Max Roser, Edouard Mathieu and David Wuepper for their valuable feedback and comments on this article."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Acknowledgements"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""43c90c61be48aa1f52d0cd22fd01d188ea6ca5d8"": {""id"": ""43c90c61be48aa1f52d0cd22fd01d188ea6ca5d8"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Guo, Z., Ouyang, W., Chen, M., Tulcan, R. 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Global Environmental Change."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""6ee649f6d787ec05aea5e5aaed8d674ee9fd2271"": {""id"": ""6ee649f6d787ec05aea5e5aaed8d674ee9fd2271"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Wuepper, D., Wiebecke, I., Meier, L., Vogelsanger, S., Bramato, S., Fürholz, A., & Finger, R. (2024). Agri-environmental policies from 1960 to 2022. Nature Food."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""74fc38a3afdcf3578096c628d3bf0ff0e6b8db33"": {""id"": ""74fc38a3afdcf3578096c628d3bf0ff0e6b8db33"", ""index"": 9, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Mérel, P., Qin, Z., & Sexton, R. J. (2023). Policy-induced expansion of organic farmland: implications for food prices and welfare. 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How do chemical fertilizer reduction policies work?—Empirical evidence from rural China. Frontiers in Environmental Science."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Van Wesenbeeck, C. F. A., Keyzer, M. A., Van Veen, W. C. M., & Qiu, H. (2021). Can China's overuse of fertilizer be reduced without threatening food security and farm incomes?. Agricultural Systems."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Fan, P., Mishra, A. K., Feng, S., & Su, M. (2023). The effect of agricultural subsidies on chemical fertilizer use: Evidence from a new policy in China. Journal of Environmental Management, 344, 118423."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a41224322ad48ea44791670493c42171d8a9f06b"": {""id"": ""a41224322ad48ea44791670493c42171d8a9f06b"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Samarakoon, L. P. (2024). What broke the pearl of the Indian ocean? The causes of the Sri Lankan economic crisis and its policy implications. Journal of Financial Stability."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""cc20f7713300fda598a2bf451c79405b388f6915"": {""id"": ""cc20f7713300fda598a2bf451c79405b388f6915"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Wuepper, D., Tang, F. H., & Finger, R. (2023). National leverage points to reduce global pesticide pollution. 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Nature Communications."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""dd613d85a09d9a3765c42d3817899031dd32f438"": {""id"": ""dd613d85a09d9a3765c42d3817899031dd32f438"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Policies implemented by the European Union are also included in national totals for EU countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""How effective are policies in reducing the environmental impacts of agriculture?"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""All countries now have policies, but not all work as intended. Some drive trade-offs or lead to spillover impacts elsewhere, but there are many examples of successful stories."", ""subtitle"": ""All countries now have policies, but not all work as intended. Some drive trade-offs or lead to spillover impacts elsewhere, but there are many examples of successful stories."", ""atom-title"": ""How effective are policies in reducing the environmental impacts of agriculture?"", ""atom-excerpt"": ""All countries now have policies, but not all work as intended. Some drive trade-offs or lead to spillover impacts elsewhere, but there are many examples of successful stories."", ""featured-image"": ""agri-policies-thumbnail.png""}",1,2024-03-27 11:47:20,2024-04-22 14:25:03,2024-04-22 14:23:39,listed,ALBJ4LuNcdX6xabYv4WqUDWPb5wEWq2vX_jiqjlgSMRlJeF3rSXYCJ2Q06IS_0C7enRC5Owi-dAapDt8UbdlgA,,"Agriculture is a difficult problem to solve. It feeds 8 billion people but is also one of the world’s most environmentally damaging sectors. It’s the [leading driver](https://ourworldindata.org/environmental-impacts-of-food) of deforestation, biodiversity loss, land use, freshwater withdrawals, and water pollution. The world will need effective governmental policies — called agro-environmental policies — and innovations in sustainable food technologies if we want to reduce these impacts while feeding [9 or 10 billion people](https://ourworldindata.org/explorers/population-and-demography?facet=none&Metric=Population&Sex=Both+sexes&Age+group=Total&Projection+Scenario=Medium&country=~OWID_WRL). You might think, then, that the obvious thing to do is to have more and more policies focused on reducing its environmental impacts. But this assumes that all policies are effective and don’t impose trade-offs with food production or socioeconomic outcomes. This is not always the case. Sri Lanka is a particularly dramatic case showing how rash and poorly designed policies can lead to tragic consequences. In mid-2021, the government [abruptly banned](https://blogs.lse.ac.uk/businessreview/2022/07/19/what-lies-behind-sri-lankas-collapse/) the import of chemical fertilizers. On an agri-environmental policy scorecard, this might have looked good. Fertilizer use — which can cause pollution — plummeted. But it caused dramatic losses in the country’s food supplies. Rice production [fell by](https://ourworldindata.org/explorers/global-food?tab=chart&facet=none&Food=Rice&Metric=Production&Per+Capita=false&country=~LKA) almost 40% from 2021 to 2022. The production of key export crops, such as tea and rubber, also fell significantly. The country spiraled into an economic crisis. While this crisis is not entirely the result of its fertilizer ban — the import ban was partly in response to economic problems — it made things worse.1 The lack of planning or foresight made this policy so damaging. Farmers had no time to find nutrient alternatives or learn how to optimize organic production. It illustrates clearly that just because a country _has_ a policy in place doesn’t mean it produces good outcomes. I’ve written previously about how different national priorities are when it comes to food production. Farmers in most low-income countries [don’t have access](https://ourworldindata.org/africa-yields-problem) to fertilizers, pesticides, irrigation, or other vital inputs, and their yields suffer as a result. In middle- and high-income countries, farmers often [overuse fertilizers](https://ourworldindata.org/excess-fertilizer) and pesticides, causing lots of water pollution. Effective policies must consider trade-offs and priorities, not just in terms of national outcomes but also the global environmental and socioeconomic impacts. In this article, I look at global data on agricultural policies, some success stories, and what policymakers need to consider to prevent environmental damage from being offshored to other countries. # How are national agri-environmental policies distributed across the world? Agri-environmental policies can target a range of outcomes: fertilizers, pesticides, soil health, forests, and biodiversity, to name a few. They can also be enacted in different ways: as legislation, regulation, payment schemes, or monitoring frameworks. David Wuepper and his colleagues collected data on agri-environmental policies in 200 countries from 1960 to 2022. This database was published [in a new paper](https://www.nature.com/articles/s43016-024-00945-8) in _Nature Food_.2 Unsurprisingly, the number of policies has increased over time, with the majority coming into action in the 2000s and 2010s. In the map below, we see the number of policies by country.3 Countries in the European Union tend to have the largest number of policies. Countries across Sub-Saharan Africa and some parts of Asia have far fewer. Most countries in the EU have more than 90 policies, compared to less than 20 across most of Africa. The _number_ of policies doesn’t tell us how strictly they’re enforced. A single well-implemented policy could be far more effective than 10 poor ones. To account for this, the researchers developed an intensity-weighted metric. This weighs the _number_ of policies by the levels of policy stringency and enforcement in a country and the levels of corruption they face. This intensity-weighted metric is shown in the map below. The overall distribution is similar, which is not surprising. If anything, adjusting for intensity widens the gap between countries. That’s because the metrics are positively correlated, according to the researchers: countries with more policies also tend to have stronger enforcement and lower levels of corruption. # Richer countries tend to have more agri-environmental policies Both metrics — the number and intensity of policies — tend to be higher in richer countries. The chart below shows the number of policies measured against gross domestic product (GDP) per capita. Most countries with large numbers of policies have a high average income. But being rich doesn’t _guarantee_ that a country will put many policies in place. Some — such as Qatar, the United Arab Emirates, and Bahrain — have only a handful. You might think this is because their agricultural sector is small: farming makes up just a [few percent of their GDP](https://ourworldindata.org/grapher/agriculture-share-gdp). But this is also true for most European countries. In the United Kingdom and Germany, it’s less than 1%. In France and Italy, less than 2%. It’s worth noting that richer countries also [tend to use](https://ourworldindata.org/grapher/fertilizer-consumption-per-hectare-vs-gdp-per-capita) more inputs such as fertilizers and pesticides, which are some of the most regulated parts of agriculture. It makes sense that they would have more targets and legislation to reduce it. The European Union and the United States use about five times as much fertilizer per hectare as the African average. Within Africa, many countries use much less than this — as little as a few kilograms per hectare. Some lower-income countries use almost no fertilizers or pesticides and have little to regulate. This is shown in the chart below. # Are policies effective in reducing environmental impacts? We’ll discuss some policy “failures” later. But let’s first examine some clear examples of policies that have effectively achieved their goals. Countries across Europe have implemented policies to reduce fertilizer use. Many have been successful. As I mentioned earlier, a blanket ban on fertilizers, or even reduction policies in countries where farmers use very little, is likely detrimental. This is not the case in Europe: many farmers still overapply fertilizers, often with little benefit to yields. In the chart below, you can see the change in total fertilizer consumption since 1990.4 Fertilizer use [dropped steeply](https://agriculture.ec.europa.eu/system/files/2019-07/market-brief-fertilisers_june2019_en_0.pdf) in the 1990s and the first decade of the 2000s due to reforms in the EU’s Common Agricultural Policy. Consumption dropped by at least one-third, with some countries seeing over 40% reductions. That’s despite most countries [having seen an increase or maintenance](https://ourworldindata.org/grapher/cereal-yield?tab=chart&country=FRA~GBR~Western+Europe+%28FAO%29~DEU~NLD) of crop yields over the same period. China has also managed to turn the tide on fertilizer use. As you can see in the chart below, fertilizer consumption was growing rapidly from the 1960s to the early 2000s. But it peaked around 2015 and is now falling. China has focused on using fertilizers more efficiently. To achieve this, it introduced various subsidy programs for farmers. The result has been a reduction in nutrient inputs while yields [continued to increase](https://www.google.com/url?q=https://ourworldindata.org/explorers/crop-yields?facet%3Dnone%26country%3D~CHN%26Crop%3DWheat%26Metric%3DActual%2Byield&sa=D&source=docs&ust=1712146409922507&usg=AOvVaw0waGHVzr_S8bNFEaKKJlRr). Various scientific papers expect that these policies and the reduction of subsidies played a pivotal role.5 In 2015, China also introduced a “zero-growth pesticide policy” to reduce the overconsumption of pesticides. Again — as you can see from the chart below — it has achieved its goal. What’s impressive is how quickly this happened: use fell immediately, and in less than five years, it has seen an impressive drop. Various monitoring programs across the country have already noticed a reduction in the concentration of pesticides in rivers.6 I’ve picked only a few examples, but other cross-country studies find that environmental policies and practices matter. Researchers David Wuepper, Fiona Tang, and Robert Finger find that a third of the differences in pesticide pollution risk between countries can be attributed to their agricultural policies.7 When looking at the number and stringency of policies, they find that more than 40% of the differences in soil erosion rates across country borders can be explained by differences in policies.2 Policies also explain many differences in forest loss and restoration rates.8 Agri-environmental policies do matter. They can make the difference between unsustainable versus efficient use of fertilizers and pesticides, cut rates of soil erosion, and transform countries from net losers to net gainers of forest. On the other hand, poorly designed policies can backfire when they don’t consider trade-offs with other environmental or socioeconomic problems. # Agri-environmental policies can have spillover impacts to other countries Earlier, we saw the damaging effects of Sri Lanka's abrupt ban on chemical fertilizers. That’s just one example of where policy design — or lack thereof — can go wrong. Policies can have negative impacts in a few ways. First, they might not consider trade-offs with other environmental issues. As David Wuepper and colleagues note in their study on pesticide reductions, promoting organic farming _does_ lower the risk of pesticide pollution but can also reduce crop yields.7 That means farmers must use more land or displace food production to other countries that might use even more pesticides. That brings us to the second point: policies in one country can have negative impacts that spill over to others. In a study published in _Nature Communications_, researchers looked at what would happen to greenhouse gas emissions from agriculture if England and Wales went fully organic.9 Domestic emissions would fall; in this regard, it would be a policy “win”. But there would also be a significant shortfall in food supplies, requiring the two countries to import more food from elsewhere. When these agricultural emissions are included, total emissions would _increase_. What appears to be a “win” when only considering England and Wales is, in fact, a “loss” for the world — and climate — as a whole. You can imagine similar examples for measures such as land use or forestry. Countries could reduce their farmland area and increase their forest cover while driving more land use and forest loss in other countries. And it’s not just about environmental spillovers: poor policies can also impact food prices, access, and security. Researchers note, for example, that a rise in organic farming in rich countries could raise food prices for consumers in poorer ones.10 ## If national policies can have global consequences, policymakers need to look at global data Environmental impacts are mostly offshored when there are large differences in policies across countries. The European Union or the United States can’t offshore deforestation to Brazil if the latter has a zero-deforestation mandate. The United Kingdom can’t displace water pollution to India if it has tight controls on fertilizer and pesticide runoff. If _every_ country had strict environmental policies, it would be hard for any country to offshore its burden elsewhere. The problem is that — as we saw earlier — there _are_ vast differences in the number and enforcement of policies between countries. What can richer countries — with stricter domestic policies — do to prevent their impacts from “leaking” elsewhere? First, they can’t force other countries to adopt the same policies. In some cases, they would cause a lot of harm. Restricting fertilizer and pesticide use for farmers who can only afford small amounts can ruin their harvests and livelihoods for very little environmental benefit. What they can do is measure the full impact of their policies and account for any spillovers to other countries. Researchers already do this for carbon dioxide (CO2) emissions from fossil fuels: they estimate “[consumption-based emissions](https://ourworldindata.org/grapher/production-vs-consumption-co2-emissions)” which adjust for the CO2 [embedded in the goods and services](https://ourworldindata.org/consumption-based-co2) they import. This tells us how much of their emissions are being offshored and whether they are reducing their emissions when this is considered. This is harder for metrics such as deforestation, land use, fertilizer, or pesticide consumption, but many organizations are making progress. [Trase Earth](https://trase.earth/), for example, uses trade and geospatial data to map deforestation in the supply chains of products such as beef, palm oil, soy, and cocoa. Countries and companies can then see where their products are coming from and whether they’ve increased the risk of deforestation. Some countries are starting to implement policies that tackle international impacts. In 2018, for example, France launched its [National Strategy against Imported Deforestation](https://trase.earth/insights/supporting-france-s-plan-to-increase-transparency-over-deforestation), which commits it to ending the imports of unsustainable products by 2030. Next year, the EU [will ban the sale](https://environment.ec.europa.eu/topics/forests/deforestation/regulation-deforestation-free-products_en) of seven imported commodities — beef, soy, palm oil, wood, cocoa, coffee, and rubber — if grown on recently deforested land. Even then, the total impacts are not straightforward: this could reduce environmental pressures but negatively impact smallholder farmers in other countries if they lose some of their export markets. Effective commitments – that provide support for farmers who need to adapt to new policies – will be crucial to ensure that countries are not only improving the environment at home, but also contributing to more sustainable practices for the world as a whole. Samarakoon, L. P. (2024). What broke the pearl of the Indian ocean? The causes of the Sri Lankan economic crisis and its policy implications. Journal of Financial Stability. Wuepper, D., Wiebecke, I., Meier, L., Vogelsanger, S., Bramato, S., Fürholz, A., & Finger, R. (2024). Agri-environmental policies from 1960 to 2022. Nature Food. Policies implemented by the European Union are also included in national totals for EU countries. Official governmental statistics — for example, [in the UK](https://www.gov.uk/government/statistics/british-survey-of-fertiliser-practice-2022) — confirm the same reduction in fertilizer use. Yang, Y., Li, Z., & Jin, M. (2022). How do chemical fertilizer reduction policies work?—Empirical evidence from rural China. Frontiers in Environmental Science. Van Wesenbeeck, C. F. A., Keyzer, M. A., Van Veen, W. C. M., & Qiu, H. (2021). Can China's overuse of fertilizer be reduced without threatening food security and farm incomes?. Agricultural Systems. Fan, P., Mishra, A. K., Feng, S., & Su, M. (2023). The effect of agricultural subsidies on chemical fertilizer use: Evidence from a new policy in China. Journal of Environmental Management, 344, 118423. Guo, Z., Ouyang, W., Chen, M., Tulcan, R. X. S., Wang, L., Lin, C., & He, M. (2023). Increasing precipitation deteriorates the progress of pesticide reduction policy in the vulnerable watershed. In _npj Clean Water_. Wuepper, D., Tang, F. H., & Finger, R. (2023). National leverage points to reduce global pesticide pollution. Global Environmental Change. Wuepper, D., Crowther, T., Lauber, T., Routh, D., Le Clec'h, S., Garrett, R. D., & Börner, J. (2024). Public policies and global forest conservation: Empirical evidence from national borders. Global Environmental Change. Smith, L. G., Kirk, G. J., Jones, P. J., & Williams, A. G. (2019). The greenhouse gas impacts of converting food production in England and Wales to organic methods. Nature Communications. Mérel, P., Qin, Z., & Sexton, R. J. (2023). Policy-induced expansion of organic farmland: implications for food prices and welfare. European Review of Agricultural Economics.",How effective are policies in reducing the environmental impacts of agriculture? 1tqoeu-Qe0qvQi2FwhI5xWhxQbR1hzgxJ-ZjzetJFj_s,marriages-and-divorces,linear-topic-page,"{""toc"": [{""slug"": ""marriages-are-becoming-less-common"", ""text"": ""Marriages are becoming less common"", ""title"": ""Marriages are becoming less common"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""in-many-countries-marriage-rates-are-declining"", ""text"": ""In many countries, marriage rates are declining"", ""title"": ""In many countries, marriage rates are declining"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""marriages-across-cohorts-have-declined"", ""text"": ""Marriages across cohorts have declined"", ""title"": ""Marriages across cohorts have declined"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""people-are-marrying-later"", ""text"": ""People are marrying later"", ""title"": ""People are marrying later"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""there-has-been-a-decoupling-of-parenthood-and-marriage"", ""text"": ""There has been a ‘decoupling’ of parenthood and marriage"", ""title"": ""There has been a ‘decoupling’ of parenthood and marriage"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""the-share-of-children-born-outside-of-marriage-has-increased-substantially-in-almost-all-oecd-countries"", ""text"": ""The share of children born outside of marriage has increased substantially in almost all OECD countries"", ""title"": ""The share of children born outside of marriage has increased substantially in almost all OECD countries"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""globally-the-percentage-of-women-in-either-marriage-or-cohabitation-is-decreasing-but-only-slightly"", ""text"": ""Globally, the percentage of women in either marriage or cohabitation is decreasing, but only slightly"", ""title"": ""Globally, the percentage of women in either marriage or cohabitation is decreasing, but only slightly"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""single-parenting-is-common-and-in-many-countries-it-has-increased-in-recent-decades"", ""text"": ""Single parenting is common, and in many countries, it has increased in recent decades"", ""title"": ""Single parenting is common, and in many countries, it has increased in recent decades"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""same-sex-marriage-has-become-possible-in-many-countries"", ""text"": ""Same-sex marriage has become possible in many countries"", ""title"": ""Same-sex marriage has become possible in many countries"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""where-are-same-sex-marriages-legal"", ""text"": ""Where are same-sex marriages legal?"", ""title"": ""Where are same-sex marriages legal?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""some-perspective-on-the-progress-made-regarding-marriage-equality"", ""text"": ""Some perspective on the progress made regarding marriage equality"", ""title"": ""Some perspective on the progress made regarding marriage equality"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""marriage-trends-show-that-social-institutions-can-and-often-do-change-quickly"", ""text"": ""Marriage trends show that social institutions can, and often do change quickly"", ""title"": ""Marriage trends show that social institutions can, and often do change quickly"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""divorce-rates-increased-after-1970-in-recent-decades-the-trends-very-much-differ-between-countries"", ""text"": ""Divorce rates increased after 1970 – in recent decades the trends very much differ between countries"", ""title"": ""Divorce rates increased after 1970 – in recent decades the trends very much differ between countries"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""trends-in-the-rate-of-divorces-relative-to-the-size-of-the-population"", ""text"": ""Trends in the rate of divorces relative to the size of the population"", ""title"": ""Trends in the rate of divorces relative to the size of the population"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""the-share-of-marriages-ending-in-divorce"", ""text"": ""The share of marriages ending in divorce"", ""title"": ""The share of marriages ending in divorce"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""divorces-by-age-and-cohort"", ""text"": ""Divorces by age and cohort"", ""title"": ""Divorces by age and cohort"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""marriages-in-many-countries-are-getting-longer"", ""text"": ""Marriages in many countries are getting longer"", ""title"": ""Marriages in many countries are getting longer"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""all-charts"", ""text"": ""Interactive charts on Marriages and Divorces"", ""title"": ""Interactive charts on Marriages and Divorces"", ""isSubheading"": false}, {""slug"": ""article-endnotes"", ""text"": ""Endnotes"", ""title"": ""Endnotes"", ""isSubheading"": false}, {""slug"": ""article-citation"", ""text"": ""Citation"", ""title"": ""Citation"", ""isSubheading"": false}, {""slug"": ""article-licence"", ""text"": ""Licence"", ""title"": ""Licence"", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Marriage, as a social institution, has been around for thousands of years."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" With things that are thousands of years old, it is easy to assume that they can only change slowly. But developments since the middle of the 20th century show that this assumption is wrong: in many countries, marriages are becoming less common, people are marrying later, unmarried couples are increasingly choosing to live together, and in many countries, we are seeing a ‘decoupling’ of parenthood and marriage. Within the last decades the institution of marriage has changed more than in thousands of years before."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Here we present the data behind these fast and widespread changes and discuss some of the main drivers behind them."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Marriages are becoming less common"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""In many countries, marriage rates are declining"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The proportion of people who are getting married is going down in many countries across the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart here shows this trend for a selection of countries. It combines data from multiple sources, including statistical country offices and reports from the UN, Eurostat, and the OECD."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/marriage-rate-per-1000-inhabitants?country=ARG~AUS~BOL~ITA~KOR~GBR~USA"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For the US we have data on marriage rates going back to the start of the 20th century. This lets us see when the decline started, and trace the influence of social and economic changes during the process."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""In 1920, shortly after the First World War, there were 12 marriages annually for every 1,000 people in the US. Marriages in the US then were almost twice as common as today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the 1930s, during the Great Depression, the rate fell sharply. In the 1930s marriages became again more common and in 1946 – the year after the Second World War ended – marriages reached a peak of 16.4 marriages per 1,000 people."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Marriage rates fell again in the 1950s and then bounced back in the 1960s."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The long decline started in the 1970s. Since 1972, marriage rates in the US have fallen by almost 50%, and are currently at the lowest point in recorded history."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart also shows that in comparison to other rich countries, the US has had particularly high historical marriage rates. But in terms of changes over time, the trend looks similar for other rich countries. The UK and Australia, for example, have also seen marriage rates declining for decades, and are currently at the lowest point in recorded history."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For non-rich countries the data is sparse, but available estimates from Latin America, Africa, and Asia suggest that the decline of marriages is not exclusive to rich countries. Over the period 1990 - 2010, there was "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/marriage-rate-1990-vs-2010"", ""children"": [{""text"": ""a decline"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in marriage rates in the majority of countries around the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But there’s still a lot of cross-country variation around this general trend, and in some countries, changes are going in the opposite direction."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Marriages across cohorts have declined"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart looks at the change in marriages from a different angle and answers the question: How likely were people of different generations to be married by a given age?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In many rich countries there are statistical records going back several generations, allowing us to estimate marriage rates by age and year of birth. The chart here uses those records to give marriage rates by age and year of birth for five cohorts of men in England and Wales."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For instance, you can look at 30-year-olds, and see what percentage of them in each cohort was married. Of those men who were born in 1940, about 83% were married by age 30. Among those born in 1980 only about 25% were married by age 30."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The trend is stark. English men in more recent cohorts are much less likely to have married, and that’s true at all ages."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are two causes for this: an increasing share of people in younger cohorts are not getting married; and younger cohorts are increasingly choosing to marry later in life. We explore this second point below."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Share of men in England and Wales who were married by a certain age"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Share-of-men-married-by-age-England-and-Wales.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""People are marrying later"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In many countries, declining marriage rates have been accompanied by an increase in the age at which people are getting married. This is shown in the chart here, where we plot the average age of women at first marriage."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The increase in the age at which people are getting married is stronger in richer countries, particularly in North America and Europe."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In countries in sub-Saharan Africa, the average age at marriage has increased less or broadly remained unchanged."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/age-at-marriage-women"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""More people marrying later means that a greater share of young people are unmarried."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""According to the British census of 1971 about 85% of women between the age of 25 and 29 were married, as this chart shows. By 2011 that figure had declined to 58%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For older people the trend is reversed – the share of older women who never got married is declining. In the 1971 census, the share of women 60-64 who had ever been married was lower than it is for women in that age bracket in the decades since."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can create similar charts for both men and women across all countries, using the "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://population.un.org/MarriageData/Index.html#/maritalStatusChart"", ""children"": [{""text"": ""UN World Marriage Data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""text"": "" "", ""spanType"": ""span-simple-text""}, {""url"": ""https://population.un.org/MarriageData/Index.html#/maritalStatusChart"", ""children"": [{""text"": ""site"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". This lets you explore in more detail the distribution of marriages by age across time, for both men and women."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Share of women who were ever married in the UK at a given age"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Share-of-women-that-were-ever-married-by-age.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""There has been a ‘decoupling’ of parenthood and marriage"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""The share of children born outside of marriage has increased substantially in almost all OECD countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""An arrangement where two or more people are not married but live together is referred to as cohabitation. In recent decades cohabitation has become increasingly common around the world. In the US, for example, the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""US Census Bureau "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""url"": ""https://www.census.gov/library/stories/2018/11/cohabitaiton-is-up-marriage-is-down-for-young-adults.html"", ""children"": [{""text"": ""estimates"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that the share of young adults between the ages of 18 and 24 living with an unmarried partner went up from 0.1% to 9.4% over the period 1968-2018; and according to a "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.pewsocialtrends.org/2019/11/06/marriage-and-cohabitation-in-the-u-s/"", ""children"": [{""text"": ""survey"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" from Pew Research, today most Americans favor allowing unmarried couples to have the same legal rights as married couples."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The increase in cohabitation is the result of the two changes that we discussed above: fewer people are choosing to marry and those people who do get married tend to do so when they are older and often live with their partner before getting married. In the UK, for example, 85% of people who get married cohabited first."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Long-run data on the share of people living in cohabitation across countries is not available, but some related data points are: in particular, the proportion of births outside marriage provides a relevant proxy measure, allowing comparisons across countries and time; if more unmarried people are having children, it suggests that more people are entering long-term cohabiting relationships without first getting married. It isn’t a perfect proxy – as we’ll see below, rates of single parenting have also changed, meaning that rates of births outside marriage will not match perfectly with cohabitation rates – but it provides some information regarding the direction of change."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart here shows the percentage of all children who were born to unmarried parents."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we can see, the share of children born outside of marriage has increased substantially in almost all OECD countries in recent decades. The exception is Japan, where there has been only a very minor increase."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 1970, most OECD countries saw less than 10% of children born outside of marriage. In 2014, the share had increased to more than 20% in most countries, and to more than half in some."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The trend is not restricted to very rich countries. In Mexico and Costa Rica, for example, the increase has been very large, and today the majority of children are born to unmarried parents."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-births-outside-marriage"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Globally, the percentage of women in either marriage or cohabitation is decreasing, but only slightly"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In recent decades there has been a decline in global marriage rates, and at the same time, there has been an increase in cohabitation. What’s the combined effect if we consider marriage and cohabitation together?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart below plots estimates and projections, from the UN Population Division, for the percentage of women of reproductive age (15 to 49 years) who are either married or living with an unmarried partner."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Overall, the trend shows a global decline – but only a relatively small one, from 69% in 1970 to 64% projected for 2020. At any given point in the last five decades, around two-thirds of all women were married or cohabitated."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are differences between regions. In East Asia, the share of women who are married or in a cohabiting union increased, in South America the share is flat, and in North America and North Europe it declined."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-women-married-or-in-a-union"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Single parenting is common, and in many countries, it has increased in recent decades"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart shows the share of households of a single parent living with dependent children. There are large differences between countries."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-single-parent-families"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The causes and situations leading to single parenting are varied, and unsurprisingly, single-parent families are very diverse in terms of socio-economic background and living arrangements, across countries, within countries, and over time. However, there are some common patterns:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""numbered-list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""Women head the majority of single-parent households, and this gender gap tends to be stronger for parents of younger children. Across OECD countries, about 12% of children aged 0-5 years live with a single parent; 92% of these live with their mother."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Single-parent households are among the most financially vulnerable groups. This is true even in rich countries. According to "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ec.europa.eu/social/main.jsp?catId=738&langId=en&pubId=8234&furtherPubs=yes"", ""children"": [{""text"": ""Eurostat data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", across European countries, 47% of single-parent households were “at risk of poverty or social exclusion” in 2017, compared with 21% of two-parent households."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Single parenting was probably more common a couple of centuries ago. But single parenting back then was often caused by "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/maternal-mortality"", ""children"": [{""text"": ""high maternal mortality"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" rather than choice or relationship breakdown; it was also typically short in duration since remarriage rates were high."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Same-sex marriage has become possible in many countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Marriage equality is increasingly considered a human and civil right, with important political, social, and religious implications around the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 1989, Denmark became the first country to recognize a legal relationship for same-sex couples, establishing ‘registered partnerships’ granting those in same-sex relationships "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.nytimes.com/1989/10/02/world/rights-for-gay-couples-in-denmark.html"", ""children"": [{""text"": ""most of the rights"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" given to married heterosexuals."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It took more than a decade for same-sex marriage to be legal anywhere in the world. In December 2000, the Netherlands became the first country to establish same-sex marriage by law."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the first two decades of the 21st century legislation has quickly spread across more countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Where are same-sex marriages legal?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This map shows in green all the countries where same-sex marriage is legal."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Many of the countries that allow same-sex marriage are in Western Europe. But same-sex marriage is also legal in other parts of the world, especially in North and South America."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/marriage-for-same-sex-partners-velasco"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Some perspective on the progress made regarding marriage equality"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The rate of adoption of marriage equality legislation over time gives us some perspective on just how quickly things have changed. The first chart shows that in the year 2000, same-sex marriage was not legal in any country – two decades later it was legal in many more."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/countries-marriage-same-sex-partners-velasco"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/grapher/share-of-people-saying-they-do-not-want-homosexual-neighbors?tab=chart"", ""children"": [{""text"": ""Changes in attitudes towards homosexuality"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" are one of the key factors that have enabled the legal transformations that are making same-sex marriage increasingly possible."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The second chart shows that the number of people living in countries that have legalized same-sex marriage has also increased a lot."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/people-marriage-same-sex-partners-velasco"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And as the third chart shows, same-sex sexual acts are now legal in a majority of all countries"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/countries-in-which-same-sex-sexual-acts-are-legal"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Despite these positive trends, much remains to be done to improve the rights of LGBTQ people. In some countries people are imprisoned and even killed simply because of their sexual orientation or gender identity; and even in countries where same-sex sexual activity is legal, these groups of people face violence and discrimination."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Marriage trends show that social institutions can, and often do change quickly"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Across the world, fewer people are choosing to marry, and those who do marry are, on average, doing so later in life.  The underlying drivers of these trends include the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/contraceptive-prevalence-any-methods-of-women-ages-15-49"", ""children"": [{""text"": ""rise of contraceptives"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", the increase of female participation in labor markets (as we explain in our article "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/female-labor-supply"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""), and the transformation of institutional and legal environments, such as new legislation conferring more rights on unmarried couples."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These changes have led to a broad transformation of family structures. In the last decades, many countries have seen an increase in cohabitation, and it is becoming more common for children to live with a single parent, or with parents who are not married."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These changes have come together with a large and significant shift in people’s perceptions of the types of family structures that are possible, acceptable, and desirable. Perhaps the clearest example of this is the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/same-sex-marriage-country-count"", ""children"": [{""text"": ""rise of same-sex marriage"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The de-institutionalization of marriage and the rise of new family models since the middle of the 20th-century show that social institutions that have been around for thousands of years can change very rapidly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Divorce rates increased after 1970 – in recent decades the trends very much differ between countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Trends in the rate of divorces relative to the size of the population"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How have divorce rates changed over time? Are divorces on the rise across the world?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart here we show the crude divorce rate – the number of divorces per 1,000 people in the country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When we zoom out and look at the large-scale picture at the global or regional level since the 1970s, we see an overall increase in divorce rates. The UN in its "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.un.org/en/development/desa/population/publications/pdf/popfacts/PopFacts_2011-1.pdf"", ""children"": [{""text"": ""overview of global marriage patterns"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" notes that there is a general upward trend: \""at the world level, the proportion of adults aged 35-39 who are divorced or separated has doubled, passing from 2% in the 1970s to 4% in the 2000s.\"""", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But, when we look more closely at the data we can also see that this misses two key insights: there are notable differences between countries; and it fails to capture the pattern of these changes in the period from the 1990s to today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we see in the chart, for many countries divorce rates increased markedly between the 1970s and 1990s. In the US, divorce rates more than doubled from 2.2 per 1,000 in 1960 to over 5 per 1,000 in the 1980s. In the UK, Norway, and South Korea, divorce rates more than tripled. Since then divorce rates declined in many countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The trends vary substantially from country to country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, the US stands out as a bit of an outlier, with consistently higher divorce rates than most other countries, but also an earlier 'peak'. South Korea had a much later 'peak', with divorce rates continuing to rise until the early 2000s. In other countries – such as Mexico and Turkey – divorces continue to rise."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The pattern of rising divorce rates, followed by a plateau or fall in some countries (particularly richer countries) might be partially explained by the differences in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/marriages-and-divorces#divorces-by-age-the-recent-reduction-in-divorce-rates-might-be-explained-by-lower-likelihood-of-divorce-in-younger-cohorts"", ""children"": [{""text"": ""divorce rates across cohorts"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/marriages-and-divorces#length-of-marriage"", ""children"": [{""text"": ""delay in marriage"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" we see in younger couples today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Economists Betsey Stevenson and Justin Wolfers looked in detail at the changes and driving forces in marriage and divorce rates in the US."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" They suggest that the changes we see in divorce rates may be partly reflective of the changes in expectations within marriages as women enter the workforce. Women who married before the large "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/female-labor-supply"", ""children"": [{""text"": ""rise in female employment"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" may have found themselves in marriages where expectations were no longer suited. Many people in the postwar years married someone who was probably a good match for the postwar culture but ended up being the wrong partner after the times had changed. This may have been a driver behind the steep rise in divorces throughout the 1970s and 1980s."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/divorces-per-1000-people"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The share of marriages ending in divorce"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Trends in crude divorce rates give us a general overview of how many divorces happen each year but need to be interpreted with caution. First, crude rates mix a large number of cohorts – both older and young couples; and second, they do not account for how the number of marriages is changing."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To understand how patterns of divorce are changing it is more helpful to look at the percentage of marriages that end in divorce and look in more detail at these patterns by cohort."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Let's take a look at a country where divorce rates have been declining in recent decades."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart here we show the percentage of marriages which ended in divorce in England and Wales since 1963. This is broken down by the number of years after marriage – that is, the percentage of couples who had divorced five, ten and twenty years after they got married."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Here we see that for all three lines, the overall pattern is similar:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""The share of marriages that end in divorce increased from the 1960s to the 1990s."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 1963, only 1.5% of couples had divorced before their fifth anniversary, 7.8% had divorced before their tenth, and 19% before their twentieth anniversary. By the mid-1990s this had increased to 11%, 25% and 38%, respectively."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since then, divorces have been on the decline. The percentage of couples divorcing in the first five years has halved since its 1990s peak. And the percentage who got divorced within the first 10 years of their marriage has also fallen significantly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/marriages-uk-ended-in-divorce"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Divorces by age and cohort"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What might explain the recent reduction in overall divorce rates in some countries?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The overall trend can be broken down into two key drivers: a reduction in the likelihood of divorce for younger cohorts; and a lengthening of marriage before divorce for those that do separate."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see both of these factors in the analysis of divorce rates in the US from Stevenson and Wolfers."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This chart maps out the percentage of marriages ending in divorce: each line represents the decade they got married (those married in the 1950s, 60s, 70s, 80s, and 1990s) and the x-axis represents the years since the wedding."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see that the share of marriages ending in divorce increased significantly for couples married in the 1960s or 70s compared to those who got married in the 1950s. The probability of divorce within 10 years was twice as high for couples married in the 1960s versus those who got married in the 1950s. For those married in the 1970s, it was more than three times as likely."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You might have heard the popularised claim that \""half of all marriages end in divorce\"". We can see here where that claim might come from – it was once true: 48% of American couples that married in the 1970s were divorced within 25 years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But since then the likelihood of divorce has fallen. It fell for couples married in the 1980s, and again for those in the 1990s. Both the likelihood of divorce has been falling, and the length of marriage has been increasing."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Share of marriages ending in divorce in the US, by year of marriage"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Share-of-marriages-end-in-divorces-in-US-Stevenson-Wolfers.png"", ""hasOutline"": false, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is also true for marriages in the UK. This chart shows the cumulative share of marriages that ended in divorce: each line represents the year in which couples were married. A useful way to compare different age cohorts is by the steepness of the line: steeper lines indicate a faster accumulation of divorces year-on-year, particularly in the earlier stages of marriages."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You might notice that the divorce curves for couples in the 1960s are shallower and tend to level out in the range of 20% to 30%. Divorce rates then became increasingly steep throughout the 1970s; 80s and 90s, and eventually surpassed cumulative rates from the 1960s. But, since the 1990s, these curves appear to be falling once again, mirroring the findings from the US."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We don't know yet how long the marriages of younger couples today will last. It will take several decades before we have the full picture on more recent marriages and their eventual outcomes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/marriages-ending-divorce-uk"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Marriages in many countries are getting longer"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we saw from data on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/marriages-and-divorces#divorce-rates"", ""children"": [{""text"": ""divorce rates"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", in some countries – particularly richer countries such as the UK, US and Germany – divorce rates have been falling since the 1990s. This can be partially explained by a reduction in the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""share"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of marriages ending in divorce, but also by the length of marriages before their dissolution."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How has the length of marriages changed over time?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart here we see the duration of marriages before divorce across a number of countries where this data is available. An important point to note here is that the definitions are not consistent across countries: some countries report the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""median"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" length of marriage; others the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""mean"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". Since the distribution of marriage lengths is often skewed, the median and mean values can be quite different."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""So, we have to keep this in mind and be careful if we make cross-country comparisons. On the chart shown we note for each country whether the marriage duration is given as the median or mean value."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But we can gain insights from single countries "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""over time"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". What we see for a number of countries is that the average duration of marriage before divorce has been increasing since the 1990s or early 2000s. If we take the UK as an example: marriages got notably shorter between the 1970s to the later 1980s, falling from around 12 to 9 years. But, marriages have once again increased in length, rising back to over 12 years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This mirrors what we saw in data on the share of marriages ending in divorce: divorce rates increased significantly between the 1960s/70s through the 1990s, but have seen a fall since then."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see a similar pattern in the United States, New Zealand, Australia, and Singapore. However, there is still a significant amount of heterogeneity between countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/avg-duration-marriages"", ""type"": ""chart"", ""parseErrors"": []}, {""top"": [], ""type"": ""all-charts"", ""heading"": ""Interactive charts on Marriages and Divorces"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""0abc35af4f9488466a68e7ea103d9a7c28d2d956"": {""id"": ""0abc35af4f9488466a68e7ea103d9a7c28d2d956"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""UK Office for National Statistics (2014) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/marriagecohabitationandcivilpartnerships/datasets/marriagestatisticscohabitationandcohortanalyses"", ""children"": [{""text"": ""Marriage statistics, cohabitation and cohort analyses"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "".\u000b\u000b"", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/app/uploads/2020/07/Share-of-men-that-are-married-by-cohort-England-and-Wales-ONS.csv"", ""children"": [{""text"": ""Download the underlying data for this chart (.csv)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""1ad7d3e55f905fa38b2e493b99e34bf501669c4f"": {""id"": ""1ad7d3e55f905fa38b2e493b99e34bf501669c4f"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The average age at first marriage can be measured by directly asking people the age at which they first married. This data is unfortunately not available for all countries, so in some cases, figures correspond to an indirect estimate based on marriage rates by age. You find more details "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://www.stat.go.jp/info/meetings/cambodia/pdf/rp4_ch30.pdf"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3a7de3f4731041505d8020d1b76d777f19327cd9"": {""id"": ""3a7de3f4731041505d8020d1b76d777f19327cd9"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Office for National Statistics (2014). Marriage statistics, cohabitation and cohort analyses. Available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/marriagecohabitationandcivilpartnerships/datasets/marriagestatisticscohabitationandcohortanalyses"", ""children"": [{""text"": ""online"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3f1cf5ab2a077124b875f43b7c83a5e27d736004"": {""id"": ""3f1cf5ab2a077124b875f43b7c83a5e27d736004"", ""index"": 13, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""As the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/divorce/bulletins/divorcesinenglandandwales/2018"", ""children"": [{""text"": ""UK Office for National Statistics"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" notes:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""\""The median duration of marriage at divorce in this release is represented by the middle value when the data are arranged in increasing order. The median is used rather than the mean because the duration of marriage for divorces is not symmetrically distributed. Therefore, the median provides a more accurate reflection of this distribution. The mean would be affected by the relatively small number of divorces that take place when duration of marriage exceeds 15 years.\"""", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""62de8c6c80f8bfa76f4c92ca9c2842601c9c5dcf"": {""id"": ""62de8c6c80f8bfa76f4c92ca9c2842601c9c5dcf"", ""index"": 10, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For more details see Jean-François Mignot. Decriminalizing Homosexuality: A Global Overview Since the 18th Century. Annales de démographie historique, In press, 1."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""698daa515785cae9abdab5f63a226ecd0a95f428"": {""id"": ""698daa515785cae9abdab5f63a226ecd0a95f428"", ""index"": 11, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""You can read more about the driving forces of family change in Stevenson, B., & Wolfers, J. (2007). Marriage and divorce: Changes and their driving forces. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Journal of Economic Perspectives"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", 21(2), 27-52. Available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.aeaweb.org/articles?id=10.1257/jep.21.2.27"", ""children"": [{""text"": ""online"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""69ad6a2185d307b9741721a79d060846dc73ee22"": {""id"": ""69ad6a2185d307b9741721a79d060846dc73ee22"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For a discussion of historical remarriage patterns see for example Van Poppel, F. (1998). Nineteenth-century remarriage patterns in the Netherlands. The Journal of interdisciplinary history, 28(3), 343-383. Available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.jstor.org/stable/pdf/205419.pdf"", ""children"": [{""text"": ""online"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7532d00435411d3f7a73a5b5b92913626c8ce2c7"": {""id"": ""7532d00435411d3f7a73a5b5b92913626c8ce2c7"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""A different way to slice the data is to calculate the proportion of children who live in single-parent households. When calculated this way the ranking of countries changes, and the US stands out as the country with the highest share of children living with a single parent. You find a discussion, as well as a map with cross-country estimates of this alternative metric "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.pewresearch.org/fact-tank/2019/12/12/u-s-children-more-likely-than-children-in-other-countries-to-live-with-just-one-parent/"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7d1a5e727b9613de20dffd3f495fcc1d0296e086"": {""id"": ""7d1a5e727b9613de20dffd3f495fcc1d0296e086"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The concept of “risk of poverty or social exclusion” corresponds to the intersection of several vulnerability dimensions. It covers persons who are either at risk of poverty, severely materially deprived, or living in a household with a very low work intensity. You find more details about this in Eurostat "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:At_risk_of_poverty_or_social_exclusion_(AROPE)"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a2687c8ea207c5041b864726ac7202f35115bcbf"": {""id"": ""a2687c8ea207c5041b864726ac7202f35115bcbf"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Recorded evidence of marriage ceremonies (and divorce) dates back to to the third millennium BCE, in ancient Mesopotamia and Babylonia. Bertman, S. (2005). "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Handbook to Life in Ancient Mesopotamia"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". Oxford University Press."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b80c0edecf601eb6e51a665a1fb38bb2fa78e1cd"": {""id"": ""b80c0edecf601eb6e51a665a1fb38bb2fa78e1cd"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""United Nations, Department of Economic and Social Affairs, Population Division (2019). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://population.un.org/MarriageData/Index.html#/maritalStatusChart"", ""children"": [{""text"": ""World Marriage Data 2019"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "".\u000b\u000b"", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/app/uploads/2020/07/Share-of-women-in-the-UK-ever-married.csv"", ""children"": [{""text"": ""Download the underlying data for this chart (.csv)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c386c75626f32e5b80b2af339baf3d87507108e4"": {""id"": ""c386c75626f32e5b80b2af339baf3d87507108e4"", ""index"": 12, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Stevenson, B., & Wolfers, J. (2007). Marriage and divorce: Changes and their driving forces. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Journal of Economic Perspectives"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""21"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(2), 27-52. Available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.aeaweb.org/articles?id=10.1257/jep.21.2.27"", ""children"": [{""text"": ""online"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e83c2eb4c6cedd6a37e3142834f3d0646d8748b4"": {""id"": ""e83c2eb4c6cedd6a37e3142834f3d0646d8748b4"", ""index"": 9, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For details see Adamczyk, A., & Liao, Y. C. (2019). Examining public opinion about LGBTQ-related issues in the United States and across multiple nations. Annual Review of Sociology, 45, 401-423."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f23d4020bcb3e809cd72d8a739c4fb000d0e5dfc"": {""id"": ""f23d4020bcb3e809cd72d8a739c4fb000d0e5dfc"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This is based on estimates from 2016 published in the OECD Family Database (Table SF1.3.A. Living arrangements of children by age, online "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.oecd.org/els/family/database.htm"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "")."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""linear-topic-page"", ""title"": ""Marriages and Divorces"", ""authors"": [""Esteban Ortiz-Ospina"", ""Max Roser""], ""excerpt"": ""How is the institution of marriage changing? What percentage of marriages end in divorce? Explore global data on marriages and divorces."", ""dateline"": ""This page was first published in July 2020 and last revised in April 2024."", ""subtitle"": ""How is the institution of marriage changing? What percentage of marriages end in divorce? Explore global data on marriages and divorces."", ""sidebar-toc"": true, ""featured-image"": ""marriages-and-divorces-thumbnail.png""}",1,2023-11-10 14:32:42,2020-07-25 13:33:09,2023-12-28 16:31:12,unlisted,ALBJ4Lt7dGSPdhAHKaLl5HH7zh97_gPE96bd-nLIyr8JTMLSV0HhInUBcK4qW-grxJkSSIBHNCnPh4RB4nNHdw,,"Marriage, as a social institution, has been around for thousands of years.1 With things that are thousands of years old, it’s easy to assume that they can only change slowly. But developments since the middle of the 20th century show that this assumption is wrong: in many countries marriages are becoming less common, people are marrying later, unmarried couples are increasingly choosing to live together, and in many countries we are seeing a ‘decoupling’ of parenthood and marriage. Within the last decades the institution of marriage has changed more than in thousands of years before. Here we present the data behind these fast and widespread changes and discuss some of the main drivers behind them. ## Marriages are becoming less common ### In many countries marriage rates are declining The proportion of people who are getting married is going down in many countries across the world. The chart here shows this trend for a selection of countries. It combines data from multiple sources, including statistical country offices and reports from the UN, Eurostat and the OECD. You can change the selection of countries using the option Add Country directly in the interactive chart. #### Marriage rates in the US over the last century For the US we have data on marriage rates going back to the start of the 20th century. This lets us see when the decline started, and trace the influence of social and economic changes during the process. * In 1920, shortly after the First World War, there were 12 marriages annually for every 1,000 people in the US. Marriages in the US then were almost twice as common as today. * In the 1930s, during the Great Depression, the rate fell sharply. In the 1930s marriages became again more common and in 1946 – the year after the Second World War ended – marriages reached a peak of 16.4 marriages per 1,000 people. * Marriage rates fell again in the 1950s and then bounced back in the 1960s. * The long decline started in the 1970s. Since 1972, marriage rates in the US have fallen by almost 50%, and are currently at the lowest point in recorded history. #### How did marriage rates change around the world? The chart also shows that in comparison to other rich countries, the US has had particularly high historical marriage rates. But in terms of changes over time, the trend looks similar for other rich countries. The UK and Australia, for example, have also seen marriage rates declining for decades, and are currently at the lowest point in recorded history. For non-rich countries the data is sparse, but available estimates from Latin America, Africa and Asia suggest that the decline of marriages is not exclusive to rich countries. Over the period 1990 - 2010 there was [a decline](https://ourworldindata.org/grapher/marriage-rate-1990-vs-2010) in marriage rates in the majority of countries around the world. But there’s still a lot of cross-country variation around this general trend, and in some countries changes are going in the opposite direction. In China, Russia and Bangladesh, for example, marriages are more common today than a couple of decades ago. ### Marriages rates in 1990 vs. 2010 Compare marriage rates in 1990 and 2010 for all countries in our interactive scatter plot. https://ourworldindata.org/grapher/marriage-rate-1990-vs-2010 ### In many countries there has been a large decline in marriages across cohorts This chart looks at the change in marriages from a different angle and answers the question: How likely were people in different generations to be married by a given age? In many rich countries there are statistical records going back several generations, allowing us to estimate marriage rates by age and year of birth. The chart here uses those records to give marriage rates by age and year of birth for five cohorts of men in England and Wales. For instance, you can look at 30-year-olds, and see what percentage of them in each cohort was married. Of those men who were born in 1940, about 83% were married by age 30. Among those born in 1980 only about 25% were married by age 30. The trend is stark. English men in more recent cohorts are much less likely to have married, and that’s true at all ages. There are two causes for this: an increasing share of people in younger cohorts are not getting married; and younger cohorts are increasingly choosing to marry later in life. We explore this second point below. [Download the underlying data for this chart (.csv)](https://ourworldindata.org/app/uploads/2020/07/Share-of-men-that-are-married-by-cohort-England-and-Wales-ONS.csv) ## Average age at marriage ### People are marrying later In many countries, declining marriage rates have been accompanied by an increase in the age at which people are getting married. This is shown in the chart here, where we plot the average age of women at first marriage.3 The increase in the age at which people are getting married is stronger in richer countries, particularly in North America and Europe. In Sweden, for example, the average age of marriage for women went up from 28 in 1990 to 34 years in 2017. In Bangladesh and several countries in sub-Saharan Africa, the average age at marriage is low and has remained unchanged for several years. In Niger, where child marriage is common, the average age at marriage for women has remained constant, at 17 years, since the early 1990s.**_ (NB. You find child marriage data in our interactive chart _****_[here](https://ourworldindata.org/violence-against-rights-for-children#child-marriage)_****_)._** But these countries are the exceptions. The age at which women marry is increasing in many countries in all regions, from Norway to Japan to Chile. ### Share of women married by age and year of birth More people marrying later means that a greater share of young people being unmarried. According to the British census of 1971 about 85% of women between the age of 25 and 29 were married, as this chart shows. By 2011 that figure had declined to 58%. For older people the trend is reversed – the share of older women who never got married is declining. In the 1971 census the share of women 60-64 who had ever been married was lower than it is for women in that age-bracket in the decades since. You can create similar charts for both men and women across all countries, using the _UN World Marriage Data_ site [here](https://population.un.org/MarriageData/Index.html#/maritalStatusChart). This lets you explore in more detail the distribution of marriages by age across time, for both men and women. [Download the underlying data for this chart (.csv)](https://ourworldindata.org/app/uploads/2020/07/Share-of-women-in-the-UK-ever-married.csv) ## There has been a ‘decoupling’ of parenthood and marriage ### The share of children born outside of marriage has increased substantially in almost all OECD countries An arrangement where two or more people are not married but live together is referred to as cohabitation. In recent decades cohabitation has become increasingly common around the world. In the US, for example, the _US Census Bureau_[estimates](https://www.census.gov/library/stories/2018/11/cohabitaiton-is-up-marriage-is-down-for-young-adults.html) that the share of young adults between the age of 18 and 24 living with an unmarried partner went up from 0.1% to 9.4% over the period 1968-2018; and according to a [recent survey](https://www.pewsocialtrends.org/2019/11/06/marriage-and-cohabitation-in-the-u-s/) from Pew Research, today most Americans favor allowing unmarried couples to have the same legal rights as married couples. The increase in cohabitation is the result of the two changes that we discussed above: fewer people are choosing to marry and those people who do get married tend to do so when they are older, and often live with their partner before getting married. In the UK, for example, 85% of people who get married cohabited first.5 Long-run data on the share of people living in cohabitation across countries is not available, but some related datapoints are: In particular, the proportion of births outside marriage provide a relevant proxy measure, allowing comparisons across countries and time; if more unmarried people are having children, it suggests that more people are entering long-term cohabiting relationships without first getting married. It isn’t a perfect proxy – as we’ll see below, rates of single parenting have also changed, meaning that rates of births outside marriage will not match perfectly with cohabitation rates – but it provides some information regarding the direction of change. The chart here shows the percentage of all children who were born to unmarried parents. As we can see, the share of children born outside of marriage has increased substantially in almost all OECD countries in recent decades. The exception is Japan, where there has been only a very minor increase. In 1970, most OECD countries saw less than 10% of children born outside of marriage. In 2014, the share had increased to more than 20% in most countries, and to more than half in some. The trend is not restricted to very rich countries. In Mexico and Costa Rica, for example, the increase has been very large, and today the majority of children are born to unmarried parents. ### Globally, the percentage of women in either marriage or cohabitation is decreasing, but only slightly In recent decades there has been a decline in global marriage rates, and at the same time that there has been an increase in cohabitation. What’s the combined effect if we consider marriage and cohabitation together? The chart below plots estimates and projections, from the UN Population Division, for the percentage of women of reproductive age (15 to 49 years) who are either married or living with an unmarried partner. Overall, the trend shows a global decline – but only a relatively small one, from 69% in 1970 to 64% projected for 2020. At any given point in the last five decades, around two-thirds of all women were married or cohabitated. There are differences between regions. In East Asia the share of women who are married or in a cohabiting union increased, in South America the share is flat, and in North America and North Europe it declined. You can use the option 'Add region' to plot the series for other regions. ### Single parenting is common, and in many countries it has increased in recent decades This chart shows the share of households of a single parent living with dependent children. There are large differences between countries. In Colombia there has been an upward trend, and according to the most recent estimates, 13% of all households are a single parent with one or more dependent children. In India, on the other hand, the corresponding figure is 5%, with no clear trend up or down.6 The causes and situations leading to single parenting are varied, and unsurprisingly, single-parent families are very diverse in terms of socio-economic background and living arrangements, across countries, within countries, and over time. However, there are some common patterns: 0. Women head the majority of single-parent households, and this gender gap tends to be stronger for parents of younger children. Across OECD countries, about 12% of children aged 0-5 years live with a single parent; 92% of these live with their mother.7 1. Single-parent households are among the most financially vulnerable groups. This is true even in rich countries. According to [Eurostat data](https://ec.europa.eu/social/main.jsp?catId=738&langId=en&pubId=8234&furtherPubs=yes), across European countries 47% of single-parent households were “at risk of poverty or social exclusion” in 2017, compared with 21% of two-parent households.8 2. Single parenting was probably more common a couple of centuries ago. But single parenting back then was often caused by [high maternal mortality](https://ourworldindata.org/maternal-mortality) rather than choice or relationship breakdown; and it was also typically short in duration, since remarriage rates were high.9 ## Same-sex marriage has become possible in many countries Marriage equality is increasingly considered a human and civil right, with important political, social, and religious implications around the world. In 1989, Denmark became the first country to recognize a legal relationship for same-sex couples, establishing ‘registered partnerships’ granting those in same-sex relationships [most of the rights](https://www.nytimes.com/1989/10/02/world/rights-for-gay-couples-in-denmark.html) given to married heterosexuals. It took more than a decade for same-sex marriage to be legal anywhere in the world. In December 2000, the Netherlands became the first country to establish same-sex marriage by law. In the first two decades of the 21st century legislation has quickly spread across more countries. ### Where are same-sex marriages legal? This map shows in green all the countries where same-sex marriage is legal. More than half of the countries that allow same-sex marriage are in Western Europe. But there are several Western European countries that still do not allow them. In Italy, Switzerland and Greece same-sex marriage is not legal, although in these countries there are [alternative forms](https://ourworldindata.org/grapher/civil-union-for-same-sex-partners) of recognition for same-sex couples. But same-sex marriage is also legal in other parts of the world, especially in North and South America. ### Some perspective on the progress made regarding marriage equality The rate of adoption of marriage equality legislation over time gives us some perspective on just how quickly things have changed. In the year 2000 same-sex marriage was not legal in any country – 20 years later it was legal in many more. Changes in [attitudes towards homosexuality](https://ourworldindata.org/grapher/public-opinion-homosexuality-scatter) are one of the key factors that have enabled the legal transformations that are making same-sex marriage increasingly possible.10 The second chart shows that the number of people living in countries that have legalized same-sex marriage has also increased a lot. And as the third chart shows, same-sex sexual acts are now legal in a majority of all countries11. Despite these positive trends, much remains to be done to improve the rights of LGBTQ people. In some countries people are imprisoned and even killed simply because of their sexual orientation or gender identity; and even in countries where same-sex sexual activity is legal, these groups of people face violence and discrimination. ### Legalizing homosexuality You can explore the year when homosexuality became legal in each country in our interactive map here https://ourworldindata.org/grapher/year-when-homosexuality-became-legal ## Marriage trends show that social institutions can, and often do change quickly Across the world, fewer people are choosing to marry, and those who do marry are, on average, doing so later in life.  The underlying drivers of these trends include the [rise of contraceptives](https://ourworldindata.org/grapher/contraceptive-prevalence-any-methods-of-women-ages-15-49), the increase of female participation in labor markets (as we explain in our article **[here](https://ourworldindata.org/female-labor-supply)**), and the transformation of institutional and legal environments, such as new legislation conferring more rights on unmarried couples.12 These changes have led to a broad transformation of family structures. In the last decades, many countries have seen an increase in cohabitation, and it is becoming more common for children to live with a single parent, or with parents who are not married. These changes have come together with a large and significant shift in people’s perceptions of the types of family structures that are possible, acceptable and desirable. Perhaps the clearest example of this is the [rise of same-sex marriage](https://ourworldindata.org/grapher/same-sex-marriage-country-count). The de-institutionalization of marriage and the rise of new family models since the middle of the 20th century show that social institutions that have been around for thousands of years can change very rapidly. --- # Divorces --- ## Divorce rates increased after 1970 – in recent decades the trends very much differ between countries ### Trends in the rate of divorces relative to the size of the population How have divorce rates changed over time? Are divorces on the rise across the world? In the chart here we show the crude divorce rate – the number of divorces per 1,000 people in the country. When we zoom out and look at the large-scale picture at the global or regional level since the 1970s, we see an overall increase in divorce rates. The UN in its [overview of global marriage patterns](https://www.un.org/en/development/desa/population/publications/pdf/popfacts/PopFacts_2011-1.pdf) notes that there is a general upward trend: ""at the world level, the proportion of adults aged 35-39 who are divorced or separated has doubled, passing from 2% in the 1970s to 4% in the 2000s."" But, when we look more closely at the data we can also see that this misses two key insights: there are notable differences between countries; and it fails to capture the pattern of these changes in the period from the 1990s to today. As we see in the chart, for many countries divorce rates increased markedly between the 1970s and 1990s. In the US, divorce rates more than doubled from 2.2 per 1,000 in 1960 to over 5 per 1,000 in the 1980s. In the UK, Norway and South Korea, divorce rates more than tripled. Since then divorce rates declined in many countries. The trends vary substantially from country to country. In the chart the US stands out as a bit of an outlier, with consistently higher divorce rates than most other countries, but also an earlier 'peak'. South Korea had a much later 'peak', with divorce rates continuing to rise until the early 2000s. In other countries – such as Mexico and Turkey – divorces continue to rise. As the [OECD Family Database](http://www.oecd.org/social/family/SF_3_1_Marriage_and_divorce_rates.pdf) notes, between 1995 and 2017 (or the nearest available estimate), divorce rates increased in 18 OECD countries, but fell in 12 others. The pattern of rising divorce rates, followed by a plateau or fall in some countries (particularly richer countries) might be partially explained by the differences in [divorce rates across cohorts](https://ourworldindata.org/marriages-and-divorces#divorces-by-age-the-recent-reduction-in-divorce-rates-might-be-explained-by-lower-likelihood-of-divorce-in-younger-cohorts), and the [delay in marriage](https://ourworldindata.org/marriages-and-divorces#length-of-marriage) we see in younger couples today. Economists Betsey Stevenson and Justin Wolfers looked in detail at the changes and driving forces in marriage and divorce rates in the US.13 They suggest that the changes we see in divorce rates may be partly reflective of the changes in expectations within marriages as women entered the workforce. Women who married before the large [rise in female employment](https://ourworldindata.org/female-labor-supply) may have found themselves in marriages where expectations were no longer suited. Many people in the postwar years married someone who was probably a good match for the postwar culture, but ended up being the wrong partner after the times had changed. This may have been a driver behind the steep rise in divorces throughout the 1970s and 1980s. ### The share of marriages ending in divorce Trends in crude divorce rates give us a general overview of how many divorces happen each year, but need to be interpreted with caution. First, crude rates mix a large number of cohorts – both older and young couples; and second, they do not account for how the number of marriages is changing. To understand how patterns of divorce are changing it is more helpful to look at percentage of marriages that end in divorce, and look in more detail at these patterns by cohort. Let's take a look at a country where divorce rates been declining in recent decades. In the chart here we show the percentage of marriages which ended in divorce in England and Wales since 1963. This is broken down by the number of years after marriage – that is, the percentage of couples who had divorced five, ten and twenty years after they got married. Here we see that for all three lines, the overall pattern is similar: * The share of marriages that end in divorce increased through the 1960s to the 1990s. * In 1963, only 1.5% of couples had divorced before their fifth anniversary, 7.8% had divorced before their tenth, and 19% before their twentieth anniversary. By the mid-1990s this had increased to 11%, 25% and 38%, respectively. * Since then, divorces have been on the decline. The percentage of couples divorcing in the first five years has halved since its 1990s peak. And the percentage who got divorced within the first 10 years of their marriage has also fallen significantly. ### Divorces by age and cohort What might explain the recent reduction in overall divorce rates in some countries? The overall trend can be broken down into two key drivers: a reduction in the likelihood of divorce for younger cohorts; and a lengthening of marriage before divorce for those that do separate. We see both of these factors in the analysis of divorce rates in the US from Stevenson and Wolfers.13 This chart maps out the percentage of marriages ending in divorce: each line represents the decade they got married (those married in the 1950s, 60s, 70s, 80s and 1990s) and the x-axis represents the years since the wedding. We see that the share of marriages ending in divorce increased significantly for couples married in 1960s or 70s compared to those who got married in the 1950s. The probability of divorce within 10 years was twice as high for couples married in the 1960s versus those who got married in the 1950s. For those married in the 1970s, it was more than three times as likely. You might have heard the popularised claim that ""half of marriages end in divorce"". We can see here where that claim might come from – it was once true: 48% of American couples that married in the 1970s were divorced within 25 years. But since then the likelihood of divorce has fallen. It fell for couples married in the 1980s, and again for those in the 1990s. Both the likelihood of divorce has been falling, and the length of marriage has been increasing. This is also true for marriages in the UK. This chart shows the cumulative share of marriages that ended in divorce: each line represents the year in which couples were married. A useful way to compare different age cohorts is by the steepness of the line: steeper lines indicate a faster accumulation of divorces year-on-year, particularly in the earlier stages of marriages. You might notice that the divorce curves for couples in the 1960s are shallower and tend to level out in the range of 20% to 30%. Divorce rates then became increasingly steep throughout the 1970s; 80s and 90s, and eventually surpass cumulative rates from the 1960s. But, since the 1990s, these curves appear to be falling once again, mirroring the findings from the US. We don't know yet how long the marriages of younger couples today will last. It will take several decades before we have the full picture on more recent marriages and their eventual outcomes. ## Marriages in many countries are getting longer As we saw from data on [divorce rates](https://ourworldindata.org/marriages-and-divorces#divorce-rates), in some countries – particularly richer countries such as the UK, US and Germany – divorce rates have been falling since the 1990s. This can be partially explained by a reduction in the _share_ of marriages ending in divorce, but also by the length of marriages before their dissolution. How has the length of marriages changed over time? In the chart here we see the duration of marriages before divorce across a number of countries where this data is available. An important point to note here is that the definitions are not consistent across countries: some countries report the _median_ length of marriage; others the _mean_. Since the distribution of marriage lengths is often skewed, the median and mean values can be quite different. As the [UK Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/divorce/bulletins/divorcesinenglandandwales/2018) notes: -- So, we have to keep this in mind and be careful if we make cross-country comparisons. On the chart shown we note for each country whether the marriage duration is given as the median or mean value. But, we can gain insights for single countries _over time_. What we see for a number of countries is that the average duration of marriage before divorce has been increasing since the 1990s or early 2000s. If we take the UK as an example: marriages got notably shorter between the 1970s to the later 1980s, falling from around 12 to 9 years. But, marriages have once again increased in length, rising back to over 12 years. This mirrors what we saw in data on the share of marriages ending in divorce: divorce rates increased significantly between the 1960s/70s through the 1990s, but have seen a fall since then. We see a similar pattern in the United States, New Zealand, Australia, and Singapore. However, there is still a significant amount of heterogeneity between countries. --- # Data sources --- ### UN World Marriage Data * **Data:** Marital status, marriage rates, and mean age of marriage, broken down by sex * **Geographical coverage:** Single countries around the world * **Time span:** from 1971 onwards * **Available at:** Online [here](https://population.un.org/MarriageData/Index.html#/maritalStatusData). ### UN Population Division * **Data:** Household size and composition (including single parent households) * **Geographical coverage:** Single countries around the world * **Time span:** from 1960 onwards * **Available at:** Online **[here](https://population.un.org/Household/index.html#/countries/840)**. ### OECD Family Database * **Data:** Marital and divorce rates, births outside of marriage, and cohabitation status * **Geographical coverage:** OECD countries only * **Time span:** from 1970 onwards * **Available at:** Online **[here](http://www.oecd.org/els/family/database.htm)**. ### Eurostat * **Data:** Crude marriage and divorce rates; children born outside of marriage * **Geographical coverage:** European countries only * **Time span:** from 1960 onwards * **Available at:** Online **[here](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Marriage_and_divorce_statistics#Fewer_marriages.2C_more_divorces)**. ### Pew Research Center * **Data:** Policies and legalisation of same-sex marriage * **Geographical coverage:** Single countries across the world * **Time span:** from 2000 onwards * **Available at:** Online **[here](https://www.pewforum.org/fact-sheet/gay-marriage-around-the-world/)**. ### National Statistical Agencies We also rely on national databases, which provide a variety of data including marriage and divorce rates; length of marriage; marital and cohabitation status. Examples include: * **UK:**[UK Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/bulletins/populationestimatesbymaritalstatusandlivingarrangements/2019) * **United States: **[US Census Bureau](https://www.census.gov/library/publications/2006/compendia/statab/126ed/vital-statistics.html) and [Centers for Disease Control and Prevention](https://www.cdc.gov/nchs/nvss/marriage-divorce.htm?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fnchs%2Fmardiv.htm) * **Australia:**[Australian Bureau of Statistics](https://www.abs.gov.au/AUSSTATS/abs@.nsf/Previousproducts/3310.0Main%20Features42017?opendocument&tabname=Summary&prodno=3310.0&issue=2017&num=&view=) * **Singapore:**[Singapore Statistics](https://www.singstat.gov.sg/find-data/search-by-theme/population/marital-status-marriages-and-divorces/latest-data) * **Germany:**[Statistisches Bundesamt](https://www.destatis.de/EN/Themes/Society-Environment/Population/Marriages-Divorces-Life-Partnerships/Tables/statistical-parameters.html) * **Sweden:**[Statistics Sweden](http://www.statistikdatabasen.scb.se/pxweb/en/ssd/START__BE__BE0101__BE0101L/AktenskapVaraktighet/) * **Ecuador:**[Instituto Nacional de Estadisticas y Censos, Registro Estadístico de Divorcios, 2018](https://www.ecuadorencifras.gob.ec/documentos/web-inec/Poblacion_y_Demografia/Matrimonios_Divorcios/2018/Principales_resultados_MYD_2018.pdf) * **New Zealand:**[NZ.Stat Infoshare](http://archive.stats.govt.nz/infoshare/) Office for National Statistics (2014). Marriage statistics, cohabitation and cohort analyses. Available online [here](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/marriagecohabitationandcivilpartnerships/datasets/marriagestatisticscohabitationandcohortanalyses). Recorded evidence of marriage ceremonies (and divorce) dates back to to the third millenium BCE, in ancient Mesopotamia and Babylonia. Bertman, S. (2005). _Handbook to Life in Ancient Mesopotamia_. Oxford University Press. United Nations, Department of Economic and Social Affairs, Population Division (2019). [World Marriage Data 2019](https://population.un.org/MarriageData/Index.html#/maritalStatusChart). The average age at first marriage can be measured by directly asking people the age at which they first married. This data is unfortunately not available for all countries, so in some cases figures correspond to an indirect estimate based on marriage rates by age. You find more details **[here](https://www.stat.go.jp/info/meetings/cambodia/pdf/rp4_ch30.pdf)**. For a discussion of historical remarriage patterns see for example Van Poppel, F. (1998). Nineteenth-century remarriage patterns in the Netherlands. The Journal of interdisciplinary history, 28(3), 343-383. Online [here](https://www.jstor.org/stable/pdf/205419.pdf). You can read more about the driving forces of family change in Stevenson, B., & Wolfers, J. (2007). Marriage and divorce: Changes and their driving forces. _Journal of Economic Perspectives_, 21(2), 27-52. Online [here](http://unionstats.gsu.edu/4960/Stevenson-Wolfers_2007.pdf). A different way to slice the data is to calculate the proportion of children who live in single-parent households. When calculated this way the ranking of countries changes, and the US stands out as the country with the highest share of children living with a single parent. You find a discussion, as well as a map with cross-country estimates of this alternative metric [here](https://www.pewresearch.org/fact-tank/2019/12/12/u-s-children-more-likely-than-children-in-other-countries-to-live-with-just-one-parent/). The concept of “risk of poverty or social exclusion” corresponds to the intersection of several vulnerability dimensions. It covers persons who are either at risk of poverty, or severely materially deprived, or living in a household with a very low work intensity. You find more details about this in Eurostat [here](https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:At_risk_of_poverty_or_social_exclusion_(AROPE)). UK Office for National Statistics (2014) – [Marriage statistics, cohabitation and cohort analyses](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/marriagecohabitationandcivilpartnerships/datasets/marriagestatisticscohabitationandcohortanalyses). For more details see Kenny, C., & Patel, D. (2017). Norms and Reform: Legalizing Homosexuality Improves Attitudes. Center for Global Development Working Paper, (465). Stevenson, B., & Wolfers, J. (2007). [Marriage and divorce: Changes and their driving forces](http://unionstats.gsu.edu/4960/Stevenson-Wolfers_2007.pdf). _Journal of Economic Perspectives_, _21_(2), 27-52. For details see Adamczyk, A., & Liao, Y. C. (2019). Examining public opinion about LGBTQ-related issues in the United States and across multiple nations. Annual Review of Sociology, 45, 401-423. This is based on estimates from 2016 published in the OECD Family Database (Table SF1.3.A. 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You can find the most up-to-date data for all countries in our "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/coronavirus-data-explorer"", ""children"": [{""text"": ""Coronavirus Data Explorer"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": """", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/excess-mortality-covid"", ""type"": ""prominent-link"", ""title"": ""Explore our continuously updated presentation of data on excess mortality"", ""thumbnail"": ""covid-excess-mortality.png"", ""description"": """", ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""New research publications on excess mortality by Janine Aron and John Muellbauer:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://voxeu.org/article/us-excess-mortality-rate-covid-19-substantially-worse-europe-s"", ""type"": ""prominent-link"", ""title"": ""The US excess mortality rate from COVID-19 is substantially worse than Europe’s"", ""description"": ""In this follow-up article for VoxEU the two researchers find that Europe’s cumulative excess mortality rate from March to July is 28% lower than the US rate."", ""parseErrors"": []}, {""url"": ""https://drive.google.com/file/d/17N9zmr4sKIu5yfSiIDrZd4WFwQIIRww-/view?usp=drive_link"", ""type"": ""prominent-link"", ""title"": ""Transatlantic excess mortality comparisons in the pandemic"", ""description"": ""In this new follow-up article the two researchers compare the excess mortality in Europe and the US."", ""parseErrors"": []}, {""text"": [{""text"": ""1. Why is it important to examine excess mortality data?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Excess mortality is a count of deaths from all causes relative to what would normally have been expected. In a pandemic, deaths rise sharply, but causes are often inaccurately recorded, particularly when reliable tests are not widely available. The "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/covid-deaths"", ""children"": [{""text"": ""death count"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" attributed to Covid-19 may thus be significantly undercounted. Excess mortality data overcome two problems in reporting Covid-19-related deaths. Miscounting from misdiagnosis or under-reporting of Covid-19-related deaths is avoided. Excess mortality data include ‘collateral damage’ from other health conditions, left untreated if the health system is overwhelmed by Covid-19 cases, or by deliberate actions that prioritise patients with Covid-19 over those with other symptoms."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In a pandemic, measures taken by governments and by individuals also influence death rates. For example, deaths from traffic accidents may decline but suicide rates may rise. Excess mortality captures the net outcome of all these factors. Figure 1 illustrates how the degree of Covid-19 recording relative to excess deaths has varied across some European countries. In Belgium, with a broad definition of what constitutes a Covid-19 death, the excess over 100 percent might suggest that most excess deaths are due to Covid-19 and other deaths, such as those due to road accidents, may have declined."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Excess mortality data can be used to draw lessons from cross- and within-country differences and help analyse the social and economic consequences of the pandemic and relaxing lockdown restrictions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For country comparisons (where under-recording may differ), policy-makers should examine "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""robust measures "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""expressed relative to the benchmarks of 'normal' deaths. ‘Normal’ death rates reflect persistent factors such as the age composition of the population, the incidence of smoking and air pollution, the prevalence of obesity, poverty and inequality, and the normal quality of health service delivery. Estimating the virus reproduction rate, "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""R"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", is crucial for assessing the rate and nature of relaxation of lockdowns."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Excess death figures could help to avoid the measurement biases inherent in other data typically used to estimate R in epidemiological models."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Figure 1: Attributed Covid-19 deaths as a percentage of excess deaths for poor performers (‘all ages’): cumulated over pandemic weeks"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Figure-1.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""2. How is excess mortality measured and who measures it?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""National statistical agencies publish actual weekly deaths and averages of past ‘normal’ deaths. For example, the Office for National Statistics (ONS) reports ‘normal’ deaths for England and for Wales as the average of the previous five years’ deaths. However, there are no published benchmarks for more granular or disaggregated data, such as sub-regions or cities. Using the weekly historical data, researchers could calculate such benchmarks with some effort. The ratio or percentage of excess deaths relative to ‘normal’ deaths, the P-score, is an easily understood measure of excess mortality, see Box 1. We argue that national statistical offices should publish P-scores for states and sub-regions. In the U.S., the National Centre for Health Statistics publishes data on excess deaths and a variant on P-scores (see Box 1), defining excess deaths as deviations from ‘normal’ deaths plus a margin adjusting for the uncertainty of the data."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" These data include counties and states, and are disaggregated by gender, age and ethnicity. The NCHS thus sets an international standard for statistical agencies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, to obtain cross-European comparisons requires data collation from individual national agencies to construct P-scores or variant P-scores, which are largely comparable, see section 4.1. Another alternative are the Z-scores compiled by EuroMOMO"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" for 24 states, see Box 1. EuroMOMO’s measures of weekly excess mortality in Europe show the mortality patterns between different time-periods, across countries, and by age-groups. The Z-scores standardise data on excess deaths by scaling by the standard deviation of deaths. EuroMOMO are currently not permitted to publish actual excess death figures by country and do not publish the standard deviations used in their calculations. However, they graph the Z-scores and the estimated confidence intervals back to 2015 providing a visual guide to their variability. In contrast to the P-scores, the Z-scores are a measure that is less easily interpretable. Moreover, if the natural variability of the weekly data is lower in one country compared to another, then the Z-scores could lead to exaggeration of excess mortality compared to the P-scores. Strictly, the Z-scores are not comparable across countries, though see the caveats in section 4.1."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At least five separate journalistic endeavours have recently engaged in the time-consuming effort of collating and presenting more transparent excess mortality data, see Table 1. The Financial Times plots numbers of excess deaths, and the P-score or percentage of deaths that are above normal deaths. The Economist shows figures and graphics for excess deaths but not P-scores. However, the published estimates of P-scores in newspapers give only a recent snapshot, missing the context of historical variability provided by EuroMOMO. And we only have P-scores for some countries, regions and cities. A third measure of excess mortality is per capita excess mortality, where excess deaths (actual deaths minus ‘normal’ deaths) are divided by population, see Box 1, is used by the BBC (Table 1)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""gray-section"", ""items"": [{""text"": [{""text"": ""Table 1: Sources of comparative excess mortality data for Europe, the UK and the US, and other countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""expandable-paragraph"", ""items"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Sources"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""rows"": [{""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Source and metafiles"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Measure reported"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Period and type of data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Benchmark"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Disaggregation?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Locations compared"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""First publication date"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}]}, {""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""url"": ""https://www.mortality.org/"", ""children"": [{""text"": ""The Human Mortality Database"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (HMD)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Comprehensive, transparent metafile for data sources and coverage."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Death counts and death rates by country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""[The raw data allow P-scores to be calculated]."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Weekly, 2000-2020 for many. 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This takes into account uncertainty created by the natural variability of x."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""The per capita excess mortality "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""is defined as follows:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""(x minus the expected value of x for the population), divided by the population."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""The Z-score "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""is defined as follows:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""(x minus the expected value of x for the population), divided by the standard deviation for the population of x around its expected value."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""EuroMOMO "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""estimate "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""the expected value of each country’s weekly deaths using data for the previous five years, taking seasonal factors and trends into account, and adjust for delays in registration."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""EuroMOMO assume that a Poisson distribution, adjusted for excess dispersion is a good approximation to the underlying probability distribution of weekly deaths."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Graphs published for each country show the weekly Z-scores since 2015 compared to their usual range of -2 to +2, the approximate 95% confidence interval. Around 2.5% of observations would thus usually have a Z-value over 2. The Z-score equals 4 line is also shown, corresponding to a ‘substantial increase’: under usual conditions, the Z-value would exceed 4 only around 0.003% of the time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The graphs show more deviations of Z-scores ’exceeding 2’ and ‘exceeding 4’, than one would expect. The main reason is that to fit the baseline, EuroMOMO chose only the period of the year when additional processes (e.g. Winter influenza and Summer heat waves) leading to excess deaths are not likely to happen. Normal variability is thus measured after excluding these seasons."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""parseErrors"": []}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""3. Key issues for comparing rates of excess mortality across and within nations"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are several reasons for wanting to compare excess mortality between regions or countries. The first is simply to compare the death toll of the first wave of the pandemic. Useful aggregate measures include the count of excess deaths relative to normal deaths, for example, the P-score, and excess deaths relative to population size, see Box 1. The second of these measures has the problem that older populations tend to have higher normal death counts. This measure of excess deaths will overstate the incidence of the pandemic in older compared to younger populations. For the second reason, that of evaluating the effectiveness of policy responses, one needs to dig deeper, and the simple measures above require further interpretation. Countries may differ in the size of the initial source of infection, in their age structure, in the distribution of co-morbidities in the population and the prevalence of dense urban centres, making some countries more vulnerable. Comparing age-standardised mortality can be helpful in controlling for differences in age structures. Finally, the third motivation for comparisons is a purely objective one of improving the scientific understanding of the dynamics of the spread of infections, their incidence and the death rates of those infected. Key to this last endeavour is the production of granular data, i.e. disaggregation of excess deaths data by age, gender, region, and, where possible, socio-economic categories."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A recent controversy in the UK amongst statisticians has served to reinforce the point of our paper, which is that there can be international comparability "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""now"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of excess mortality with aggregate and more granular P-score data. There are already widely available granular data sets on related aspects such as inequality and urban density, which could be combined with such data for illuminating the comparisons across countries and revealing the effectiveness of different types of policy. Ideally there should be transparent definitions of data and comparability of definitions across nations which may involve coordination by existing international bodies for standards of data dissemination. This will evolve over time but does not preclude analysis "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""now"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". Important is the accessibility of data to all, especially modellers in the fields of epidemiology, economics and sociology. Scientific analysis with appropriate data is needed to inform policy "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""now"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" because not only may there may be successive waves of the pandemic in each country, but many countries experiencing later pandemic crises have the potential to reignite infections in earlier countries when borders are open, and there may be pandemics in future years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Turning to the controversy, Spiegelhalter (2020a) in a Guardian article on 30th April 2020 made valid points about data definitional differences and poor collection of data across some countries of Covid-19 infection and mortality rates. We are in agreement on this, but although he discusses the more reliable data on excess mortality, he argues that we will have to wait for months if not years before we can begin making useful comparisons across countries. However, given that the first wave of the pandemic in Europe has neared its end in most countries, "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""now"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" is a good time to make international comparisons at least within Europe.  Indeed, on 4 May 2020, a letter"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" from three statistics professors, Philip Brown, James Smith and Henry Wynn disputed Spiegelhalter’s claims saying: “Yes, there are inconsistencies, underreporting and heterogeneity within countries, but the policies adopted by different countries show very large differences in effects that would seem to dwarf such worries.” Their concern was that the article would deflect criticism of the political handling of the crisis (and indeed it had already in their view). They argue that comparisons combined with careful modelling are needed "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""now"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" to explain variations in mortality rates and infection rates across locations toward improved policy. They cite for instance a U.S. modelling endeavour, Rubin et al. (2020), the latest version of which analyses and forecasts US county level data on death rates, taking into account local factors across US counties including population density, incidence of smoking and social distancing as measured by cell phone movement data. The statisticians suggest that such modelling tools are appropriate to apply to country comparisons, and critical for modelling testing and tracing to the community level. We emphasise this modelling point more broadly in section 8."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To interpret large differences in excess mortality between nations requires consideration of several factors, and the within-nation deviations in these factors: the average infection rates in preceding weeks, average mortality risk from Covid-19 for those infected (the case fatality rate) and constraints on Covid-19-specific health capacity."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Turning to the first of the factors, consider differences in infection rates. Compare two countries or regions with the same average Covid-19 mortality risk where 1 percent of all adults are infected in A, while 5 percent are infected in B. Then the rate of excess deaths for adults measured by the P-score will be about 5 times as large in B in the weeks following the incidence of the infection. Countries that locked down early and had effective test, trace and isolate procedures kept down the average infection rate and hence the excess death rate."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Within countries, infection rates can differ. London’s higher excess mortality was influenced by higher initial imports of infections and a higher virus reproduction number given its high density and hard-to-avoid close physical contact on public transport and at work. Thus, countries that have a higher fraction of adults in locations or occupations where the virus can more easily spread will tend to have higher excess death rates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Mortality risks for infected adults, the second of the factors mentioned above, can differ between and within countries. For example, the percentage increase in mortality risk may be greater for some ethnic groups, or for some co-morbidities such as diabetes or pre-existing lung conditions. Then country differences in the prevalence of obesity and smoking will influence comparative excess mortality. Lastly, a country’s excess mortality is further driven up, and potentially "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""much "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""further, by limited Covid-19-specific health capacity. The death rate among infected adults depends on capacity constraints on numbers of hospital beds and staff, numbers of ventilators, PPE, testing and logistical failures in delivery, e.g. to care homes. Given similar initial capacities, a country with a higher average infection rate will be more likely to run into these constraints. By the same logic, given the same high infection rate, a country with lower health capacity would have a higher rate of excess mortality. This is why there is such a focus on ‘flattening the pandemic curve’. Different capacity constraints can have different implications for different groups. For example, lack of PPE and testing facilities in care homes will have disproportionately larger effects on mortality for the oldest individuals and this could affect country comparisons."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Covid-19, therefore, interacts with the age distribution, the nature of health service delivery, poverty and inequality, ethnic and occupational structures, air pollution, the relative size of major conurbations and so on. Comparing rates of excess mortality statistics within countries by age groups, by city size and by occupational, social and ethnic groups should generate important insights for future pandemic policy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Finally, it should be considered whether excess mortality statistics alone are sufficient to measure the impact of a pandemic. The health economics literature has given attention to Quality Adjusted Life Expectancy (QALY) as a criterion for expenditure on health-improving policies. QALYs measure the number of reasonably healthy years a person might expect to live. The number of QALYs lost could supplement the increased death count resulting from the pandemic as a measure of its impact. However, detailed actuarial and medical information is entailed in the complex estimation of the number of QALYs lost. QALYs and the attachment of monetary values to QALYs have long been controversial, see Loomes and Mackenzie (1989), but the concept of a QALY does focus attention on the relative value (by age group) of expected years lost in a pandemic. The excess mortality of working age adults with a normal life expectancy of 30 years might be weighed against the excess mortality of 85-year olds with a life expectancy of 5 years. If the choice is to attach more weight to excess mortality for working age adults this will affect comparisons of countries with different age-specific mortality rates, see section 7."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""4. Comparability of statistical measures of excess mortality and other data issues to consider"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""4.1 Can we compare the different statistical measures for excess mortality (from all causes) across countries?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Comparisons between relatively homogeneous countries with moderate population sizes (such as European countries, Japan and Korea) and large countries such as China and the U.S., which span very diverse regions with potentially very different timings and incidence of the pandemic, are necessarily difficult. For the latter, it makes far more sense to compare populous regions or states with nation states of comparable scale."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""P-scores, per capita measures of excess deaths and Z-scores use the concept of ‘normal deaths’ in their numerator by comparing raw death figures with what would normally have been expected. Assuming that the data definitions for the death counts, such as the definition of the week, type of death count data collected (registration versus occurrence data, see below) and timeliness of the collection, are identical across countries (which they are not, see the next sub-section), we consider the relative comparability of the statistical measures described in section 2. For any measure, it is clear that cumulating actual deaths and normal deaths over the period of the first wave of a pandemic gives a more robust summary of its impact, as compared to examining only the peak week."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Comparability of P-scores and variant P-scores"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""P-scores "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""are robustly comparable across countries, with the caveat that the measure of ‘normal deaths’ is likely to be only approximate (see below). However, the underlying death count data do need to be transparent and fully comparable to make the comparisons valid, see section 4.2."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Normal death rates "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""already "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""reflect persistent factors such as the age composition of the population, the incidence of smoking and air pollution, the prevalence of obesity, poverty and inequality, and the normal quality of health service delivery. This makes P-scores particularly attractive even if age compositions and other persistent factors differ. Since they measure the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""percentage deviation "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""compared to what is normal, these persistent differences will already be incorporated in the definition of the ‘normal’ death rate."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Variant P-scores add an allowance for historic data variability to the normal number of deaths to define an upper threshold (supposedly based on the 95 percent confidence interval around normal deaths). They define excess deaths relative to that threshold and scale by the same threshold to compute a percentage. The variant P-score is therefore always a bit below the simple P-score but tracks it closely. Because the variant is more complex, the simple P-score is preferable. It can always be accompanied by an indication of the margin of uncertainty around estimated normal deaths. When cumulated over a number of weeks, that margin of uncertainty falls so that there is then even less difference between the simple and variant measures (see Figure 3)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Comparability of the per capita excess mortality measure"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Scaling excess deaths by population is obviously better than attempting to compare crude excess death counts for countries with vastly different populations. However, countries with older populations will tend to have higher normal death rates. This automatically means that countries like Italy with an older population will have higher measures of per capita excess mortality than countries with younger populations, such as England. Therefore, comparisons of per capita excess mortality need to be made with caution. A possible argument in favour of per capita excess mortality is that total population could be regarded as a rough proxy for the ability of the society to absorb excess deaths. However, on that logic, dividing excess deaths by the working age population would make more sense."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Comparability of Z-Scores"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As explained in Box 1, Z-scores deflate excess deaths by the standard deviation of normal deaths. In principle, given the assumption of the Poisson distribution, see Box 1, "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Z-scores "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""should not be compared across countries of very different sizes, though they are useful for comparing the profile of weekly excess deaths for an individual country. The reason is, that countries with small populations and therefore more noisy weekly counts of mortality, have higher standard deviations relative to normal deaths than the more populous countries. In practice, due to the inappropriate assumption of the Poisson distribution (see Appendix 1), the excess mortality rankings between countries are more similar to the P-scores than expected."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Poisson is likely to be poor approximation to the stochastic process for number of deaths, even in what EuroMOMO call "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""normal "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""seasons. EuroMOMO exclude Winter and Summer because of systematic shifts in mean deaths due to ‘flu, bad weather or heat waves. But it seems extreme to assume there are no systematic shifts in mean deaths throughout Spring and Autumn. If there are excess deaths due to a bad ‘flu in Winter, then in Spring below-average excess deaths should result. There are other examples, such as a measles outbreak, or changes in support for the homeless or for care homes (e.g. from fiscal austerity measures), that may affect mortality rates. There could also be time-varying clusters of different influences - such as a varying previous exposure to risks such as smoking - among the most vulnerable age groups. Thus, the constant "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""mean"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" assumption is almost certainly wrong. Turning to the weekly "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""standard deviation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" for ‘normal’ seasons used by EuroMOMO to deflate the Z-score (see Box 1), variations in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""systematic factors"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" such as these which shift the mean will be included in the measure, as well as random noise (see Box 2). Hence, Z-scores include these systematic features in the denominator and numerator. The paradox is that this makes the Z-scores somewhat more comparable for countries of different sizes (see Appendix 1). The Z-scores indicate approximately (given the Poisson assumption) in which weeks excess deaths were statistically significant;  hence they can in principle distinguish those countries with few, if any, weeks of excess deaths (e.g. Germany), from countries with many weeks of excess deaths (e.g. Belgium), irrespective of their large population size differences."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another major defect of Z-scores, compared to P-scores and per capita excess death measures, is that their cumulation over multiple pandemic weeks is problematic. While excess deaths can be cumulated, the standard deviation of normal deaths cannot, and, in any case, EuroMOMO do not report either excess deaths or these standard deviations. This makes it hard to obtain a comprehensive summary of the pandemic’s impact from the Z-scores."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""gray-section"", ""items"": [{""text"": [{""text"": ""Box 2: Two pieces of evidence against the Poisson assumption used in EuroMOMO Z-scores"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""expandable-paragraph"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""We consider two pieces of evidence against the assumption of a Poisson distribution by EuroMOMO. Both show there are common systematic factors driving mortality data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""numbered-list"", ""items"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""We examine the correlations of Z-scores within the UK"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". If there are systematic sources of variation of death rates, as well as pure noise, these systematic factors for the UK regions are very likely to be "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""correlated"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". On 100 observations, 2015-2019, excluding winter and summer weeks as for EuroMOMO, the correlation matrix is:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""rows"": [{""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": []}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""England"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Wales"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Scotland"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""N. 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Thus, the Poisson distribution cannot be correct as it assumes independence between regions and over time. Moreover, simple regressions between the Z-scores for Wales, Scotland and N. Ireland and that for England, give coefficients, respectively, of 0.32 (0.089), 0.30 (0.088) and 0.29 (0.092), with standard errors in parentheses. In reverse, a multiple regression of the Z-score for England on all the others gives: Wales 0.29 (0.097); Scotland 0.25 (0.099); N. Ireland 0.24 (0.094)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""2. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""We examine the ratios of Z-scores to P-scores"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "".  If they shared the same concept of normal or expected deaths, the Z/P ratio would equal the ratio of ‘normal’ deaths to their standard deviation. Under the constant mean Poisson assumption, this ratio would be proportional to the square root of the number of normal deaths. We lack access to EuroMOMO’s estimates of the normal number of deaths, but these should be close to the previous 5 years’ average. The ranking (high to low) of the estimated Z/P ratios in the peak week of the pandemic for the different countries, should be the same as their ranking by the normal number of deaths. EuroMOMO adjusts the Poisson assumption with a small allowance for extra dispersion but this should not affect the ranking."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The table shows that the expected ranking if the adjusted Poisson assumption were true is far from being confirmed by the evidence. One should expect Belgium to have the lowest Z/P and France the highest, with Italy the second highest, within Europe. Instead, Italy has the lowest, despite its relatively large number of normal deaths. Within the UK, with the exception of Wales, the rankings of ratios of Z/P do follow the rankings by population size and normal death counts. Regions with small populations - hence small numbers of normal deaths - should have "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""somewhat"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" noisier death rates since the purely random component of deaths would be larger compared to the systematic component. But only if the systematic component were zero would the ratio of the standard deviation to normal deaths be entirely determined by the normal number of deaths. Appendix 1 spells out the same argument somewhat more formally."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Peak weeks of excess mortality: country P-scores and Z-scores compared"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""rows"": [{""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Peak weeks"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Excess mortality scores"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}, 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These definitional differences need to be highlighted and made transparent across country data providers and international organisations reporting excess mortality statistics. The transparent reportage of the Human Mortality Database is exemplary in this regard."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""The accuracy of the basic data collected"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Perhaps the biggest single pitfall for comparability may arise from the accuracy of the raw mortality data. In our VoxEU article (Aron and Muellbauer, 2020a) we highlighted the advantages of excess mortality data over recorded Covid-deaths, see also section 1, assuming that the collection of data on deaths from all causes would be relatively up-to-date and complete."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Yet countries differ in the efficiency of their death registration systems, particularly where those systems are devolved to regional or local administrations. Then, problems in one location can affect or delay the nationaI data, and sometimes the national recording system can be slow to absorb regional information. In a pandemic, it can happen that the capacity of systems is temporarily overwhelmed, most of all in hotspots, often in urban areas. Occasionally the recording methods may be so weak overall, that the observers resort to data on burials."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The most striking recent example of revisions in the raw mortality figures is that for Spain announced on May 27"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""th"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}, {""text"": "". Raw deaths were suddenly revised up by around 12,000, back to early March. Catalonia, whose capital is Barcelona, accounted for well over half of these increases, followed by the regions of Madrid and Castilla La Mancha. A closer look at the data revisions by age shows that the bulk of the revisions were for those aged 75 or more. This is consistent with news reports of the many deaths in care homes."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" As we shall see, the upward revision in the Spanish data currently places Spain neck and neck with England as the European country with the highest cumulative P-score for the ‘all ages’ group (Table 2), whereas previous data put England’s all-age P-score well ahead."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Lag between occurrences versus registration data on death counts"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another difference is between the death counts by week of registration of the death and week of actual occurrence of the death. The registration data occur later than the occurrence data. EuroMOMO Z-scores apparently use data by occurrence for all reporting countries, see Table 1."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" HMD use occurrence data for most countries, with the exception of England and Wales."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The occurrence-data are particularly prone to revision, and with the lags of registration data behind occurrence data often increasing during the height of a pandemic. Comparability in dating the peak week of mortality is sensitive to how the data are recorded. For example, in the UK, the peak week for all underlying regions is week 15 using occurrence data, as for the EuroMOMO Z-scores in Table 2. By contrast, death counts based on registration data for the UK show peak weeks of week 17 for N. Ireland, week 16 for England and Wales and week 15 for Scotland, see Table 2. Figure 2 compares for England the occurrence and registration data in calculated P-scores."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is also important to be cautious when comparing cumulative P-scores across countries if the pandemic has not yet run its full course in some countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Figure 2: Peak of pandemic occurred earlier than when registrations were recorded: contrasting ‘all age’ excess mortality P-scores for England by registration or occurrence data"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-15"", ""children"": [{""children"": [{""text"": ""15"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Figure-2.png"", ""hasOutline"": false, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Measurement of ‘normal deaths’"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The 5-year average could be a crude estimate of normal deaths, e.g. if there are time trends in mortality. If mortality is on an improving trend, normal deaths would be over-estimated by the 5-year average. On the other hand, where populations are increasing or are ageing, the count of normal deaths could also be rising. EuroMOMO use statistical models to adjust for such trends but do not provide their estimates of ‘normal’/expected deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If spring is especially warm as has been the case in Europe in 2020, it is possible that the 5-year average overestimates expected deaths, taking the weather into account. In the latter case, the simple P-score would then underestimate the impact of the pandemic. Also note that not just the effects of the pandemic but of societal reactions, whether driven by government regulation or private behaviour, will be reflected in the death count. Greater social distancing, lower rates of traffic accidents and of deaths due to alcohol abuse as well as ‘collateral damage’ will all affect the death count."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Definition of the week"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Countries differ in how they define the week. The mostly widely accepted international definition starts the week on Monday and ends on Sunday. However, of the countries we compare, England, Wales and Northern Ireland start the week on Saturday and ends it on Friday, while all the others, including Scotland follow international practice. This is a relatively minor issue and largely washes out when cumulating excess deaths over multiple weeks, e.g. eleven weeks."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""5.  Why the age distribution matters"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Differences in the age distribution between countries would be irrelevant if mortality risk increased in the same proportion for all. This can never be the case because children have a far lower mortality risk. In countries where children make up a high proportion of the population, the P-scores and excess mortality relative to the total population for the all ages group will be lower."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Looking only at the adult part of the population in a pandemic, there is strong empirical evidence "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""against "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""the hypothesis of a proportionate increase in mortality risk at all adult ages. We cannot be sure to what extent this is due to differences in rates of infection or differences in mortality risk once infected."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-16"", ""children"": [{""children"": [{""text"": ""16"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The evidence in section 7 for six countries is for a more than proportionate increase for older adults, i.e. the group of older adults (85+) has a higher P-score than the group of younger adults (15-64). Comparing two countries with the same age-specific P-scores, the country with the higher proportion of older adults would then have a higher all-age adult P-score."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Countries also differ in the age-profile of P-scores. One can see this when comparing the ratio of the P-score for the group of working age adults to that of the group of older adults, e.g. those over 65 or over 85. This ratio is less than 1 everywhere, but some countries have far lower P-scores for working-age adults relative to older adults. To see the implications, take a simple example of two countries with the same age-structure of young and old adults. Suppose the P-score is 1 for the old in both countries, but that country A has a P-score of 0.1 for young adults while that for country B is 0.3. The overall P-score for country B will clearly be higher than for country A. However, if country B also has a higher fraction of young adults, that will attenuate the difference in the overall P-scores between the two countries. Thus, differences in age distributions between countries will affect the measured all-age P-scores and this should be recognised when comparing P-scores."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One could envisage an ‘age-standardised P-score’, adapting the ‘age-standardized mortality rate’, sometimes used to examine the impact of a pandemic. The latter is a weighted average of the age-specific mortality rates per 100 000 persons, where the weights are the proportions of persons in the corresponding age groups of a standard population. The WHO explains the rationale: “Two populations with the same age-specific mortality rates for a particular cause of death will have different overall death rates if the age distributions of their populations are different. Age-standardized mortality rates adjust for differences in the age distribution of the population by applying the observed age-specific mortality rates for each population to a standard population.”"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-17"", ""children"": [{""children"": [{""text"": ""17"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" A theoretical population, the European Standard Population (ESP), is widely used in Europe to compute age-standardised death rates. This has a particular distribution by age, averaging data from across Europe. The current version from Eurostat was introduced in 2013. The ONS in the UK has also used age-standardised death rates to compare mortality risk from Covid-19 between the UK regions or between locations with different levels of economic and social deprivation."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-18"", ""children"": [{""children"": [{""text"": ""18"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, the ‘age-standardized mortality rate’ unfortunately conflates variations in normal mortality risk with variations in risk of death during a pandemic. Thus, if the age-standardised mortality rate in 2020 is higher in region A than in region B, this does not necessarily indicate that the Covid-19 mortality risk is higher in A. It may be that normal mortality risk, e.g. based on the average of the previous 5 years, is higher in region A than in B. Age-standardisation removes that part of the difference due to differing "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""age structures "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""of the two populations; but it does not remove from normal mortality risk the socio-economic differences, and differences in the incidence of obesity or smoking and in health provision."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""An ‘age-standardised P-score’ would give a better grasp of the increased mortality risk due to Covid-19 than the ‘age-standardized mortality rate’. The P-scores for each age group could be computed and the weighted average taken using the age structure of the reference population, rather than of the region or country being considered. It is a better concept because it compares the age-standardised mortality rates during the pandemic period with those normally expected. This type of P-score would provide a provisional answer to the question: ‘how different would the overall mortality rate have been with a different age structure of the population?’"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are also potentially "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""other "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""ways of standardising aggregate P-scores (or mortality per 100,000 of population) to remove part of the source of between-region or between-country variation. For example, one could standardise by proportions of the population resident in towns and cities classified by common size categories."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The simple aggregate P-score (which weights the age-specific P-scores by the fraction of the population in each age group) and these various standardised aggregate P-scores (which weight the age-specific P-scores by the fraction of the population in each age group in a hypothetical population) have intuitive appeal and can be informatively compared across countries. However, one has to be aware of the limitation of any single measure of comparability between countries. Subsumed within the aggregates are implicit value judgements. For example, crucially in the case of a pandemic, there is an implicit assumption that the toll of an older life lost is the same as that of a younger life. However, when a younger life is lost, many more years of life expectancy are lost, and one might want to attach a larger weight to deaths of the young, see section 3. An important argument of the lockdown sceptics is an extreme version of this last point: “the virus is mainly killing off those that were on their way out anyway”, see Kelly (2020). This article quotes a major downward revision of his estimates by British statistician, David Spiegelhalter, who initially suggested that a large number of those dying of Covid-19 would have died in the coming year in any case, but now suggests about 5-15 percent but less than a quarter."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-19"", ""children"": [{""children"": [{""text"": ""19"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" On the 11"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""th"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}, {""text"": "" June, cancer specialist Karol Sikora stated for the Telegraph that at least half of those dying of Covid-19 would have died anyway by the end of the Summer of 2020. To try to get a clear position on the issue, Tim Harford (who should be credited for his contribution to the public understanding of data, probability and risk), invited actuary Stuart McDonald"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-20"", ""children"": [{""children"": [{""text"": ""20"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" to comment in the BBC programme “More or Less”."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-21"", ""children"": [{""children"": [{""text"": ""21"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" McDonald disagreed with the assertion that a majority would have died in the next 3 months as it was neither supported by the data nor his own research. While it is true that three-quarters of the excess deaths were of people aged 75 and above, and that the majority had one or more pre-existing medical conditions (co-morbidities), in practice, life expectancy is quite high. For example, at the age of 80, life expectancy is 9 years for males and 10 years for females. Co-morbidities add little to this, in his opinion, since four-fifths of this cohort has two or more co-morbidities, and 90 percent have one or more (there is of course variation around the average). He stated that it was hard to find examples of less than two years’ life expectancy. From detailed data in the insurance industry, he suggested that an obese male smoker aged 80, and even with heart or pulmonary disorders, would still have a life expectancy of at least 5 years. This suggests that the pandemic had a huge impact not just on the death count but on life-years lost, properly measured."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Granular data, disaggregating by region, age and gender, as beginning to be provided by Eurostat (see Table 1), allows the observer to apply their own value judgements. These data, combined with medical information at the country level, would be a crucial input in estimates of life-years lost, alongside counts of excess mortality. Granular data are more informative for evaluating the effectiveness of the policy response and for enhancing scientific understanding to inform policies on ending lock-downs and reducing the risk of a second wave of infections."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""6.  What can we learn from a comparison of the P-scores from the ‘all ages’ data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Cumulation of the P-scores over time is required to get a comprehensive summary measure of the impact of the pandemic. Looking at comparisons over a single week or two, for example, is insufficiently reliable as there is much variation over individual weeks. Different observers choose different periods to define the beginning and end of the pandemic, for instance beginning with the day when the first Covid-19 deaths or first 50 such deaths were registered. In contrast, we frame our comparisons using the same length of period for each country that we are comparing. We use 11 weeks, which is a comprehensive period to measure the extent of the first wave of the pandemic in European countries (not long enough for the US). The actual weeks chosen differ by country: the timing matches the P-scores. Cumulating the P-scores for ‘all ages’ data shows, see Figure 3, that England is slightly ahead of Spain, but that they are ‘neck and neck’. There is also little difference between the two types of P-scores (ordinary and variant) in terms of ranking. Italy, Belgium, the Netherlands and France follow Spain, while within the UK, Scotland, Wales and N. Ireland follow England."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One caveat is that the English data are from registration data and not occurrence data (see section 4.2). Therefore, the timing of the England peak cannot be compared with the timing of the peak for the other European countries which use occurrence data, since registration of death follows after occurrence of death, with a lag."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Examining the detailed P-scores by week for England and the rest of the UK, and the other European countries, it is clear that the peak incidence in Spain is more severe, but more protracted at high levels of deaths in England (Figures 4a and 4b). The same comparison applies to Belgium and Italy, with the latter more protracted. The incidence is quite a bit lower in N. Ireland, which follows Wales and Scotland, behind the England."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The detailed numbers behind the pictures are contained in Table 2. The Z-scores from EuroMOMO are also presented. 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Ireland"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-24"", ""children"": [{""children"": [{""text"": ""24"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Figure-4b.png"", ""hasOutline"": false, ""parseErrors"": []}, {""type"": ""gray-section"", ""items"": [{""text"": [{""text"": ""Table 2: Our P-scores/variant P-scores and EuroMOMO’s Z-scores for poor performers showing peak weeks of excess mortality in the first wave of the pandemic"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""expandable-paragraph"", ""items"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Sources and Notes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""url"": ""#note-25"", ""children"": [{""children"": [{""text"": ""25"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""P-Scores [these use data on deaths by week of occurrence– except for the UK which uses data on deaths by week of registration]"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""rows"": [{""type"": ""table-row"", ""cells"": [{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""All age-groups"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Week 10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Week 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{""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""-1.69"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""0.12"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}, {""type"": ""table-cell"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""-3.36"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}]}]}], ""size"": ""narrow"", ""type"": ""table"", ""template"": ""header-column-row"", ""parseErrors"": []}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""7.  Excess mortality for other age groups: 15-64 and 85+"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Here, we focus on two age groups, those aged 15-64, containing most of the working age population, and the elderly, those aged 85 or more, many of whom will have been residents in care homes. The evidence here confirms the point made in section 5, that the percentage increase in mortality risk due to the pandemic, measured by the P-score, was higher for older ages. As in section 6, we present the cumulated P-scores over time to get a comprehensive summary measure of the impact of the pandemic for the two age groups. We use the same length of period, 11 weeks, for each country, sufficient to measure the extent of the first wave of the pandemic, though the actual weeks chosen will differ by country as before (see Table 2). What differs from section 6 is that for reasons of data access, ‘England and Wales’ as an entity are examined here, rather than England alone and other regions of the UK. Cumulating the P-scores for both age groups in Figure 5, shows that in all countries, P-scores are lower for the 15-64 age group than for the 85+ age group. ‘England and Wales’ lies slightly below Spain for the 85+ age group but is well above it for the working age group of 15-64. In ranking, Belgium, Italy, France and the Netherlands follow Spain and ‘England and Wales’ for the older age group. But Belgium, France and the Netherlands seem to have sustained far lower deaths than Spain and Italy, and especially ‘England and Wales’, amongst the working age population group."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is unclear to what extent these striking differences are due to differences in rates of infection or differences in mortality risk once infected. Over the 11 pandemic weeks, the cumulative P-score for the 15-64 age group in France was negative, though in the middle of the period there were some weeks when it was positive, see Figure 6. This suggests that social distancing and related measures in France may have reduced deaths from other causes for the working age population, which actually saved lives over the first-wave pandemic period. The Netherlands and Belgium also have remarkably low cumulative P-scores for the 15-64 age group and a number of weeks with negative P-scores."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The increase in expected years of life lost, is another measure of the pandemic’s impact (section 3). Average life expectancy in the 15-64 age group is obviously substantially higher than the average for the 85+ age group, so many more expected years of life are lost in each excess death among the younger group than among the older. From the higher incidence of deaths among the working age population in England (which dominates the ‘England and Wales’ figures), it is obvious that England is easily the worst in Europe in terms of expected years of lives lost."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Turning to the timing of the pandemic’s incidence, the ‘England and Wales’ data are from registration data and not occurrence data (see section 4.2). Since registration of death follows after occurrence of death, with a lag, the timing of the England and Wales’ peak occurs around one week after its occurrence data, which in turn is later than the peak in most European countries. The timing of the peak week is mostly the same for the two age groups. It is led by Italy in week 13, followed by Spain and France in week 14, the Netherlands in weeks 14-15, Belgium in week 15 and England and Wales in weeks 16-17 (but week 15 according to the occurrence data in section 4.2)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Turning to the detail in Figure 6, the peak incidences for the 85+ age group in Spain and in Belgium are more severe, but for ‘England and Wales’ the pattern is more protracted at a high level of deaths. The same comparison applies to France and the Netherlands versus Italy, with the last more protracted. Italy initially dominated the headlines for Covid-19-related deaths but ranked fourth for peak excess mortality figures for the over-85s, below Spain, ‘England and Wales’ and Belgium."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most disturbing, as noted above, is the comparative story for the 15-64 age-group, where England’s relative record in excess mortality in the Covid-19 era is strikingly higher than in the European countries. The 15-64 age-group includes the mass of the working age population. For this age group, the weekly pattern is rather different than for the over-85s, with ‘England and Wales’ displaying both a high peak incidence and protracted high level of deaths, followed by Spain and then Italy. Figure 6 shows that not only is England distinctive in the rate of excess mortality in the peak week for the working age group, but the same is true in comparisons of the two weeks before the peak and the subsequent week."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The EuroMOMO graphic visualisations by finer age categories can offer further clues, comparing the 15-44 and 45-64 age groups. Section 3 suggested that comparisons of Z-scores for comparably populous countries and those with larger populations yields reasonable approximations in ranking. England and Spain were the only countries with significant excess mortality in the 15-44 age group according to Z-scores, with England far ahead of Spain. Comparisons of Z-scores with less populous states tend to understate excess mortality in the latter, but evidence from the large countries France and Italy suggest that England is a European outlier. While Z-score comparisons with Wales, Scotland and Northern Ireland understate their excess mortality, the differences compared with England are so large that the conclusion that England was exceptional cannot be avoided. For the 45-64 age group, there is evidence of significant levels of excess mortality, at least in the peak weeks of the pandemic, for all the countries in our comparison group of countries with the exception of Northern Ireland. The Z-score evidence is consistent with the patterns in Figure 6 for the 15-64 age group, even if the Z-scores for the smaller countries, Belgium and the Netherlands slightly understate their relative excess mortality. While the Z-scores also understate excess mortality for the 45-64 age group in Scotland, Wales and Northern Ireland, the figures for England are so much higher, that its outlier status is confirmed for this age group as well as the 15-44 age group."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These country differences call for further analysis, especially by age and by regional differences within countries (contrasting, for example, regions with large urban centres and those without). It would be interesting to know to what extent working age excess mortality in London dominated the data for England. It is also possible that cramped housing conditions in London, especially for poorly paid workers, accounts for some of the exceptionalism of the data for England. Regional and country differences by occupational categories should also be illuminating. Aron and Muellbauer (2020b) drew attention to evidence for England and Wales of major occupational differences in the incidence of deaths attributed to Covid-19 and in age-standardised death rates. Of the countries in our comparison group, England and Wales (and Scotland) have the highest ratios of prison population to total population, followed by Spain."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-26"", ""children"": [{""children"": [{""text"": ""26"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Further analysis is needed of excess mortality in the prison population as it is possible that failures to protect inmates from infection in countries with high infection rates could help explain some of the country differences of excess mortality for those of working age."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""7.1 Toward comparable international statistics on excess deaths amongst care home residents"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One of the stark differences between countries is how well protected were residents in the care homes. The main elements of what happened in care homes in the UK, France, Italy and in Spain is, by now, well-known. Care home staff had inadequate personal protective equipment (PPE) and inadequate access to Covid-19-tests and residents were not well-shielded from potential infection from visitors and staff. Yet, many elderly patients with the Covid-19 infection were released from hospitals to the care homes to reduce the pressure on hospitals from the volume of new cases, and therefore spread the infection to other residents. It is important to explore comparisons between countries of their excess deaths in care homes, for example at the least, the percentage of cumulative Covid-19 deaths that occurred in care homes. The clues in the rate of excess deaths for the 85+ age group, which show the largest increase in Spain, are consistent with newspaper reports of the disaster that befell many care homes in Spain."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We were not able to find comparable data at this stage for excess deaths of those normally resident in care homes across the European countries. However, considerable strides have been made in improving international comparability through the pioneering work of the International Long-Term Care Policy Network, e.g. Comas-Herrera et al. (2020). For international comparability, counts of deaths of those resident in care homes, plus those "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""normally "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""resident in care homes but dying elsewhere (e.g. in hospital), would have to be regularly published. Few if any countries currently do this. To compute the percentage of excess deaths in care homes or for the comprehensive definition which includes deaths of care home residents outside the care homes, requires data for the previous five years to be able to estimate ‘normal’ deaths."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-27"", ""children"": [{""children"": [{""text"": ""27"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Another issue for international comparability concerns differences in definitions of what constitutes a care home. A focus on those over 65 or 75 years of age to exclude some of the other groups, such as refugees, sometimes included in the care home definition, could help international comparability."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is interesting that England and Wales have some of the most comprehensive data on mortality in care homes internationally, see Comas-Herrera et al. (2020). They cite ONS data showing that from early March to 12 June 2020, excess deaths in care homes in England and Wales numbered 26,745, where total excess deaths for England and Wales were 59,138. Thus, about 45 percent of total excess deaths took place in care homes. The ONS have not produced data on excess deaths among those normally resident in care homes, however, clearly a higher percentage as some may have died elsewhere. We would like to know what fraction of excess deaths were of care home residents (within the home or out of it, say in hospital). The Care Quality Commission (CQC) estimates that 84 percent of total care home residents’ deaths took place in care homes in the same period. But this includes normal deaths that would have occurred in the absence of the pandemic, as well as the deaths induced by the pandemic (Covid-19 attributed deaths, mis-measured, unattributed Covid-19 deaths and those caused indirectly by Covid-19, through being untreated, for example). To correct the estimate of 84 percent for normal deaths included in it, and to include deaths of care home residents outside the homes, we consider CQC data on Covid-19-attributed deaths as follows. For the period from early March until the 1 May, the CQC estimate that 72 percent of Covid-19-attributed deaths of care home residents occurred in care homes. They give figures for England alone, from 2 May to 12 June, of and 77 percent. Scaling up the above figure of 45 percent of total excess deaths that took place "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""in"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" care homes for England and Wales, by the 84 percent figure, i.e. 45.2/0.84, would give an estimate of 54 percent for the percentage of all excess mortality accounted for by care home residents in England and Wales (whether inside or out of the care home at time of death). This would almost certainly be an underestimate, since the 84 percent is an over-estimate, but the 54 percent estimate gives a lower bound."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To potentially correct the estimate of 84 percent for the normal deaths included in it, and to include deaths of care home residents outside the homes, we consider the specific CQC data on Covid-19-attributed deaths as follows. For the period from early March until the 1 May, the CQC estimate that 72 percent of Covid-19-attributed deaths of care home residents in England and Wales occurred"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" in"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" care homes. Their equivalent figure for England alone, for the later period of 2 May to 12 June, is 77 percent. However, if the CQC estimate of 77 percent better represented the fraction of excess deaths of care home residents that took place in care homes than the 84 percent figure used above, then 58 percent (i.e. 45.2/0.77), would be the estimate of the fraction of all excess deaths accounted for by residents of care homes (whether inside or out of the care home at time of death)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Although Comas-Herrera et al. (2020) examine data sources for 27 countries outside the UK, the only other two countries found with data on excess deaths in care homes are Belgium and France. In Belgium the attribution of deaths to Covid-19 is so widely-defined that the count of Covid-19 attributed deaths actually exceeds the count of excess deaths, see Figure 1 above. For Belgium, Comas-Herrera et al. (2020) report that care home residents accounted for 64 percent of all deaths linked to Covid-19. This suggests that the percentage of excess deaths accounted for by care home residents in Belgium is not far from the 64 percent figure. They report for France that care home residents accounted for 49 percent of Covid-19 deaths. However, since the count of Covid-19 deaths understates excess deaths in France, see Figure 1, it seems likely that a higher percentage of excess deaths occurred among care home residents. For Canada, estimates suggest 81 percent of Covid-19 deaths were among residents in long-term care, but comparable estimates for excess deaths are not available."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We can obtain a little more information for the UK by examining data in Table 4 for the four nations comparing the total excess death count in each with information on the location of Covid-19 attributed deaths. The period covered is weeks 13-23 of the pandemic (for dates, see Table 2). For the UK as a whole, 80 percent of excess deaths have been attributed to Covid-19, though for Wales the percentage was far higher."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-28"", ""children"": [{""children"": [{""text"": ""28"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" For the UK nearly half of excess deaths attributed to Covid-19 occurred in hospital and one quarter in care homes, though many of the hospital deaths were of patients who were resident in care homes. The remaining 20 percent may also be related to Covid-19, as unrecorded or mis-recorded deaths, and those indirectly affected by Covid-19 through other health conditions, such as heart conditions and cancer, being left untreated due to implied capacity constraints in the health service."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The percentage of excess deaths that took place in care homes from Covid-19 in England, at about a quarter, matches the overall UK figure, but in Scotland and N. Ireland this was sharply higher at 39 and 35 percent, respectively, and in Wales about 30 percent. Concerning the number of Covid-19 deaths, 30 percent of these occurred in care homes in England and in Wales, with 47 percent in Scotland and 43 percent in Northern Ireland. These percentages of Covid-19 deaths are an underestimate of those normally resident in care homes, because some died in hospital. Hopefully, the compilation of those data will be undertaken by the ONS and the regional health authorities, so that the scale of excess deaths in care homes and its regional variation is properly appreciated."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Figure 5: Cumulative P-scores of excess mortality for poor performers by two age groups"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-29"", ""children"": [{""children"": [{""text"": ""29"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Figure-5.png"", ""hasOutline"": false, ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Figure 6: Recent weeks of P-scores for poor performers showing peak weeks of excess mortality by age-group"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-30"", ""children"": [{""children"": [{""text"": ""30"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Figure-6.png"", ""hasOutline"": false, ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Figure 7: Total COVID deaths as a share of excess deaths for the UK (‘all ages’): cumulated over pandemic weeks."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-31"", ""children"": [{""children"": [{""text"": ""31"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Figure-7.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""8.  International/national statistical agencies should publish improved measures of excess mortality"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Even if we deem the P-scores and the population-deflated statistics to be comparable across countries, underlying measurement issues of the death count strongly affect the comparability across countries. These definitional differences need to be highlighted and made transparent across country data providers and international organisations reporting excess mortality statistics. The transparent reportage of the Human Mortality Database (HMD) is exemplary in this regard."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The impact of the pandemic on deaths has been very strongly related to age and co-morbidity. The proportions of people with one, two or more co-morbidities is highly related to age. The discussion in the previous section highlighted striking differences between countries in age-related P-scores. Publication of P-scores for different age groups in a standard format should therefore be a high priority for international comparability, and HMD is a good source for such data. The evidence is that Covid-19 death rates are substantially higher for men than for women, and how this gender issue varies across countries and over time remains to be explored."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The international NUTS classification of regions provides another comparable frame for international comparisons. As regions differ in their urban/rural structure, comparing regional data can give important insights into risk factors for death rates. Moreover, as the incidence of the pandemic differs in timing and intensity, regional comparisons can throw light on the dynamics of the spread of infections. Eurostat has embarked on a major expansion of regional mortality data according to the NUTS classification, which should greatly aid research."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another important source of variation across countries has been in the incidence of Covid-19 deaths in care homes. Countries undoubtedly differ in the proportion of older citizens resident in care homes. It would be highly desirable to develop an international standard frame to define what constitutes a care home, perhaps by the size-distribution of the number of residents. Then, comparisons of excess mortality in care homes would be possible. At present, there are limited internationally comparable data on deaths attributed to Covid-19 that occurred in care homes, see Table 4 for a UK comparison, but almost none on excess deaths of those in care homes or normally resident there."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Within countries such as the UK, there have now been several studies comparing the incidence of deaths attributed to Covid-19 by local measures of economic deprivation, occupation and ethnicity. It would highly desirable for parallel studies of excess deaths to be carried out. International comparability is harder in these dimensions given difficulties in standardising categories in measures of deprivation, occupational classification (sometimes not recorded on death certificates, but recoverable from census records) and missing data for some countries on the sensitive issue of ethnicity."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Considerable benefits can be reaped from tabulation, cross-tabulation and correlations, trying to control for common features like density by region, in proposing hypotheses. It is important to allow modellers ready access to transparent, comparable international data to a granular level to be combined with other granular data already available (e.g. on inequality) to test such hypotheses in models. Forecasting P-scores from epidemiological models for different scenarios on ending lockdown measures should be an important aid to formulating policy."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-32"", ""children"": [{""children"": [{""text"": ""32"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Granular data by location within and between countries must be produced and made accessible for research and forecasting. An example using granular Italian death registry data is Ciminelli and Garcia-Mandicó (2020)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-33"", ""children"": [{""children"": [{""text"": ""33"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Belloc et al. (2020) caution against drawing simplistic conclusions from cross-country correlations; they too stress the need for granular, comparable data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""National statistical offices should publish weekly P-scores of excess mortalities for the constituent countries, regions and broad social groupings such as care home residents, to help understand the pandemic and inform policy."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-34"", ""children"": [{""children"": [{""text"": ""34"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" We also argue that EuroMOMO should be mandated to produce P-scores as well as Z-scores to aid comparability across countries and be far more transparent on sources and methods EuroMOMO’s five-year graphs of Z-scores visualise the natural weekly variability, helping to interpret the confidence intervals. Similar practice should be followed for published P-scores, including at national statistical agencies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To end on a cautionary note, excess mortality should also be examined in a longer-term perspective. Spiegelhalter (2020) argues the main impact of Covid-19 may be to shift forward the date of death by a few months for those close to death because of underlying poor health. However, as discussed in section 6, expert actuaries strongly dispute his claim. Moreover, total years of life lost, see section 3, is an alternative indicator of the pandemic’s social toll. Even in the extreme and improbable case envisaged by Spiegelhalter, total years of life lost could still show a large upturn. As we saw in section 6, record excess mortality of those of working age in England, making this a particularly telling issue in comparing with other European countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If national statistical agencies regularly published monthly, 3-month, 6-month and 12-month moving averages, and weekly P-scores, this would greatly assist our ability to interpret the pandemic data."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-35"", ""children"": [{""children"": [{""text"": ""35"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Provision of timely, regularly updated and comparable granular data on excess mortality by national and international statistical agencies should be high on the agenda. It is not enough to leave this to hard-working journalists."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""We take responsibility for interpretations of data and analysis but are grateful for advice on data and other matters to Jose Manuel Aburto (Dept of Sociology, Oxford University), Ainhoa Alustiza Galarza (HMD), Nick Andrews (Public Health England), Gabriele Ciminelli (Asia school of Business), Adelina Comas-Herrera (Care Policy and Evaluation Centre, Department of Health Policy, London School of Economics and Political Science), Laurie Davies (Mathematics Department, University Duisburg-Essen), Francesca De’ Donato ("", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.eurosurveillance.org/search?option1=pub_affiliation&value1=Department+of+Epidemiology%2C+Lazio+Regional+Health+Service%2C+ASL+Roma+1%2C+Rome%2C+Italy&option912=resultCategory&value912=ResearchPublicationContent"", ""children"": [{""text"": ""Department of Epidemiology, Lazio Regional Health Service, Rome, Italy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""), Mark O'Doherty (Public Health Agency, Northern Ireland), Faisal Islam (Economics Editor, BBC), Dmitri Jdanov (HMD), Gareth John (NHS Wales Informatics Service), Ridhi Kashyap (Nuffield College), Amparo Larrauri (Departamento de Enfermedades Transmisibles, Centro Nacional de Epidemiología, CIBER  Epidemiología y Salud Pública, Spain), Diogo Marques (Public Health Scotland), Bent Nielsen (Nuffield College), Justine Pooley (ONS), Max Roser (Our World in Data, Oxford University), Charles Tallack (Health Foundation), and Lasse Skafte Vestergaard (Faculty of Health and Medical Sciences and Statens Serum Institut, University of Copenhagen.)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Acknowledgements"", ""parseErrors"": []}, {""type"": ""gray-section"", ""items"": [{""text"": [{""text"": ""Appendix 1"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""expandable-paragraph"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""Let x(it) be the weekly death count in country i in week t. It appears that EuroMOMO define"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-36"", ""children"": [{""children"": [{""text"": ""36"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" the excess death measure Z"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""(it)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" as:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Z(it) = (x(it) – μ(it)) / sigma(it)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""where μ(it) is the predicted value from a model based on historical data up to 5 years ago for seasons of the year less affected by flu and heat waves, and incorporates some trends and seasonals, and where sigma reflects the standard deviation of residuals, but is actually computed from a Poisson process modified for longer tails. Each country in the network estimates its own model within a broad methodology and supplies the hub with its weekly estimates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We think a more transparent and non-parametric measure is the P-score:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""P(it) = (x(it) – x ̅(it))/x ̅(it)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""where x̄(it) is the average weekly death count over the previous 5 years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There is also a parametric variant P"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""EM"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}, {""children"": [{""text"": ""(it)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" which could be defined on EuroMOMO’s data using their predicted values for ‘normal’ deaths as:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""P^EM (it) = (x(it) – μ(it))/μ(it)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Poisson assumption, even modified for longer tails, is nowhere near correct for describing the stochastic process generating x(it). The constant mean and independence over time assumptions must be wrong, as explained in Box 2 of the paper, which shows that it is implausible to assume that there are zero systematic mean shifts at all times in the Spring and Autumn. When EuroMOMO measure the standard deviation for ‘normal’ seasons, variation in these systematic factors as well as random noise will be present."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This suggests a better model of the death count is:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""x(it) = β × W(it) + ε(it)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""where "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""W(it)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""  is a set of variables which reflect the systematic component of variations in deaths and ε(it) is white noise whose distribution can be approximated perhaps by a Poisson or binomial or normal distribution, assuming a constant variance σ"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""(i)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}, {""text"": "". Then it is clear that EuroMOMO’s estimated sigma is an amalgam of the standard deviation, σ"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""(i)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", and of the variation of  "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""W(it)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" around an average value."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Our simple measure of the excess death rate, a P-score, is then:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""P(it) = [(β × W(it) - W ̅(it)) + (ε(it) - ε ̅(it))] / (W ̅(it) - ε ̅(it))"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""using 5-year moving averages for "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""W-bar(it)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""-bar"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When a pandemic arrives, "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""W"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(it) jumps far from its historical average. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""P(it)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" does a good job in indicating the jump in W. It is easily understood by non-specialists. The empirical properties of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""P(it)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" can be investigated. One would neither claim that it is serially independent, nor that it has constant variance as that depends on the properties of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""W(it)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". Econometricians could try to estimate "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""W(it)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" with a mix of deterministic variables and state-space terms, to try better to understand the stochastic process driving the death count."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Turning to comparisons between regions within a country, it is obvious that the smaller the population of a region, and in particular the smaller the number of normal deaths, the noisier will be the weekly death count relative to the normal expected value. In other words, the ratio:  "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Z"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(it)/P"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""EM"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}, {""children"": [{""text"": ""(it)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" = sigma(it)/μ(it), will be lower in smaller regions. One can extend the argument to populous countries compared to those with smaller populations, if overall normal mortality rates are similar. In practice, movements in P"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""EM"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}, {""children"": [{""text"": ""(it)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" will be very similar to movements in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""P"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(it), especially in pandemics, when the jump in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""W(it)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" dominates the variation in both. As a result of averaging data over sub-populations, σ"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""(i)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""/μ(i) at the country vs region level could be argued to vary approximately inversely with the square root of normal deaths for the country and region. This is a result which should not depend on the precise distribution of the white noise, constant variance process for ε(it), i.e. it should not depend on the assumption of Poisson.  However, the EuroMOMO estimate of the standard deviation  is a composite, as noted above, of σ"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""(i)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" and the variation in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""W"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(it) about its mean. Thus, it will vary far less with the level of normal deaths or population size than would be the case for σ"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""(i)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" alone. This is because, on a per capita basis, the systematic factors driving "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""W"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(it) under normal conditions are likely to be quite similar for different regions of a country. In a pandemic, however, the factors driving "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""W(it)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" can diverge more because, for example, infections spread from different starting points and at different rates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As our paper points out, the rankings of rates of excess deaths in the peak week for the most affected European countries according to Z are quite similar to those from P, even for countries such as Belgium and the Netherlands, which have smaller populations and hence smaller counts of normal deaths than the others. For nations or regions with much smaller counts of normal deaths, the rankings are different as the relative noisiness of weekly death counts compared to normal levels is higher. There is no simple adjustment to convert published Z-scores to P-scores without access to data on normal and actual deaths. In particular, it would be quite wrong to adjust the published Z-scores by the square root of population size of each country to make them more comparable. Comparability is best achieved using the P-scores."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Bibliography"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""ACN. 2020a. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.catalannews.com/society-science/item/coronavirus-crisis-shines-spotlight-on-elderly-care-homes"", ""children"": [{""text"": ""Coronavirus crisis shines spotlight on elderly care homes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "".” Catalan News, Barcelona, 1 April 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""ACN. 2020b. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.catalannews.com/society-science/item/prosecutor-investigating-handling-of-covid-19-in-seven-catalan-care-homes"", ""children"": [{""text"": ""Prosecutor investigating handling of Covid-19 in seven Catalan care homes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "".” Catalan News, Barcelona, 19 April 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Aron, J. and J. Muellbauer. 2020a. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://voxeu.org/article/excess-mortality-england-european-outlier-covid-19-pandemic"", ""children"": [{""text"": ""Measuring excess mortality: England is the European outlier in the Covid-19 pandemic."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” VOXEU, Centre for Economic Policy Research, London, 18 May, 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Aron, J. and J. Muellbauer. 2020b. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.inet.ox.ac.uk/news/inet-oxford-covid-19-blog/"", ""children"": [{""text"": ""Measuring excess mortality: the case of England during the Covid-19 Pandemic."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""”   INET Oxford COVID-19 Research, Economics Department, Oxford University."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Belloc, M., P. Buonanno, F. Drago, R. Galbiati and P. Pinotti. 2020. “Cross-country correlation analysis for research on Covid-19.” VOXEU, Centre for Economic Policy Research, London, 28 March 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Burn-Murdoch, J., V. Romei and C. Giles. 2020. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ft.com/content/6bd88b7d-3386-4543-b2e9-0d5c6fac846c?emailId=5ea6e9bcd26cbd000484719d&segmentId=2785c52b-1c00-edaa-29be-7452cf90b5a2"", ""children"": [{""text"": ""Global coronavirus death toll could be 60% higher than reported"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "".” Financial Times, 26 April 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Ciminelli, G. and S. Garcia-Mandicó. 2020. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://voxeu.org/article/covid-19-italy-analysis-death-registry-data"", ""children"": [{""text"": ""COVID-19 in Italy: An analysis of death registry data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "".”"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""VOXEU, Centre for Economic Policy Research, London, 22 April 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Comas-Herrera, A. and J-L. Fernandez. 2020. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://ltccovid.org/wp-content/uploads/2020/05/England-mortality-among-care-home-residents-report-17-May.pdf"", ""children"": [{""text"": ""England: Estimates of mortality of care home residents linked to the COVID-19 pandemic"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "".” Report available at LTCcovid.org, International Long-Term Care Policy Network, CPEC-LSE, 17 May 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Comas-Herrera A., J. Zalakaín, C. Litwin, A. T. Hsu, E. Lemmon, D. Henderson and J-L Fernández. 2020. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://ltccovid.org/2020/04/12/mortality-associated-with-covid-19-outbreaks-in-care-homes-early-international-evidence/"", ""children"": [{""text"": ""Mortality associated with COVID-19 outbreaks in care homes: early international evidence.”"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" Report available at LTCcovid.org, International Long-Term Care Policy Network, CPEC-LSE, 26 June 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Denaxas, S., H. Hemingway, L. Shallcross, M. Noursadeghi, B. Williams, D. Pillay, L. Pasea, A. González-Izquierdo, C. Pagel, S. Harris, A. Torralbo, C. Langenberg, W. Wong, and A. Banerjee. 2020. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.researchgate.net/publication/340092652_Estimating_excess_1-_year_mortality_from_COVID-19_according_to_underlying_conditions_and_age_in_England_a_rapid_analysis_using_NHS_health_records_in_38_million_adults"", ""children"": [{""text"": ""Estimating excess 1- year mortality from COVID-19 according to underlying conditions and age in England: a rapid analysis using NHS health records in 3.8 million adults."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” 10.13140/RG.2.2.36151.27047."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Economist. 2020. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.economist.com/graphic-detail/2020/04/16/tracking-covid-19-excess-deaths-across-countries"", ""children"": [{""text"": ""Tracking Covid-19 excess deaths across countries"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "",” The Economist, 16 April, 2020,"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Edwards, M. and S. McDonald. 2020. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.theactuary.com/features/2020/05/07/co-morbidity-question"", ""children"": [{""text"": ""The co-morbidity question."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” The Actuary, Institute and Faculty of Actuaries, 7th May 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Farrington, C.P., N.J Andrews, A.D. 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Tallack. 2020. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.health.org.uk/news-and-comment/charts-and-infographics/understanding-excess-mortality-the-fairest-way-to-make-international-comparisons"", ""children"": [{""text"": ""Understanding excess mortality. What is the fairest way to compare COVID-19 deaths internationally?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” The Health Foundation, 6 May 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Loomes, G. and L. McKenzie. 1989. \""The use of QALYs in health care decision making.\"" Social Science and Medicine, Elsevier 28(4): 299-308, January."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Rubin, D., G. Tasian and J. Huang. 2020. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://policylab.chop.edu/blog/covid-19-outlook-ringing-alarm-bell-epicenters-waving-caution-flag-hotspots"", ""children"": [{""text"": ""COVID-19 Outlook: Ringing the Alarm Bell for Epicenters, Waving the Caution Flag for Hotspots."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” Article, Children’s Hospital Philadelphia Policy Lab, 24 June 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Santaeulalia, I, F. Peinado, E. Sevillano and J. Mateo. 2020. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://english.elpais.com/politics/2020-06-10/scandal-over-covid-19-deaths-at-madrid-nursing-homes-sparks-fierce-political-row.html"", ""children"": [{""text"": ""Scandal over Covid-19 deaths at Madrid nursing homes sparks fierce political row"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "".\"" El País, Madrid, 10 Jun 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Spiegelhalter, D. 2020b. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.theguardian.com/commentisfree/2020/apr/30/coronavirus-deaths-how-does-britain-compare-with-other-countries"", ""children"": [{""text"": ""Coronavirus deaths: how does Britain compare with other countries?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” The Guardian, 20 April 2020"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Spiegelhalter, D. 2020b. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://medium.com/wintoncentre/how-much-normal-risk-does-covid-represent-4539118e1196"", ""children"": [{""text"": ""How much ‘normal’ risk does Covid represent?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""”, Winton Centre for Risk and Evidence Communication, Cambridge, 21 March, 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Tallack, C.  D. Finch, N. Mihaylova, C. Barclay and T. Watt. 2020. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.health.org.uk/news-and-comment/charts-and-infographics/understanding-excess-deaths-countries-regions-localities"", ""children"": [{""text"": ""Understanding excess deaths: variation in the impact of COVID-19 between countries, regions and localities."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” The Health Foundation, 4 June 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Tozer, J. 2020. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://medium.economist.com/measuring-the-true-toll-of-the-pandemic-fa7e003b3ff4"", ""children"": [{""text"": ""Measuring the true toll of the pandemic"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""”, The Economist, 24 April, 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Wu, J., A. McCann, J. Katz and E. Peltier. 2020. “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.nytimes.com/interactive/2020/04/21/world/coronavirus-missing-deaths.html"", ""children"": [{""text"": ""46,000 Missing Deaths: Tracking the True Toll of the Coronavirus Outbreak"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""”, The New York Times, 30 April, 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""00baaa91bda0db56c8aaae0d7fcb94c490eb0c84"": {""id"": ""00baaa91bda0db56c8aaae0d7fcb94c490eb0c84"", ""index"": 24, ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Data source:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}, {""text"": "" Z-scores are extracted from the EuroMOMO website, 11-Jun-2020. The P-scores and variant P-scores are calculated by the authors using the Human Mortality Database, see meta file: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf"", ""children"": [{""text"": ""https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and the Office for National Statistics for the UK. Cumulative P-scores cover the weeks shown. "", ""spanType"": ""span-simple-text""}, {""children"": [{""children"": [{""text"": ""Notes:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}, {""text"": ""  (i) The peak weeks for different countries are in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""bold"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "". The peak weeks for the all age group category is the same for the UK countries. (ii) The country ordering is by cumulative P-scores. (iii) The ONS defines a week as ending on Friday; EuroMOMO define a week as ending on Sunday; for HMD definitions, it is also Sunday for the above countries, except for England and Wales, which is Friday, see the metafile. (iv) Deaths by week of registration versus deaths by week of occurrence:  EuroMOMO use deaths by occurrence and HMD use deaths by week of occurrence for the above countries, except in the case of the UK, where deaths by week of registration are used, see section 4.2.  (v) Revisions in the raw death count data: there have been recent large revisions in the Spanish death count, see section 4.2. (vi) Which weeks are chosen matter: for example, calculating Italy’s cumulative P-score for weeks 9 to 19, instead of weeks 10 to 20, reasonable to do since the pandemic struck first in Italy, gives Italy a P-score of 37 and a variant P-score of 35, putting Italy and Belgium neck and neck."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""0753643fece561043cd2a715ec704385c597030c"": {""id"": ""0753643fece561043cd2a715ec704385c597030c"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Data sources:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""The Economist (2020): “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.economist.com/graphic-detail/2020/04/16/tracking-covid-19-excess-deaths-across-countries"", ""children"": [{""text"": ""Tracking Covid-19 excess deaths across countries"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""”, 16 April, 2020, and Tozer (2020): “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://medium.economist.com/measuring-the-true-toll-of-the-pandemic-fa7e003b3ff4"", ""children"": [{""text"": ""Measuring the true toll of the pandemic"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""”, 24 April, 2020. For the Economist, Tozer measures excess deaths from the week the first 50 Covid deaths were reported, to around April 12. As of 15 May, The Economist’s J. Tozer and M. González publish the raw country data on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://github.com/TheEconomist/covid-19-excess-deaths-tracker/tree/master/output-data/excess-deaths"", ""children"": [{""text"": ""GitHub"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Also see “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ft.com/content/6bd88b7d-3386-4543-b2e9-0d5c6fac846c?emailId=5ea6e9bcd26cbd000484719d&segmentId=2785c52b-1c00-edaa-29be-7452cf90b5a2"", ""children"": [{""text"": ""Global coronavirus death toll could be 60% higher than reported"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""”, Financial Times, 26 April, 2020 and Wu et al. (2020): “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.nytimes.com/interactive/2020/04/21/world/coronavirus-missing-deaths.html"", ""children"": [{""text"": ""46,000 Missing Deaths: Tracking the True Toll of the Coronavirus Outbreak"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""”, The New York Times, 30 April, 2020. See EuroMOMO webpage: “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.euromomo.eu/how-it-works/methods/"", ""children"": [{""text"": ""Methods"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""”."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""0d1901c17966e203445adf61b292afccab99706e"": {""id"": ""0d1901c17966e203445adf61b292afccab99706e"", ""index"": 22, ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Data sources:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""The P-scores are calculated by the authors using the Human Mortality Database, see meta file: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf"", ""children"": [{""text"": ""https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf, "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""and the Office for National Statistics for the UK. 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"", ""spanType"": ""span-simple-text""}, {""children"": [{""children"": [{""text"": ""Notes:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(i) Deaths by week of registration versus deaths by week of occurrence: HMD use deaths by week of occurrence for all the above countries, except the UK, where deaths by week of registration are used, see section 4.2."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""1a68da5553487e2440336bdcc1cbeb1083a09c69"": {""id"": ""1a68da5553487e2440336bdcc1cbeb1083a09c69"", ""index"": 32, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""They analyse daily death registry data for over 1000 Italian municipalities, which suggest that deaths registered as Covid capture only about half of excess deaths. 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(ii) Deaths by week of registration versus deaths by week of occurrence: HMD use deaths by week of occurrence for all the above countries, except the UK, where deaths by week of registration are used, see section 4.2."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7f7ddc8552971a53a323cd8e4d5c1d8ec6eb7690"": {""id"": ""7f7ddc8552971a53a323cd8e4d5c1d8ec6eb7690"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See EuroMOMO webpage: “Methods”."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9b2a60430df607ed6faa6f0ab155685b708a9273"": {""id"": ""9b2a60430df607ed6faa6f0ab155685b708a9273"", ""index"": 35, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See EuroMOMO webpage: “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.euromomo.eu/how-it-works/methods/"", ""children"": [{""text"": ""Methods"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""”."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9fe0a44b77787f69a14af4834aa5ed14f3774dd3"": {""id"": ""9fe0a44b77787f69a14af4834aa5ed14f3774dd3"", ""index"": 14, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""UK Office for National Statistics (ONS)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ac067b5f7a26e06d49f60992721ae1e290c4eea9"": {""id"": ""ac067b5f7a26e06d49f60992721ae1e290c4eea9"", ""index"": 17, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/methodologies/userguidetomortalitystatisticsjuly2017#death-rates-ratios-and-standardisation"", ""children"": [{""text"": ""User guide to mortality statistics "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""of the ONS, UK, and its report on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/deathsinvolvingcovid19bylocalareasanddeprivation/deathsoccurringbetween1marchand31may2020"", ""children"": [{""text"": ""Deaths involving COVID-19 by localarea and socioeconomic deprivation."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b7bfb4a5a9db38a6df21eb88cc1b1e73fc6358d9"": {""id"": ""b7bfb4a5a9db38a6df21eb88cc1b1e73fc6358d9"", ""index"": 25, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/prison-population-rate"", ""children"": [{""text"": ""Prison Population Rate"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" on "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Our World in Data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""bb3bd810d3b71f1984444cb67202a972604eccd5"": {""id"": ""bb3bd810d3b71f1984444cb67202a972604eccd5"", ""index"": 9, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Transmission and hence rates of infection are also influenced by factors like the nature of social distancing, availability and use of face masks, and cultural differences in the exercise of self-discipline and following of advice."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""bdafa09a219d0297a85285572c78238930422557"": {""id"": ""bdafa09a219d0297a85285572c78238930422557"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See Letters, The Guardian: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.theguardian.com/world/2020/may/04/uk-behind-the-curve-in-curbing-covid-19-deaths"", ""children"": [{""text"": ""UK behind the curve in curbing Covid-19 deaths"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", 4 May 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""be9fe3237e7b12028be64bf0ac53a49aad0e9fb6"": {""id"": ""be9fe3237e7b12028be64bf0ac53a49aad0e9fb6"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""R is the virus reproduction rate, which needs to be kept below 1 to avoid exponential growth of infections."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c2fbfa0ed8a13414d3b8374be0576cf56ba0f45d"": {""id"": ""c2fbfa0ed8a13414d3b8374be0576cf56ba0f45d"", ""index"": 27, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Thus, most excess deaths in Wales in the pandemic period were due to Covid-19, with some mitigating factors from fewer other deaths, e.g. from traffic accidents. This may reflect more transparent recording of Covid-19 deaths. Wales also had a much higher percentage of excess deaths from Covid-19 occurring in the hospitals."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ca029484a7ee3df688f2188b58d92e0434d1c46b"": {""id"": ""ca029484a7ee3df688f2188b58d92e0434d1c46b"", ""index"": 15, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Spiegelhalter (2020) suggests that for the age group 20-59, the increase in mortality risk compared to normal mortality risk for Covid-19-infected individuals is lower than for those aged 60 and over."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d6fa1e1905da71dac60ac44064ac72866137892d"": {""id"": ""d6fa1e1905da71dac60ac44064ac72866137892d"", ""index"": 31, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""A study which forecasts the one-year ahead mortality is Denaxas et al. (2020)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d8c0312803b28876fd3a78e383930291a255944a"": {""id"": ""d8c0312803b28876fd3a78e383930291a255944a"", ""index"": 18, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""“I used to think this figure would be quite big but I’ve reduced my estimate now. I’m not going to put a precise figure on it, but I definitely think the proportion of those who would have died over the next year anyway would be well below a quarter, maybe 5 to 15 percent, rather than 'less than a quarter'.” Kelly (2020), Financial Times."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e54f71b598ac8a375f2ae7da445713442cbb7580"": {""id"": ""e54f71b598ac8a375f2ae7da445713442cbb7580"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The Poisson is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time if these events occur with a known constant mean rate and independently of the time since the last event. The calculation is described in Farrington et al. (1996)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ff77be0d7942e4854b81119ab239fe5ac37af8b8"": {""id"": ""ff77be0d7942e4854b81119ab239fe5ac37af8b8"", ""index"": 19, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Stuart McDonald is an actuary with the Institute and Faculty of Actuaries and a founding member of the group of industry professionals creating a Covid-19 actuaries’ response group, launched in March 2020. For more detailed information on comorbidity, see Edwards and McDonald (2020)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""A pandemic primer on excess mortality statistics and their comparability across countries"", ""authors"": [""Janine Aron"", ""John Muellbauer"", ""Charlie Giattino"", ""Hannah Ritchie""], ""excerpt"": ""Excess mortality has become a key metric to understand the true impact of the COVID-19 pandemic. How is excess mortality measured; and what can we learn from cross-country comparisons?"", ""dateline"": ""June 29, 2020"", ""subtitle"": ""Excess mortality has become a key metric to understand the true impact of the COVID-19 pandemic. How is excess mortality measured; and what can we learn from cross-country comparisons?"", ""sidebar-toc"": false, ""featured-image"": ""Figure-3.png""}",1,2024-02-16 09:18:51,2020-06-29 13:12:00,2024-02-16 20:02:10,listed,ALBJ4Lt7KHNLwAo8Vz51W3Bd4jtmnLp3V2Unh8y6eFb3NOL2JzjSCJIk-Q-EYZtiy2hPC1EMofrYDMVDC0NbJw,," ### Explore our continuously updated presentation of data on excess mortality https://ourworldindata.org/excess-mortality-covid --- New research publications on excess mortality by Janine Aron and John Muellbauer: ### The US excess mortality rate from COVID-19 is substantially worse than Europe’s In this follow-up article for VoxEU the two researchers find that Europe’s cumulative excess mortality rate from March to July is 28% lower than the US rate. https://voxeu.org/article/us-excess-mortality-rate-covid-19-substantially-worse-europe-s ### Transatlantic excess mortality comparisons in the pandemic In this new follow-up article the two researchers compare the excess mortality in Europe and the US. https://owid.cloud/app/uploads/2020/08/Aron-and-Muellbauer-Transatlantic-excess-mortality-comparison.pdf # 1. Why is it important to examine excess mortality data? Excess mortality is a count of deaths from all causes relative to what would normally have been expected. In a pandemic, deaths rise sharply, but causes are often inaccurately recorded, particularly when reliable tests are not widely available. The [death count](http://ourworldindata.org/covid-deaths) attributed to Covid-19 may thus be significantly undercounted. Excess mortality data overcome two problems in reporting Covid-19-related deaths. Miscounting from misdiagnosis or under-reporting of Covid-19-related deaths is avoided. Excess mortality data include ‘collateral damage’ from other health conditions, left untreated if the health system is overwhelmed by Covid-19 cases, or by deliberate actions that prioritise patients with Covid-19 over those with other symptoms. In a pandemic, measures taken by governments and by individuals also influence death rates. For example, deaths from traffic accidents may decline but suicide rates may rise. Excess mortality captures the net outcome of all these factors. Figure 1 illustrates how the degree of Covid-19 recording relative to excess deaths has varied across some European countries. In Belgium, with a broad definition of what constitutes a Covid-19 death, the excess over 100 percent might suggest that most excess deaths are due to Covid-19 and other deaths, such as those due to road accidents, may have declined. Excess mortality data can be used to draw lessons from cross- and within-country differences and help analyse the social and economic consequences of the pandemic and relaxing lockdown restrictions. For country comparisons (where under-recording may differ), policy-makers should examine _robust measures _expressed relative to the benchmarks of 'normal' deaths. ‘Normal’ death rates reflect persistent factors such as the age composition of the population, the incidence of smoking and air pollution, the prevalence of obesity, poverty and inequality, and the normal quality of health service delivery. Estimating the virus reproduction rate, _R_, is crucial for assessing the rate and nature of relaxation of lockdowns.1 Excess death figures could help to avoid the measurement biases inherent in other data typically used to estimate R in epidemiological models.2 # 2. How is excess mortality measured and who measures it? National statistical agencies publish actual weekly deaths and averages of past ‘normal’ deaths. For example, the Office for National Statistics (ONS) reports ‘normal’ deaths for England and for Wales as the average of the previous five years’ deaths. However, there are no published benchmarks for more granular or disaggregated data, such as sub-regions or cities. Using the weekly historical data, researchers could calculate such benchmarks with some effort. The ratio or percentage of excess deaths relative to ‘normal’ deaths, the P-score, is an easily understood measure of excess mortality, see Box 1. We argue that national statistical offices should publish P-scores for states and sub-regions. In the U.S., the National Centre for Health Statistics publishes data on excess deaths and a variant on P-scores (see Box 1), defining excess deaths as deviations from ‘normal’ deaths plus a margin adjusting for the uncertainty of the data.4 These data include counties and states, and are disaggregated by gender, age and ethnicity. The NCHS thus sets an international standard for statistical agencies. However, to obtain cross-European comparisons requires data collation from individual national agencies to construct P-scores or variant P-scores, which are largely comparable, see section 4.1. Another alternative are the Z-scores compiled by EuroMOMO5 for 24 states, see Box 1. EuroMOMO’s measures of weekly excess mortality in Europe show the mortality patterns between different time-periods, across countries, and by age-groups. The Z-scores standardise data on excess deaths by scaling by the standard deviation of deaths. EuroMOMO are currently not permitted to publish actual excess death figures by country and do not publish the standard deviations used in their calculations. However, they graph the Z-scores and the estimated confidence intervals back to 2015 providing a visual guide to their variability. In contrast to the P-scores, the Z-scores are a measure that is less easily interpretable. Moreover, if the natural variability of the weekly data is lower in one country compared to another, then the Z-scores could lead to exaggeration of excess mortality compared to the P-scores. Strictly, the Z-scores are not comparable across countries, though see the caveats in section 4.1. At least five separate journalistic endeavours have recently engaged in the time-consuming effort of collating and presenting more transparent excess mortality data, see Table 1. The Financial Times plots numbers of excess deaths, and the P-score or percentage of deaths that are above normal deaths. The Economist shows figures and graphics for excess deaths but not P-scores. However, the published estimates of P-scores in newspapers give only a recent snapshot, missing the context of historical variability provided by EuroMOMO. And we only have P-scores for some countries, regions and cities. A third measure of excess mortality is per capita excess mortality, where excess deaths (actual deaths minus ‘normal’ deaths) are divided by population, see Box 1, is used by the BBC (Table 1). ## Table 1: Sources of comparative excess mortality data for Europe, the UK and the US, and other countries **Sources**6 |**_Source and metafiles_**|**_Measure reported_**|**_Period and type of data_**|**_Benchmark_**|**_Disaggregation?_**|**_Locations compared_**|**_First publication date_**| |[The Human Mortality Database](https://www.mortality.org/) (HMD) _Comprehensive, transparent metafile for data sources and coverage._|Death counts and death rates by country. _[The raw data allow P-scores to be calculated]._|Weekly, 2000-2020 for many. At least from 2015 for all, except Germany (2016). Occurrence data for the death count, except the UK, which is registration data.|The average benchmarks for earlier years can be calculated from the earlier data e.g. 2015-2019.|By age groups: 0-14, 15-64, 65-74, 75-84, 85+. By gender (F, M, total).|22 countries: Austria, Belgium, Bulgaria, Czechia, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Italy, Luxembourg, Netherlands, Norway, Portugal, Scotland, Slovakia, Spain, Sweden, UK: England and Wales, UK: Scotland, and the USA.|_Regularly updated._ Open access on website.| |[Eurostat](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Weekly_death_statistics) _Transparent metafile for data sources and coverage._|Number of weekly deaths. _[The raw data allow P-scores to be calculated.]_|Weekly, 2000-2020 Eurostat recommends date of occurrence data for death counts, but accepts date of registration. May vary by country.|Historical average of deaths for that week over 2016-2019.|Three levels of regional breakdowns (NUTS levels): major socio-economic regions (e.g. countries); major sub-national regions; and small subnational regions (e.g. cities). By age group: 5-year groups, 20 in all. By gender (F, M, total).|22 countries: Austria, Belgium, Bulgaria, Czechia, Denmark, England and Wales, Estonia, Finland, France, Germany, Hungary, Iceland, Italy, Luxembourg, Netherlands, Norway, Portugal, Scotland, Slovakia, Spain, Sweden, and the USA. Sub-national regional data available at both NUTS Level 2 (major regions) and NUTS Level 3 (smaller, higher-resolution regions) for most countries.|_Regularly updated. _Downloadable on website.| |[European Mortality Monitoring Project](https://www.euromomo.eu/graphs-and-maps/#excess-mortality) (EuroMOMO) _There is no metafile for data sources and coverage. The underlying data are not fully transparent._|Z-scores by country for 2015-2020; total (_summing_ _all countries_) weekly and cumulated excess deaths and pooled number of deaths for 2016-2020. Excess deaths are not _reported_ for individual countries. Expected levels of deaths are not published.|Weekly data. Week ends on Sunday. Occurrence data for the death count, including the UK.|Deviation in mortality from an _expected _level. See Box 1 for a description of the method and how the expected level is modelled.|All ages and by age groups, recently expanded: 0-14, 15-44, 45-64, 65-74, 75-84, 65+, and 85+|UK and its constituent nations and regions, 24 European countries: Austria, Belgium, Denmark, Estonia, Finland, France, Germany (Berlin), Germany (Hesse), Greece, Hungary, Ireland, Italy, Luxembourg, Malta, Netherlands, Norway, Portugal, Spain, Sweden and Switzerland. _[Note: the fraction of the population covered by the country level data is not given, e.g. “Italy” in fact only covers 14% of the population, see text.]_|Began in 2008. Since 2016 supported by European Centre for Disease Prevention and Control (ECDC) and the World Health Organization (WHO) Regional Office for Europe. _Regularly updated._ Data are not downloadable except from charts.| |[The Health Foundation](https://www.health.org.uk/), UK _Clear description on graphs of data sources and coverage._|Weekly and/or cumulative P-scores; cumulative excess deaths by designated time period for a subset of the RHS locations.|Weekly, 28-Feb-20 to end-May-20. Occurrence data for the death count except for the UK, which uses registration data.|Baseline differs by country, see their interactive graphs. For the UK, it is the historical average of deaths for that week over 2015-2019. But for Madrid, for example, the average is over 2018-19.|Regional disaggregation in the UK to local authority level, presented graphically.|UK and its constituent nations and regions and local authorities. _European countries:_ France Italy, Spain and their constituent regions. Sweden, Germany. _Cities:_ London, Madrid, NY City, Paris.|4 June 2020 _In two articles._ _Not updated._ _Data are not downloadable except from charts._| |[The Economist](https://github.com/TheEconomist/covid-19-excess-deaths-tracker/tree/master/output-data/excess-deaths) _Clear description of data sources and coverage and method on GitHub._|Numbers of deaths, Covid-19-deaths and of excess deaths (actual deaths minus the expected deaths). _[The raw data allow P-scores to be calculated.]_|Weekly; approximately monthly in one table. Occurrence data for most countries. UK based on registration data.|“Expected deaths”, averages ranging from 2 to 5 years, see GitHub.|Some regional disaggregation, see next column.|United Kingdom and its constituent nations and regions and London. _Other countries:_ Austria, Belgium, Brazil (5 cities: São Paulo, Rio de Janeiro, Fortaleza, Manaus and Recife), Chile (and regions), Denmark, Ecuador, France (and departments), Germany, Indonesia (burials in Jakarta), Italy (and regions), Mexico (Mexico City), Netherlands, Norway, Peru, Portugal, Russia (Moscow), South Africa, Spain (and regions), Sweden, Switzerland, Turkey (burials in Istanbul), United States (and regions).|Started 16 April 2020. _Regularly updated._ _Open access __[on GitHub](https://github.com/TheEconomist/covid-19-excess-deaths-tracker)__._| |[The Financial Times](https://github.com/Financial-Times/coronavirus-excess-mortality-data) _Clear description of data sources and coverage and method on GitHub._|Number of deaths and of excess deaths (actual deaths minus the expected deaths). _[The raw data allow P-scores to be calculated.]_|Weekly and cumulative, from beginning of outbreak. Occurrence data for most countries. UK based on registration data.|Historical average of deaths for that week over 2015-2019.|Regional disaggregation in the UK to its constituent nations and sub-regions in England. Local-level data available for some other countries.|UK and its constituent nations and regions. _European countries:_ Italy (and regions); Austria; Belgium; Denmark; France (and regions); Germany; Iceland; Netherlands; Norway; Portugal; Russia (cities only); Spain (and regions); Sweden (and Stockholm); Switzerland; Turkey (Istanbul only). _Other countries:_ Brazil (and regions); Chile (and regions); Ecuador (and Guayas); Indonesia (Jakarta only); Israel; Peru (and regions); South Africa; USA (and states).|26 April 2020. _Regularly updated._ _Open access __[on GitHub](https://github.com/Financial-Times/coronavirus-excess-mortality-data)__._| |[The New York Times](https://github.com/nytimes/covid-19-data/tree/master/excess-deaths) _Clear description of data sources and coverage and method on GitHub._|Number of deaths and of excess deaths (actual deaths minus the expected deaths). _[The raw data allow P-scores to be calculated.]_|Weekly or monthly, differs per country. Occurrence data for most countries. UK based on registration data.|“Expected deaths”, averages ranging from 2 to 5 years, data-dependent, and differing by country, and adjusting reported deaths for trends and seasonal components using a linear model, see GitHub (e.g. 5-years for the U.S. over 2015-2019).|No regional disaggregation. Some cities, see next column.|Austria, Belgium, Brazil (only 6 cities: São Paulo, Rio de Janeiro, Fortaleza, Manaus, Recife and Belem), Denmark, Ecuador (and Guayas), Finland, France (and Paris), Germany, Indonesia (only Jakarta), Israel, Italy (and Bergamo and Milan), Japan (only Tokyo), Netherlands, Norway (and Oslo), Mexico (only Mexico City), Peru (and Lima), Portugal, Russia (only Moscow, St. Petersburg), Spain (and Madrid and Catalonia), South Korea, Sweden (and Stockholm), Switzerland, Thailand, United Kingdom (and London), United States (and 6 cities: Boston, Chicago, Denver, Detroit, Miami, NYC).|30 April 2020. _Regularly updated._ _Open access __[on GitHub](https://github.com/nytimes/covid-19-data/tree/master/excess-deaths)__._| |[BBC](https://www.bbc.co.uk/news/world-53073046) _Citation of data sources. No metafile with links to data or sources._|Official COVID-19 deaths. Number of excess deaths (actual deaths minus the expected deaths).|Cumulative over the pandemic; different periods for different countries. Occurrence data for most countries. UK based on registration data.|For most countries, taken as the historical average of deaths for that week over 2015-2019.|Regional disaggregation in the UK to its constituent nations and sub-regions in England. Some cities, see next column.|UK and its constituent nations and regions. _European countries:_ Austria, Belgium, Denmark; France; Germany; Italy; Netherlands; Norway; Portugal; Russia (cities only); Serbia; Spain; Sweden; Switzerland; Turkey (Istanbul only) _Other countries:_ Brazil (six cities only); Chile; Ecuador; Indonesia (Jakarta only); Iran; Japan; Peru; South Africa; South Korea; Thailand; USA.|18 June 2020. _Data not downloadable._| |[The Guardian](https://www.theguardian.com/world/ng-interactive/2020/may/29/excess-deaths-uk-has-one-highest-levels-europe) _Citation of data sources. No metafile with links to data or sources. Data not downloadable._|Number of deaths, official COVID-19 deaths and of excess deaths (actual deaths minus the expected deaths).|Weekly; covering weeks 1-20 of the first wave of the pandemic. Occurrence data for most countries. UK based on registration data.|Historical average of deaths for that week over 2015-2019.|None. No regional data. No age or gender breakdowns.|UK (no breakdown by constituent regions). Spain, Denmark, Sweden, Netherlands, _European countries:_ Austria, Germany, Belgium, Italy, France, USA.|29 May 2020. _Data not downloadable._| ## Box 1: Measures of excess mortality: P-scores, per capita excess mortality and Z-scores Denote the number of weekly deaths by x. **The P-score **is defined as follows: (x minus the expected ('normal') value of x for the population), divided by the expected value of x for the population. **A variant P-score **(U.S. National Center of Health Statistics) is defined as follows: (x minus the upper threshold for the expected value of x for the population), divided by the upper threshold for the expected value of x for the population. * _The upper threshold _is defined as the expected value plus the 2.5% confidence interval for this expected value. This takes into account uncertainty created by the natural variability of x. **The per capita excess mortality **is defined as follows: (x minus the expected value of x for the population), divided by the population. **The Z-score **is defined as follows: (x minus the expected value of x for the population), divided by the standard deviation for the population of x around its expected value. * EuroMOMO _estimate _the expected value of each country’s weekly deaths using data for the previous five years, taking seasonal factors and trends into account, and adjust for delays in registration. * EuroMOMO assume that a Poisson distribution, adjusted for excess dispersion is a good approximation to the underlying probability distribution of weekly deaths.7 * Graphs published for each country show the weekly Z-scores since 2015 compared to their usual range of -2 to +2, the approximate 95% confidence interval. Around 2.5% of observations would thus usually have a Z-value over 2. The Z-score equals 4 line is also shown, corresponding to a ‘substantial increase’: under usual conditions, the Z-value would exceed 4 only around 0.003% of the time. * The graphs show more deviations of Z-scores ’exceeding 2’ and ‘exceeding 4’, than one would expect. The main reason is that to fit the baseline, EuroMOMO chose only the period of the year when additional processes (e.g. Winter influenza and Summer heat waves) leading to excess deaths are not likely to happen. Normal variability is thus measured after excluding these seasons.8 # 3. Key issues for comparing rates of excess mortality across and within nations There are several reasons for wanting to compare excess mortality between regions or countries. The first is simply to compare the death toll of the first wave of the pandemic. Useful aggregate measures include the count of excess deaths relative to normal deaths, for example, the P-score, and excess deaths relative to population size, see Box 1. The second of these measures has the problem that older populations tend to have higher normal death counts. This measure of excess deaths will overstate the incidence of the pandemic in older compared to younger populations. For the second reason, that of evaluating the effectiveness of policy responses, one needs to dig deeper, and the simple measures above require further interpretation. Countries may differ in the size of the initial source of infection, in their age structure, in the distribution of co-morbidities in the population and the prevalence of dense urban centres, making some countries more vulnerable. Comparing age-standardised mortality can be helpful in controlling for differences in age structures. Finally, the third motivation for comparisons is a purely objective one of improving the scientific understanding of the dynamics of the spread of infections, their incidence and the death rates of those infected. Key to this last endeavour is the production of granular data, i.e. disaggregation of excess deaths data by age, gender, region, and, where possible, socio-economic categories. A recent controversy in the UK amongst statisticians has served to reinforce the point of our paper, which is that there can be international comparability _now_ of excess mortality with aggregate and more granular P-score data. There are already widely available granular data sets on related aspects such as inequality and urban density, which could be combined with such data for illuminating the comparisons across countries and revealing the effectiveness of different types of policy. Ideally there should be transparent definitions of data and comparability of definitions across nations which may involve coordination by existing international bodies for standards of data dissemination. This will evolve over time but does not preclude analysis _now_. Important is the accessibility of data to all, especially modellers in the fields of epidemiology, economics and sociology. Scientific analysis with appropriate data is needed to inform policy _now_ because not only may there may be successive waves of the pandemic in each country, but many countries experiencing later pandemic crises have the potential to reignite infections in earlier countries when borders are open, and there may be pandemics in future years. Turning to the controversy, Spiegelhalter (2020a) in a Guardian article on 30th April 2020 made valid points about data definitional differences and poor collection of data across some countries of Covid-19 infection and mortality rates. We are in agreement on this, but although he discusses the more reliable data on excess mortality, he argues that we will have to wait for months if not years before we can begin making useful comparisons across countries. However, given that the first wave of the pandemic in Europe has neared its end in most countries, _now_ is a good time to make international comparisons at least within Europe.  Indeed, on 4 May 2020, a letter9 from three statistics professors, Philip Brown, James Smith and Henry Wynn disputed Spiegelhalter’s claims saying: “Yes, there are inconsistencies, underreporting and heterogeneity within countries, but the policies adopted by different countries show very large differences in effects that would seem to dwarf such worries.” Their concern was that the article would deflect criticism of the political handling of the crisis (and indeed it had already in their view). They argue that comparisons combined with careful modelling are needed _now_ to explain variations in mortality rates and infection rates across locations toward improved policy. They cite for instance a U.S. modelling endeavour, Rubin et al. (2020), the latest version of which analyses and forecasts US county level data on death rates, taking into account local factors across US counties including population density, incidence of smoking and social distancing as measured by cell phone movement data. The statisticians suggest that such modelling tools are appropriate to apply to country comparisons, and critical for modelling testing and tracing to the community level. We emphasise this modelling point more broadly in section 8. To interpret large differences in excess mortality between nations requires consideration of several factors, and the within-nation deviations in these factors: the average infection rates in preceding weeks, average mortality risk from Covid-19 for those infected (the case fatality rate) and constraints on Covid-19-specific health capacity. Turning to the first of the factors, consider differences in infection rates. Compare two countries or regions with the same average Covid-19 mortality risk where 1 percent of all adults are infected in A, while 5 percent are infected in B. Then the rate of excess deaths for adults measured by the P-score will be about 5 times as large in B in the weeks following the incidence of the infection. Countries that locked down early and had effective test, trace and isolate procedures kept down the average infection rate and hence the excess death rate.10 Within countries, infection rates can differ. London’s higher excess mortality was influenced by higher initial imports of infections and a higher virus reproduction number given its high density and hard-to-avoid close physical contact on public transport and at work. Thus, countries that have a higher fraction of adults in locations or occupations where the virus can more easily spread will tend to have higher excess death rates. Mortality risks for infected adults, the second of the factors mentioned above, can differ between and within countries. For example, the percentage increase in mortality risk may be greater for some ethnic groups, or for some co-morbidities such as diabetes or pre-existing lung conditions. Then country differences in the prevalence of obesity and smoking will influence comparative excess mortality. Lastly, a country’s excess mortality is further driven up, and potentially _much _further, by limited Covid-19-specific health capacity. The death rate among infected adults depends on capacity constraints on numbers of hospital beds and staff, numbers of ventilators, PPE, testing and logistical failures in delivery, e.g. to care homes. Given similar initial capacities, a country with a higher average infection rate will be more likely to run into these constraints. By the same logic, given the same high infection rate, a country with lower health capacity would have a higher rate of excess mortality. This is why there is such a focus on ‘flattening the pandemic curve’. Different capacity constraints can have different implications for different groups. For example, lack of PPE and testing facilities in care homes will have disproportionately larger effects on mortality for the oldest individuals and this could affect country comparisons. Covid-19, therefore, interacts with the age distribution, the nature of health service delivery, poverty and inequality, ethnic and occupational structures, air pollution, the relative size of major conurbations and so on. Comparing rates of excess mortality statistics within countries by age groups, by city size and by occupational, social and ethnic groups should generate important insights for future pandemic policy. Finally, it should be considered whether excess mortality statistics alone are sufficient to measure the impact of a pandemic. The health economics literature has given attention to Quality Adjusted Life Expectancy (QALY) as a criterion for expenditure on health-improving policies. QALYs measure the number of reasonably healthy years a person might expect to live. The number of QALYs lost could supplement the increased death count resulting from the pandemic as a measure of its impact. However, detailed actuarial and medical information is entailed in the complex estimation of the number of QALYs lost. QALYs and the attachment of monetary values to QALYs have long been controversial, see Loomes and Mackenzie (1989), but the concept of a QALY does focus attention on the relative value (by age group) of expected years lost in a pandemic. The excess mortality of working age adults with a normal life expectancy of 30 years might be weighed against the excess mortality of 85-year olds with a life expectancy of 5 years. If the choice is to attach more weight to excess mortality for working age adults this will affect comparisons of countries with different age-specific mortality rates, see section 7. # 4. Comparability of statistical measures of excess mortality and other data issues to consider ## 4.1 Can we compare the different statistical measures for excess mortality (from all causes) across countries? Comparisons between relatively homogeneous countries with moderate population sizes (such as European countries, Japan and Korea) and large countries such as China and the U.S., which span very diverse regions with potentially very different timings and incidence of the pandemic, are necessarily difficult. For the latter, it makes far more sense to compare populous regions or states with nation states of comparable scale. P-scores, per capita measures of excess deaths and Z-scores use the concept of ‘normal deaths’ in their numerator by comparing raw death figures with what would normally have been expected. Assuming that the data definitions for the death counts, such as the definition of the week, type of death count data collected (registration versus occurrence data, see below) and timeliness of the collection, are identical across countries (which they are not, see the next sub-section), we consider the relative comparability of the statistical measures described in section 2. For any measure, it is clear that cumulating actual deaths and normal deaths over the period of the first wave of a pandemic gives a more robust summary of its impact, as compared to examining only the peak week. **_Comparability of P-scores and variant P-scores_** The _P-scores _are robustly comparable across countries, with the caveat that the measure of ‘normal deaths’ is likely to be only approximate (see below). However, the underlying death count data do need to be transparent and fully comparable to make the comparisons valid, see section 4.2. Normal death rates _already _reflect persistent factors such as the age composition of the population, the incidence of smoking and air pollution, the prevalence of obesity, poverty and inequality, and the normal quality of health service delivery. This makes P-scores particularly attractive even if age compositions and other persistent factors differ. Since they measure the _percentage deviation _compared to what is normal, these persistent differences will already be incorporated in the definition of the ‘normal’ death rate. Variant P-scores add an allowance for historic data variability to the normal number of deaths to define an upper threshold (supposedly based on the 95 percent confidence interval around normal deaths). They define excess deaths relative to that threshold and scale by the same threshold to compute a percentage. The variant P-score is therefore always a bit below the simple P-score but tracks it closely. Because the variant is more complex, the simple P-score is preferable. It can always be accompanied by an indication of the margin of uncertainty around estimated normal deaths. When cumulated over a number of weeks, that margin of uncertainty falls so that there is then even less difference between the simple and variant measures (see Figure 3). **_Comparability of the per capita excess mortality measure_** Scaling excess deaths by population is obviously better than attempting to compare crude excess death counts for countries with vastly different populations. However, countries with older populations will tend to have higher normal death rates. This automatically means that countries like Italy with an older population will have higher measures of per capita excess mortality than countries with younger populations, such as England. Therefore, comparisons of per capita excess mortality need to be made with caution. A possible argument in favour of per capita excess mortality is that total population could be regarded as a rough proxy for the ability of the society to absorb excess deaths. However, on that logic, dividing excess deaths by the working age population would make more sense. **_Comparability of Z-Scores_** As explained in Box 1, Z-scores deflate excess deaths by the standard deviation of normal deaths. In principle, given the assumption of the Poisson distribution, see Box 1, _Z-scores _should not be compared across countries of very different sizes, though they are useful for comparing the profile of weekly excess deaths for an individual country. The reason is, that countries with small populations and therefore more noisy weekly counts of mortality, have higher standard deviations relative to normal deaths than the more populous countries. In practice, due to the inappropriate assumption of the Poisson distribution (see Appendix 1), the excess mortality rankings between countries are more similar to the P-scores than expected. The Poisson is likely to be poor approximation to the stochastic process for number of deaths, even in what EuroMOMO call _normal _seasons. EuroMOMO exclude Winter and Summer because of systematic shifts in mean deaths due to ‘flu, bad weather or heat waves. But it seems extreme to assume there are no systematic shifts in mean deaths throughout Spring and Autumn. If there are excess deaths due to a bad ‘flu in Winter, then in Spring below-average excess deaths should result. There are other examples, such as a measles outbreak, or changes in support for the homeless or for care homes (e.g. from fiscal austerity measures), that may affect mortality rates. There could also be time-varying clusters of different influences - such as a varying previous exposure to risks such as smoking - among the most vulnerable age groups. Thus, the constant _mean_ assumption is almost certainly wrong. Turning to the weekly _standard deviation_ for ‘normal’ seasons used by EuroMOMO to deflate the Z-score (see Box 1), variations in _systematic factors_ such as these which shift the mean will be included in the measure, as well as random noise (see Box 2). Hence, Z-scores include these systematic features in the denominator and numerator. The paradox is that this makes the Z-scores somewhat more comparable for countries of different sizes (see Appendix 1). The Z-scores indicate approximately (given the Poisson assumption) in which weeks excess deaths were statistically significant;  hence they can in principle distinguish those countries with few, if any, weeks of excess deaths (e.g. Germany), from countries with many weeks of excess deaths (e.g. Belgium), irrespective of their large population size differences. Another major defect of Z-scores, compared to P-scores and per capita excess death measures, is that their cumulation over multiple pandemic weeks is problematic. While excess deaths can be cumulated, the standard deviation of normal deaths cannot, and, in any case, EuroMOMO do not report either excess deaths or these standard deviations. This makes it hard to obtain a comprehensive summary of the pandemic’s impact from the Z-scores. ## Box 2: Two pieces of evidence against the Poisson assumption used in EuroMOMO Z-scores We consider two pieces of evidence against the assumption of a Poisson distribution by EuroMOMO. Both show there are common systematic factors driving mortality data. 0. _We examine the correlations of Z-scores within the UK_. If there are systematic sources of variation of death rates, as well as pure noise, these systematic factors for the UK regions are very likely to be _correlated_. On 100 observations, 2015-2019, excluding winter and summer weeks as for EuroMOMO, the correlation matrix is: ||_England_|_Wales_|_Scotland_|_N. Ireland_| |England|1|||| |Wales|0.345482|1||| |Scotland|0.326606|0.205122|1|| |N. Ireland|0.298233|0.106424|0.17243138|1| These quite high correlations imply systematic factors common to all regions. Thus, the Poisson distribution cannot be correct as it assumes independence between regions and over time. Moreover, simple regressions between the Z-scores for Wales, Scotland and N. Ireland and that for England, give coefficients, respectively, of 0.32 (0.089), 0.30 (0.088) and 0.29 (0.092), with standard errors in parentheses. In reverse, a multiple regression of the Z-score for England on all the others gives: Wales 0.29 (0.097); Scotland 0.25 (0.099); N. Ireland 0.24 (0.094) 2. _We examine the ratios of Z-scores to P-scores_.  If they shared the same concept of normal or expected deaths, the Z/P ratio would equal the ratio of ‘normal’ deaths to their standard deviation. Under the constant mean Poisson assumption, this ratio would be proportional to the square root of the number of normal deaths. We lack access to EuroMOMO’s estimates of the normal number of deaths, but these should be close to the previous 5 years’ average. The ranking (high to low) of the estimated Z/P ratios in the peak week of the pandemic for the different countries, should be the same as their ranking by the normal number of deaths. EuroMOMO adjusts the Poisson assumption with a small allowance for extra dispersion but this should not affect the ranking. The table shows that the expected ranking if the adjusted Poisson assumption were true is far from being confirmed by the evidence. One should expect Belgium to have the lowest Z/P and France the highest, with Italy the second highest, within Europe. Instead, Italy has the lowest, despite its relatively large number of normal deaths. Within the UK, with the exception of Wales, the rankings of ratios of Z/P do follow the rankings by population size and normal death counts. Regions with small populations - hence small numbers of normal deaths - should have _somewhat_ noisier death rates since the purely random component of deaths would be larger compared to the systematic component. But only if the systematic component were zero would the ratio of the standard deviation to normal deaths be entirely determined by the normal number of deaths. Appendix 1 spells out the same argument somewhat more formally. **Peak weeks of excess mortality: country P-scores and Z-scores compared** |**_Peak weeks_**|**_Excess mortality scores_**|**_Ratio_**|**_‘Normal’ deaths_**|**_Population_**|| |**_All age groups, standard P-scores_**|P-score|Z-score|Z/P|number|millions| |England (week 16) (Z: week 15)|116|41.24|0.36|9,787|56.0| |Spain (week 14)|154|43.53|0.28|8,118|46.8| |Belgium (week 15)|104|30.39|0.29|2,095|11.6| |Italy P: (week 13) (Z: week 14)|85|16.94|0.20|11,818|60.5| |Netherlands (week 14)|74|23.44|0.32|2,916|17.1| |France (week 14)|67|21.72|0.32|11,380|65.3| |**Rest of UK**|||||| |Scotland (week 15)|80|15.8|0.20|1,100|5.4| |Wales (P: week 16) (Z: week 15)|77|19.5|0.25|661|3.1| |N. Ireland (P: week 17) (Z: week 15)|56|9.38|0.17|301|1.9| ## 4.2  Data issues underlying the statistics that influence their comparability Even if we deem the P-scores and the population-deflated statistics to be comparable across countries, underlying measurement issues of the death count, strongly affect the comparability across countries. These definitional differences need to be highlighted and made transparent across country data providers and international organisations reporting excess mortality statistics. The transparent reportage of the Human Mortality Database is exemplary in this regard. **_The accuracy of the basic data collected_** Perhaps the biggest single pitfall for comparability may arise from the accuracy of the raw mortality data. In our VoxEU article (Aron and Muellbauer, 2020a) we highlighted the advantages of excess mortality data over recorded Covid-deaths, see also section 1, assuming that the collection of data on deaths from all causes would be relatively up-to-date and complete. Yet countries differ in the efficiency of their death registration systems, particularly where those systems are devolved to regional or local administrations. Then, problems in one location can affect or delay the nationaI data, and sometimes the national recording system can be slow to absorb regional information. In a pandemic, it can happen that the capacity of systems is temporarily overwhelmed, most of all in hotspots, often in urban areas. Occasionally the recording methods may be so weak overall, that the observers resort to data on burials.11 The most striking recent example of revisions in the raw mortality figures is that for Spain announced on May 27th. Raw deaths were suddenly revised up by around 12,000, back to early March. Catalonia, whose capital is Barcelona, accounted for well over half of these increases, followed by the regions of Madrid and Castilla La Mancha. A closer look at the data revisions by age shows that the bulk of the revisions were for those aged 75 or more. This is consistent with news reports of the many deaths in care homes.12 As we shall see, the upward revision in the Spanish data currently places Spain neck and neck with England as the European country with the highest cumulative P-score for the ‘all ages’ group (Table 2), whereas previous data put England’s all-age P-score well ahead. **_Lag between occurrences versus registration data on death counts_** Another difference is between the death counts by week of registration of the death and week of actual occurrence of the death. The registration data occur later than the occurrence data. EuroMOMO Z-scores apparently use data by occurrence for all reporting countries, see Table 1.13 HMD use occurrence data for most countries, with the exception of England and Wales.14 The occurrence-data are particularly prone to revision, and with the lags of registration data behind occurrence data often increasing during the height of a pandemic. Comparability in dating the peak week of mortality is sensitive to how the data are recorded. For example, in the UK, the peak week for all underlying regions is week 15 using occurrence data, as for the EuroMOMO Z-scores in Table 2. By contrast, death counts based on registration data for the UK show peak weeks of week 17 for N. Ireland, week 16 for England and Wales and week 15 for Scotland, see Table 2. Figure 2 compares for England the occurrence and registration data in calculated P-scores. It is also important to be cautious when comparing cumulative P-scores across countries if the pandemic has not yet run its full course in some countries. **_Measurement of ‘normal deaths’_** The 5-year average could be a crude estimate of normal deaths, e.g. if there are time trends in mortality. If mortality is on an improving trend, normal deaths would be over-estimated by the 5-year average. On the other hand, where populations are increasing or are ageing, the count of normal deaths could also be rising. EuroMOMO use statistical models to adjust for such trends but do not provide their estimates of ‘normal’/expected deaths. If spring is especially warm as has been the case in Europe in 2020, it is possible that the 5-year average overestimates expected deaths, taking the weather into account. In the latter case, the simple P-score would then underestimate the impact of the pandemic. Also note that not just the effects of the pandemic but of societal reactions, whether driven by government regulation or private behaviour, will be reflected in the death count. Greater social distancing, lower rates of traffic accidents and of deaths due to alcohol abuse as well as ‘collateral damage’ will all affect the death count. **_Definition of the week_** Countries differ in how they define the week. The mostly widely accepted international definition starts the week on Monday and ends on Sunday. However, of the countries we compare, England, Wales and Northern Ireland start the week on Saturday and ends it on Friday, while all the others, including Scotland follow international practice. This is a relatively minor issue and largely washes out when cumulating excess deaths over multiple weeks, e.g. eleven weeks. # 5.  Why the age distribution matters Differences in the age distribution between countries would be irrelevant if mortality risk increased in the same proportion for all. This can never be the case because children have a far lower mortality risk. In countries where children make up a high proportion of the population, the P-scores and excess mortality relative to the total population for the all ages group will be lower. Looking only at the adult part of the population in a pandemic, there is strong empirical evidence _against _the hypothesis of a proportionate increase in mortality risk at all adult ages. We cannot be sure to what extent this is due to differences in rates of infection or differences in mortality risk once infected.16 The evidence in section 7 for six countries is for a more than proportionate increase for older adults, i.e. the group of older adults (85+) has a higher P-score than the group of younger adults (15-64). Comparing two countries with the same age-specific P-scores, the country with the higher proportion of older adults would then have a higher all-age adult P-score. Countries also differ in the age-profile of P-scores. One can see this when comparing the ratio of the P-score for the group of working age adults to that of the group of older adults, e.g. those over 65 or over 85. This ratio is less than 1 everywhere, but some countries have far lower P-scores for working-age adults relative to older adults. To see the implications, take a simple example of two countries with the same age-structure of young and old adults. Suppose the P-score is 1 for the old in both countries, but that country A has a P-score of 0.1 for young adults while that for country B is 0.3. The overall P-score for country B will clearly be higher than for country A. However, if country B also has a higher fraction of young adults, that will attenuate the difference in the overall P-scores between the two countries. Thus, differences in age distributions between countries will affect the measured all-age P-scores and this should be recognised when comparing P-scores. One could envisage an ‘age-standardised P-score’, adapting the ‘age-standardized mortality rate’, sometimes used to examine the impact of a pandemic. The latter is a weighted average of the age-specific mortality rates per 100 000 persons, where the weights are the proportions of persons in the corresponding age groups of a standard population. The WHO explains the rationale: “Two populations with the same age-specific mortality rates for a particular cause of death will have different overall death rates if the age distributions of their populations are different. Age-standardized mortality rates adjust for differences in the age distribution of the population by applying the observed age-specific mortality rates for each population to a standard population.”17 A theoretical population, the European Standard Population (ESP), is widely used in Europe to compute age-standardised death rates. This has a particular distribution by age, averaging data from across Europe. The current version from Eurostat was introduced in 2013. The ONS in the UK has also used age-standardised death rates to compare mortality risk from Covid-19 between the UK regions or between locations with different levels of economic and social deprivation.18 However, the ‘age-standardized mortality rate’ unfortunately conflates variations in normal mortality risk with variations in risk of death during a pandemic. Thus, if the age-standardised mortality rate in 2020 is higher in region A than in region B, this does not necessarily indicate that the Covid-19 mortality risk is higher in A. It may be that normal mortality risk, e.g. based on the average of the previous 5 years, is higher in region A than in B. Age-standardisation removes that part of the difference due to differing _age structures _of the two populations; but it does not remove from normal mortality risk the socio-economic differences, and differences in the incidence of obesity or smoking and in health provision. An ‘age-standardised P-score’ would give a better grasp of the increased mortality risk due to Covid-19 than the ‘age-standardized mortality rate’. The P-scores for each age group could be computed and the weighted average taken using the age structure of the reference population, rather than of the region or country being considered. It is a better concept because it compares the age-standardised mortality rates during the pandemic period with those normally expected. This type of P-score would provide a provisional answer to the question: ‘how different would the overall mortality rate have been with a different age structure of the population?’ There are also potentially _other _ways of standardising aggregate P-scores (or mortality per 100,000 of population) to remove part of the source of between-region or between-country variation. For example, one could standardise by proportions of the population resident in towns and cities classified by common size categories. The simple aggregate P-score (which weights the age-specific P-scores by the fraction of the population in each age group) and these various standardised aggregate P-scores (which weight the age-specific P-scores by the fraction of the population in each age group in a hypothetical population) have intuitive appeal and can be informatively compared across countries. However, one has to be aware of the limitation of any single measure of comparability between countries. Subsumed within the aggregates are implicit value judgements. For example, crucially in the case of a pandemic, there is an implicit assumption that the toll of an older life lost is the same as that of a younger life. However, when a younger life is lost, many more years of life expectancy are lost, and one might want to attach a larger weight to deaths of the young, see section 3. An important argument of the lockdown sceptics is an extreme version of this last point: “the virus is mainly killing off those that were on their way out anyway”, see Kelly (2020). This article quotes a major downward revision of his estimates by British statistician, David Spiegelhalter, who initially suggested that a large number of those dying of Covid-19 would have died in the coming year in any case, but now suggests about 5-15 percent but less than a quarter.19 On the 11th June, cancer specialist Karol Sikora stated for the Telegraph that at least half of those dying of Covid-19 would have died anyway by the end of the Summer of 2020. To try to get a clear position on the issue, Tim Harford (who should be credited for his contribution to the public understanding of data, probability and risk), invited actuary Stuart McDonald20 to comment in the BBC programme “More or Less”.21 McDonald disagreed with the assertion that a majority would have died in the next 3 months as it was neither supported by the data nor his own research. While it is true that three-quarters of the excess deaths were of people aged 75 and above, and that the majority had one or more pre-existing medical conditions (co-morbidities), in practice, life expectancy is quite high. For example, at the age of 80, life expectancy is 9 years for males and 10 years for females. Co-morbidities add little to this, in his opinion, since four-fifths of this cohort has two or more co-morbidities, and 90 percent have one or more (there is of course variation around the average). He stated that it was hard to find examples of less than two years’ life expectancy. From detailed data in the insurance industry, he suggested that an obese male smoker aged 80, and even with heart or pulmonary disorders, would still have a life expectancy of at least 5 years. This suggests that the pandemic had a huge impact not just on the death count but on life-years lost, properly measured. Granular data, disaggregating by region, age and gender, as beginning to be provided by Eurostat (see Table 1), allows the observer to apply their own value judgements. These data, combined with medical information at the country level, would be a crucial input in estimates of life-years lost, alongside counts of excess mortality. Granular data are more informative for evaluating the effectiveness of the policy response and for enhancing scientific understanding to inform policies on ending lock-downs and reducing the risk of a second wave of infections. # 6.  What can we learn from a comparison of the P-scores from the ‘all ages’ data Cumulation of the P-scores over time is required to get a comprehensive summary measure of the impact of the pandemic. Looking at comparisons over a single week or two, for example, is insufficiently reliable as there is much variation over individual weeks. Different observers choose different periods to define the beginning and end of the pandemic, for instance beginning with the day when the first Covid-19 deaths or first 50 such deaths were registered. In contrast, we frame our comparisons using the same length of period for each country that we are comparing. We use 11 weeks, which is a comprehensive period to measure the extent of the first wave of the pandemic in European countries (not long enough for the US). The actual weeks chosen differ by country: the timing matches the P-scores. Cumulating the P-scores for ‘all ages’ data shows, see Figure 3, that England is slightly ahead of Spain, but that they are ‘neck and neck’. There is also little difference between the two types of P-scores (ordinary and variant) in terms of ranking. Italy, Belgium, the Netherlands and France follow Spain, while within the UK, Scotland, Wales and N. Ireland follow England. One caveat is that the English data are from registration data and not occurrence data (see section 4.2). Therefore, the timing of the England peak cannot be compared with the timing of the peak for the other European countries which use occurrence data, since registration of death follows after occurrence of death, with a lag. Examining the detailed P-scores by week for England and the rest of the UK, and the other European countries, it is clear that the peak incidence in Spain is more severe, but more protracted at high levels of deaths in England (Figures 4a and 4b). The same comparison applies to Belgium and Italy, with the latter more protracted. The incidence is quite a bit lower in N. Ireland, which follows Wales and Scotland, behind the England. The detailed numbers behind the pictures are contained in Table 2. The Z-scores from EuroMOMO are also presented. Since Z-scores are based on occurrence data they provide a more comparable picture for England with the other European countries of the timing of the peak week. ## Table 2: Our P-scores/variant P-scores and EuroMOMO’s Z-scores for poor performers showing peak weeks of excess mortality in the first wave of the pandemic **Sources and Notes**25 **_P-Scores [these use data on deaths by week of occurrence– except for the UK which uses data on deaths by week of registration]_** |**_All age-groups_**|_Week 10_|_Week 11_|_Week 12_|_Week 13_|_Week 14_|_Week 15_|_Week 16_|_Week 17_|_Week 18_|_Week 19_|_Week 20_|_Week 21_|_Week 22_|_Week 23_|_ _| |For week ending:(iii)|_8-Mar-20_|_15-Mar-20_|_22-Mar-20_|_29-Mar-20_|_5-Apr-20_|_12-Apr-20_|_19-Apr-20_|_26-Apr-20_|_3-May-20_|_10-May-20_|_17-May-20_|_24-May-20_|_31-May-20_|_7-Jun-20_|_Cumulative P-Score *_| |England||||11|61|79|**116**|113|83|34|45|25|21|7|**55**| |Spain|-2|9|54|132|**154**|116|68|34|17|13|0||||**54**| |Belgium||-5|10|43|90|**104**|80|49|19|17|2|4|||**37**| |Italy|7|38|74|**85**|64|50|33|16|8|1.20|0.48||||**35**| |Netherlands|-7|0|17|46|**74**|72|51|39|24|8|-1||||**29**| |France|-2|6|21|41|**67**|59|41|18|5|4|1||||**24**| |_Within UK_|||||||||||||||| |Scotland||||-4|59|**80**|80|69|56|39|34|17|11|4|**41**| |Wales||||8|38|38|**77**|70|49|13|22|13|8|15|**33**| |N. Ireland||||-7|56|44|45|**56**|42|21|35|10|22|4|**30**| **_Variant P-Scores [these use data on deaths by week of occurrence– except for the UK which uses data on deaths by week of registration]_** |**_All age-groups_**|_Week 10_|_Week 11_|_Week 12_|_Week 13_|_Week 14_|_Week 15_|_Week 16_|_Week 17_|_Week 18_|_Week 19_|_Week 20_|_Week 21_|_Week 22_|_Week 23_|_ _| |For week ending:(iii)|_8-Mar-20_|_15-Mar-20_|_22-Mar-20_|_29-Mar-20_|_5-Apr-20_|_12-Apr-20_|_19-Apr-20_|_26-Apr-20_|_3-May-20_|_10-May-20_|_17-May-20_|_24-May-20_|_31-May-20_|_7-Jun-20_|_Cumulative P-Score *_| |England||||8|49|60|97|**108**|70|22|43|22|19|6|**53**| |Spain|-6|4|49|125|**148**|111|64|32|15|12|0||||**52**| |Belgium||-14|2|34|82|**94**|74|45|15|15|-2|3|||**34**| |Italy|3|34|69|**81**|59|46|28|14|5|-2|0||||**32**| |Netherlands|-17|-8|10|41|68|**68**|46|35|21|6|-5||||**26**| |France|-9|0|15|34|**61**|54|37|16|4|2|-2||||**20**| |_Within UK_|||||||||||||||| |Scotland||||-7|50|**68**|67|63|49|33|27|14|9|1|**39**| |Wales||||4|27|27|**66**|60|40|4|17|11|1|10|**31**| |N. Ireland||||-11|**41**|33|32|47|26|8|23|3|16|-4|**27**| **_Z-scores [these use data on deaths by week of occurrence for all countries]_** |For week ending:|_8-Mar-20_|_15-Mar-20_|_22-Mar-20_|_29-Mar-20_|_5-Apr-20_|_12-Apr-20_|_19-Apr-20_|_26-Apr-20_|_3-May-20_|_10-May-20_|_17-May-20_|_24-May-20_|_31-May-20_| |England|0.57|0.44|5.24|15.15|32.56|**41.24**|36.08|29.38|20.49|14.42|8.7|6.36|4.36| |Spain|0.73|4.72|17.41|40.47|**43.53**|32.84|20.06|10.11|4.73|2.69|-1.18|-0.32|-0.06| |Italy|2.62|6.42|11.72|14.73|**16.94**|13.18|8.89|6.89|4.36|3.32|1.32|1.13|-0.65| |Belgium|0.29|0.85|4.68|11.91|21.01|**30.39**|20.92|12.03|4.44|4.76|1.69|2.43|2.16| |Netherlands|0.78|2.23|6.58|15.29|**21.72**|21.23|15.1|11.47|6.14|1.97|-0.02|0.11|-0.08| |France|0.85|1.89|6.33|13.78|**23.44**|20.06|13.52|4.7|-0.25|-0.79|-1.69|0.12|-3.36| # 7.  Excess mortality for other age groups: 15-64 and 85+ Here, we focus on two age groups, those aged 15-64, containing most of the working age population, and the elderly, those aged 85 or more, many of whom will have been residents in care homes. The evidence here confirms the point made in section 5, that the percentage increase in mortality risk due to the pandemic, measured by the P-score, was higher for older ages. As in section 6, we present the cumulated P-scores over time to get a comprehensive summary measure of the impact of the pandemic for the two age groups. We use the same length of period, 11 weeks, for each country, sufficient to measure the extent of the first wave of the pandemic, though the actual weeks chosen will differ by country as before (see Table 2). What differs from section 6 is that for reasons of data access, ‘England and Wales’ as an entity are examined here, rather than England alone and other regions of the UK. Cumulating the P-scores for both age groups in Figure 5, shows that in all countries, P-scores are lower for the 15-64 age group than for the 85+ age group. ‘England and Wales’ lies slightly below Spain for the 85+ age group but is well above it for the working age group of 15-64. In ranking, Belgium, Italy, France and the Netherlands follow Spain and ‘England and Wales’ for the older age group. But Belgium, France and the Netherlands seem to have sustained far lower deaths than Spain and Italy, and especially ‘England and Wales’, amongst the working age population group. It is unclear to what extent these striking differences are due to differences in rates of infection or differences in mortality risk once infected. Over the 11 pandemic weeks, the cumulative P-score for the 15-64 age group in France was negative, though in the middle of the period there were some weeks when it was positive, see Figure 6. This suggests that social distancing and related measures in France may have reduced deaths from other causes for the working age population, which actually saved lives over the first-wave pandemic period. The Netherlands and Belgium also have remarkably low cumulative P-scores for the 15-64 age group and a number of weeks with negative P-scores. The increase in expected years of life lost, is another measure of the pandemic’s impact (section 3). Average life expectancy in the 15-64 age group is obviously substantially higher than the average for the 85+ age group, so many more expected years of life are lost in each excess death among the younger group than among the older. From the higher incidence of deaths among the working age population in England (which dominates the ‘England and Wales’ figures), it is obvious that England is easily the worst in Europe in terms of expected years of lives lost. Turning to the timing of the pandemic’s incidence, the ‘England and Wales’ data are from registration data and not occurrence data (see section 4.2). Since registration of death follows after occurrence of death, with a lag, the timing of the England and Wales’ peak occurs around one week after its occurrence data, which in turn is later than the peak in most European countries. The timing of the peak week is mostly the same for the two age groups. It is led by Italy in week 13, followed by Spain and France in week 14, the Netherlands in weeks 14-15, Belgium in week 15 and England and Wales in weeks 16-17 (but week 15 according to the occurrence data in section 4.2). Turning to the detail in Figure 6, the peak incidences for the 85+ age group in Spain and in Belgium are more severe, but for ‘England and Wales’ the pattern is more protracted at a high level of deaths. The same comparison applies to France and the Netherlands versus Italy, with the last more protracted. Italy initially dominated the headlines for Covid-19-related deaths but ranked fourth for peak excess mortality figures for the over-85s, below Spain, ‘England and Wales’ and Belgium. Most disturbing, as noted above, is the comparative story for the 15-64 age-group, where England’s relative record in excess mortality in the Covid-19 era is strikingly higher than in the European countries. The 15-64 age-group includes the mass of the working age population. For this age group, the weekly pattern is rather different than for the over-85s, with ‘England and Wales’ displaying both a high peak incidence and protracted high level of deaths, followed by Spain and then Italy. Figure 6 shows that not only is England distinctive in the rate of excess mortality in the peak week for the working age group, but the same is true in comparisons of the two weeks before the peak and the subsequent week. The EuroMOMO graphic visualisations by finer age categories can offer further clues, comparing the 15-44 and 45-64 age groups. Section 3 suggested that comparisons of Z-scores for comparably populous countries and those with larger populations yields reasonable approximations in ranking. England and Spain were the only countries with significant excess mortality in the 15-44 age group according to Z-scores, with England far ahead of Spain. Comparisons of Z-scores with less populous states tend to understate excess mortality in the latter, but evidence from the large countries France and Italy suggest that England is a European outlier. While Z-score comparisons with Wales, Scotland and Northern Ireland understate their excess mortality, the differences compared with England are so large that the conclusion that England was exceptional cannot be avoided. For the 45-64 age group, there is evidence of significant levels of excess mortality, at least in the peak weeks of the pandemic, for all the countries in our comparison group of countries with the exception of Northern Ireland. The Z-score evidence is consistent with the patterns in Figure 6 for the 15-64 age group, even if the Z-scores for the smaller countries, Belgium and the Netherlands slightly understate their relative excess mortality. While the Z-scores also understate excess mortality for the 45-64 age group in Scotland, Wales and Northern Ireland, the figures for England are so much higher, that its outlier status is confirmed for this age group as well as the 15-44 age group. These country differences call for further analysis, especially by age and by regional differences within countries (contrasting, for example, regions with large urban centres and those without). It would be interesting to know to what extent working age excess mortality in London dominated the data for England. It is also possible that cramped housing conditions in London, especially for poorly paid workers, accounts for some of the exceptionalism of the data for England. Regional and country differences by occupational categories should also be illuminating. Aron and Muellbauer (2020b) drew attention to evidence for England and Wales of major occupational differences in the incidence of deaths attributed to Covid-19 and in age-standardised death rates. Of the countries in our comparison group, England and Wales (and Scotland) have the highest ratios of prison population to total population, followed by Spain.26 Further analysis is needed of excess mortality in the prison population as it is possible that failures to protect inmates from infection in countries with high infection rates could help explain some of the country differences of excess mortality for those of working age. ## 7.1 Toward comparable international statistics on excess deaths amongst care home residents One of the stark differences between countries is how well protected were residents in the care homes. The main elements of what happened in care homes in the UK, France, Italy and in Spain is, by now, well-known. Care home staff had inadequate personal protective equipment (PPE) and inadequate access to Covid-19-tests and residents were not well-shielded from potential infection from visitors and staff. Yet, many elderly patients with the Covid-19 infection were released from hospitals to the care homes to reduce the pressure on hospitals from the volume of new cases, and therefore spread the infection to other residents. It is important to explore comparisons between countries of their excess deaths in care homes, for example at the least, the percentage of cumulative Covid-19 deaths that occurred in care homes. The clues in the rate of excess deaths for the 85+ age group, which show the largest increase in Spain, are consistent with newspaper reports of the disaster that befell many care homes in Spain. We were not able to find comparable data at this stage for excess deaths of those normally resident in care homes across the European countries. However, considerable strides have been made in improving international comparability through the pioneering work of the International Long-Term Care Policy Network, e.g. Comas-Herrera et al. (2020). For international comparability, counts of deaths of those resident in care homes, plus those _normally _resident in care homes but dying elsewhere (e.g. in hospital), would have to be regularly published. Few if any countries currently do this. To compute the percentage of excess deaths in care homes or for the comprehensive definition which includes deaths of care home residents outside the care homes, requires data for the previous five years to be able to estimate ‘normal’ deaths.27 Another issue for international comparability concerns differences in definitions of what constitutes a care home. A focus on those over 65 or 75 years of age to exclude some of the other groups, such as refugees, sometimes included in the care home definition, could help international comparability. It is interesting that England and Wales have some of the most comprehensive data on mortality in care homes internationally, see Comas-Herrera et al. (2020). They cite ONS data showing that from early March to 12 June 2020, excess deaths in care homes in England and Wales numbered 26,745, where total excess deaths for England and Wales were 59,138. Thus, about 45 percent of total excess deaths took place in care homes. The ONS have not produced data on excess deaths among those normally resident in care homes, however, clearly a higher percentage as some may have died elsewhere. We would like to know what fraction of excess deaths were of care home residents (within the home or out of it, say in hospital). The Care Quality Commission (CQC) estimates that 84 percent of total care home residents’ deaths took place in care homes in the same period. But this includes normal deaths that would have occurred in the absence of the pandemic, as well as the deaths induced by the pandemic (Covid-19 attributed deaths, mis-measured, unattributed Covid-19 deaths and those caused indirectly by Covid-19, through being untreated, for example). To correct the estimate of 84 percent for normal deaths included in it, and to include deaths of care home residents outside the homes, we consider CQC data on Covid-19-attributed deaths as follows. For the period from early March until the 1 May, the CQC estimate that 72 percent of Covid-19-attributed deaths of care home residents occurred in care homes. They give figures for England alone, from 2 May to 12 June, of and 77 percent. Scaling up the above figure of 45 percent of total excess deaths that took place _in_ care homes for England and Wales, by the 84 percent figure, i.e. 45.2/0.84, would give an estimate of 54 percent for the percentage of all excess mortality accounted for by care home residents in England and Wales (whether inside or out of the care home at time of death). This would almost certainly be an underestimate, since the 84 percent is an over-estimate, but the 54 percent estimate gives a lower bound. To potentially correct the estimate of 84 percent for the normal deaths included in it, and to include deaths of care home residents outside the homes, we consider the specific CQC data on Covid-19-attributed deaths as follows. For the period from early March until the 1 May, the CQC estimate that 72 percent of Covid-19-attributed deaths of care home residents in England and Wales occurred_ in_ care homes. Their equivalent figure for England alone, for the later period of 2 May to 12 June, is 77 percent. However, if the CQC estimate of 77 percent better represented the fraction of excess deaths of care home residents that took place in care homes than the 84 percent figure used above, then 58 percent (i.e. 45.2/0.77), would be the estimate of the fraction of all excess deaths accounted for by residents of care homes (whether inside or out of the care home at time of death). Although Comas-Herrera et al. (2020) examine data sources for 27 countries outside the UK, the only other two countries found with data on excess deaths in care homes are Belgium and France. In Belgium the attribution of deaths to Covid-19 is so widely-defined that the count of Covid-19 attributed deaths actually exceeds the count of excess deaths, see Figure 1 above. For Belgium, Comas-Herrera et al. (2020) report that care home residents accounted for 64 percent of all deaths linked to Covid-19. This suggests that the percentage of excess deaths accounted for by care home residents in Belgium is not far from the 64 percent figure. They report for France that care home residents accounted for 49 percent of Covid-19 deaths. However, since the count of Covid-19 deaths understates excess deaths in France, see Figure 1, it seems likely that a higher percentage of excess deaths occurred among care home residents. For Canada, estimates suggest 81 percent of Covid-19 deaths were among residents in long-term care, but comparable estimates for excess deaths are not available. We can obtain a little more information for the UK by examining data in Table 4 for the four nations comparing the total excess death count in each with information on the location of Covid-19 attributed deaths. The period covered is weeks 13-23 of the pandemic (for dates, see Table 2). For the UK as a whole, 80 percent of excess deaths have been attributed to Covid-19, though for Wales the percentage was far higher.28 For the UK nearly half of excess deaths attributed to Covid-19 occurred in hospital and one quarter in care homes, though many of the hospital deaths were of patients who were resident in care homes. The remaining 20 percent may also be related to Covid-19, as unrecorded or mis-recorded deaths, and those indirectly affected by Covid-19 through other health conditions, such as heart conditions and cancer, being left untreated due to implied capacity constraints in the health service. The percentage of excess deaths that took place in care homes from Covid-19 in England, at about a quarter, matches the overall UK figure, but in Scotland and N. Ireland this was sharply higher at 39 and 35 percent, respectively, and in Wales about 30 percent. Concerning the number of Covid-19 deaths, 30 percent of these occurred in care homes in England and in Wales, with 47 percent in Scotland and 43 percent in Northern Ireland. These percentages of Covid-19 deaths are an underestimate of those normally resident in care homes, because some died in hospital. Hopefully, the compilation of those data will be undertaken by the ONS and the regional health authorities, so that the scale of excess deaths in care homes and its regional variation is properly appreciated. # 8.  International/national statistical agencies should publish improved measures of excess mortality Even if we deem the P-scores and the population-deflated statistics to be comparable across countries, underlying measurement issues of the death count strongly affect the comparability across countries. These definitional differences need to be highlighted and made transparent across country data providers and international organisations reporting excess mortality statistics. The transparent reportage of the Human Mortality Database (HMD) is exemplary in this regard. The impact of the pandemic on deaths has been very strongly related to age and co-morbidity. The proportions of people with one, two or more co-morbidities is highly related to age. The discussion in the previous section highlighted striking differences between countries in age-related P-scores. Publication of P-scores for different age groups in a standard format should therefore be a high priority for international comparability, and HMD is a good source for such data. The evidence is that Covid-19 death rates are substantially higher for men than for women, and how this gender issue varies across countries and over time remains to be explored. The international NUTS classification of regions provides another comparable frame for international comparisons. As regions differ in their urban/rural structure, comparing regional data can give important insights into risk factors for death rates. Moreover, as the incidence of the pandemic differs in timing and intensity, regional comparisons can throw light on the dynamics of the spread of infections. Eurostat has embarked on a major expansion of regional mortality data according to the NUTS classification, which should greatly aid research. Another important source of variation across countries has been in the incidence of Covid-19 deaths in care homes. Countries undoubtedly differ in the proportion of older citizens resident in care homes. It would be highly desirable to develop an international standard frame to define what constitutes a care home, perhaps by the size-distribution of the number of residents. Then, comparisons of excess mortality in care homes would be possible. At present, there are limited internationally comparable data on deaths attributed to Covid-19 that occurred in care homes, see Table 4 for a UK comparison, but almost none on excess deaths of those in care homes or normally resident there. Within countries such as the UK, there have now been several studies comparing the incidence of deaths attributed to Covid-19 by local measures of economic deprivation, occupation and ethnicity. It would highly desirable for parallel studies of excess deaths to be carried out. International comparability is harder in these dimensions given difficulties in standardising categories in measures of deprivation, occupational classification (sometimes not recorded on death certificates, but recoverable from census records) and missing data for some countries on the sensitive issue of ethnicity. Considerable benefits can be reaped from tabulation, cross-tabulation and correlations, trying to control for common features like density by region, in proposing hypotheses. It is important to allow modellers ready access to transparent, comparable international data to a granular level to be combined with other granular data already available (e.g. on inequality) to test such hypotheses in models. Forecasting P-scores from epidemiological models for different scenarios on ending lockdown measures should be an important aid to formulating policy.32 Granular data by location within and between countries must be produced and made accessible for research and forecasting. An example using granular Italian death registry data is Ciminelli and Garcia-Mandicó (2020).33 Belloc et al. (2020) caution against drawing simplistic conclusions from cross-country correlations; they too stress the need for granular, comparable data. National statistical offices should publish weekly P-scores of excess mortalities for the constituent countries, regions and broad social groupings such as care home residents, to help understand the pandemic and inform policy.34 We also argue that EuroMOMO should be mandated to produce P-scores as well as Z-scores to aid comparability across countries and be far more transparent on sources and methods EuroMOMO’s five-year graphs of Z-scores visualise the natural weekly variability, helping to interpret the confidence intervals. Similar practice should be followed for published P-scores, including at national statistical agencies. To end on a cautionary note, excess mortality should also be examined in a longer-term perspective. Spiegelhalter (2020) argues the main impact of Covid-19 may be to shift forward the date of death by a few months for those close to death because of underlying poor health. However, as discussed in section 6, expert actuaries strongly dispute his claim. Moreover, total years of life lost, see section 3, is an alternative indicator of the pandemic’s social toll. Even in the extreme and improbable case envisaged by Spiegelhalter, total years of life lost could still show a large upturn. As we saw in section 6, record excess mortality of those of working age in England, making this a particularly telling issue in comparing with other European countries. If national statistical agencies regularly published monthly, 3-month, 6-month and 12-month moving averages, and weekly P-scores, this would greatly assist our ability to interpret the pandemic data.35 Provision of timely, regularly updated and comparable granular data on excess mortality by national and international statistical agencies should be high on the agenda. It is not enough to leave this to hard-working journalists. --- # Appendix 1 Let x(it) be the weekly death count in country i in week t. It appears that EuroMOMO define36 the excess death measure Z_(it)_ as: Z(it) = (x(it) – μ(it)) / sigma(it) where μ(it) is the predicted value from a model based on historical data up to 5 years ago for seasons of the year less affected by flu and heat waves, and incorporates some trends and seasonals, and where sigma reflects the standard deviation of residuals, but is actually computed from a Poisson process modified for longer tails. Each country in the network estimates its own model within a broad methodology and supplies the hub with its weekly estimates. We think a more transparent and non-parametric measure is the P-score: P(it) = (x(it) – x ̅(it))/x ̅(it) where x̄(it) is the average weekly death count over the previous 5 years. There is also a parametric variant PEM_(it)_ which could be defined on EuroMOMO’s data using their predicted values for ‘normal’ deaths as: P^EM (it) = (x(it) – μ(it))/μ(it) The Poisson assumption, even modified for longer tails, is nowhere near correct for describing the stochastic process generating x(it). The constant mean and independence over time assumptions must be wrong, as explained in Box 2 of the paper, which shows that it is implausible to assume that there are zero systematic mean shifts at all times in the Spring and Autumn. When EuroMOMO measure the standard deviation for ‘normal’ seasons, variation in these systematic factors as well as random noise will be present. This suggests a better model of the death count is: x(it) = β × W(it) + ε(it) where _W(it)_  is a set of variables which reflect the systematic component of variations in deaths and ε(it) is white noise whose distribution can be approximated perhaps by a Poisson or binomial or normal distribution, assuming a constant variance σ_(i)_2. Then it is clear that EuroMOMO’s estimated sigma is an amalgam of the standard deviation, σ_(i)_, and of the variation of  _W(it)_ around an average value. Our simple measure of the excess death rate, a P-score, is then: P(it) = [(β × W(it) - W ̅(it)) + (ε(it) - ε ̅(it))] / (W ̅(it) - ε ̅(it)) using 5-year moving averages for _W-bar(it)_-bar When a pandemic arrives, _W_(it) jumps far from its historical average. _P(it)_ does a good job in indicating the jump in W. It is easily understood by non-specialists. The empirical properties of _P(it)_ can be investigated. One would neither claim that it is serially independent, nor that it has constant variance as that depends on the properties of _W(it)_. Econometricians could try to estimate _W(it)_ with a mix of deterministic variables and state-space terms, to try better to understand the stochastic process driving the death count. Turning to comparisons between regions within a country, it is obvious that the smaller the population of a region, and in particular the smaller the number of normal deaths, the noisier will be the weekly death count relative to the normal expected value. In other words, the ratio:  _Z_(it)/PEM_(it)_ = sigma(it)/μ(it), will be lower in smaller regions. One can extend the argument to populous countries compared to those with smaller populations, if overall normal mortality rates are similar. In practice, movements in PEM_(it)_ will be very similar to movements in _P_(it), especially in pandemics, when the jump in _W(it)_ dominates the variation in both. As a result of averaging data over sub-populations, σ_(i)_/μ(i) at the country vs region level could be argued to vary approximately inversely with the square root of normal deaths for the country and region. This is a result which should not depend on the precise distribution of the white noise, constant variance process for ε(it), i.e. it should not depend on the assumption of Poisson.  However, the EuroMOMO estimate of the standard deviation  is a composite, as noted above, of σ_(i)_ and the variation in _W_(it) about its mean. Thus, it will vary far less with the level of normal deaths or population size than would be the case for σ_(i)_ alone. This is because, on a per capita basis, the systematic factors driving _W_(it) under normal conditions are likely to be quite similar for different regions of a country. In a pandemic, however, the factors driving _W(it)_ can diverge more because, for example, infections spread from different starting points and at different rates. As our paper points out, the rankings of rates of excess deaths in the peak week for the most affected European countries according to Z are quite similar to those from P, even for countries such as Belgium and the Netherlands, which have smaller populations and hence smaller counts of normal deaths than the others. For nations or regions with much smaller counts of normal deaths, the rankings are different as the relative noisiness of weekly death counts compared to normal levels is higher. There is no simple adjustment to convert published Z-scores to P-scores without access to data on normal and actual deaths. In particular, it would be quite wrong to adjust the published Z-scores by the square root of population size of each country to make them more comparable. Comparability is best achieved using the P-scores. # Bibliography ACN. 2020a. “[Coronavirus crisis shines spotlight on elderly care homes](https://www.catalannews.com/society-science/item/coronavirus-crisis-shines-spotlight-on-elderly-care-homes).” Catalan News, Barcelona, 1 April 2020. ACN. 2020b. “[Prosecutor investigating handling of Covid-19 in seven Catalan care homes](https://www.catalannews.com/society-science/item/prosecutor-investigating-handling-of-covid-19-in-seven-catalan-care-homes).” Catalan News, Barcelona, 19 April 2020. Aron, J. and J. Muellbauer. 2020a. “[Measuring excess mortality: England is the European outlier in the Covid-19 pandemic.](https://voxeu.org/article/excess-mortality-england-european-outlier-covid-19-pandemic)” VOXEU, Centre for Economic Policy Research, London, 18 May, 2020. Aron, J. and J. Muellbauer. 2020b. “[Measuring excess mortality: the case of England during the Covid-19 Pandemic.](https://www.inet.ox.ac.uk/news/inet-oxford-covid-19-blog/)”   INET Oxford COVID-19 Research, Economics Department, Oxford University. Belloc, M., P. Buonanno, F. Drago, R. Galbiati and P. Pinotti. 2020. “Cross-country correlation analysis for research on Covid-19.” VOXEU, Centre for Economic Policy Research, London, 28 March 2020. Burn-Murdoch, J., V. Romei and C. Giles. 2020. “[Global coronavirus death toll could be 60% higher than reported](https://www.ft.com/content/6bd88b7d-3386-4543-b2e9-0d5c6fac846c?emailId=5ea6e9bcd26cbd000484719d&segmentId=2785c52b-1c00-edaa-29be-7452cf90b5a2).” Financial Times, 26 April 2020. Ciminelli, G. and S. Garcia-Mandicó. 2020. “[COVID-19 in Italy: An analysis of death registry data](https://voxeu.org/article/covid-19-italy-analysis-death-registry-data).”** **VOXEU, Centre for Economic Policy Research, London, 22 April 2020. Comas-Herrera, A. and J-L. Fernandez. 2020. “[England: Estimates of mortality of care home residents linked to the COVID-19 pandemic](https://ltccovid.org/wp-content/uploads/2020/05/England-mortality-among-care-home-residents-report-17-May.pdf).” Report available at LTCcovid.org, International Long-Term Care Policy Network, CPEC-LSE, 17 May 2020. Comas-Herrera A., J. Zalakaín, C. Litwin, A. T. Hsu, E. Lemmon, D. Henderson and J-L Fernández. 2020. “[Mortality associated with COVID-19 outbreaks in care homes: early international evidence.”](https://ltccovid.org/2020/04/12/mortality-associated-with-covid-19-outbreaks-in-care-homes-early-international-evidence/) Report available at LTCcovid.org, International Long-Term Care Policy Network, CPEC-LSE, 26 June 2020. Denaxas, S., H. Hemingway, L. Shallcross, M. Noursadeghi, B. Williams, D. Pillay, L. Pasea, A. González-Izquierdo, C. Pagel, S. Harris, A. Torralbo, C. Langenberg, W. Wong, and A. Banerjee. 2020. “[Estimating excess 1- year mortality from COVID-19 according to underlying conditions and age in England: a rapid analysis using NHS health records in 3.8 million adults.](https://www.researchgate.net/publication/340092652_Estimating_excess_1-_year_mortality_from_COVID-19_according_to_underlying_conditions_and_age_in_England_a_rapid_analysis_using_NHS_health_records_in_38_million_adults)” 10.13140/RG.2.2.36151.27047. The Economist. 2020. “[Tracking Covid-19 excess deaths across countries](https://www.economist.com/graphic-detail/2020/04/16/tracking-covid-19-excess-deaths-across-countries),” The Economist, 16 April, 2020, Edwards, M. and S. McDonald. 2020. “[The co-morbidity question.](https://www.theactuary.com/features/2020/05/07/co-morbidity-question)” The Actuary, Institute and Faculty of Actuaries, 7th May 2020. Farrington, C.P., N.J Andrews, A.D. Beale and M.A. Catchpole. 1996. “A statistical algorithm for the early detection of outbreaks of infectious disease.” Journal of theRoyal Statistical Society A 159: 547-563. Kelly, J. 2020. “[Spiegelhalter says majority of Covid deaths would not have occurred in coming year.](https://ftalphaville.ft.com/2020/05/22/1590156197000/Spiegelhalter-says-vast-majority-of-Covid-deaths-would-not-have-occurred-in-coming-year-/)” Financial Times, 22 May 2020. Krelle, H.,  C. Barclay  and C. Tallack. 2020. “[Understanding excess mortality. What is the fairest way to compare COVID-19 deaths internationally?](https://www.health.org.uk/news-and-comment/charts-and-infographics/understanding-excess-mortality-the-fairest-way-to-make-international-comparisons)” The Health Foundation, 6 May 2020. Loomes, G. and L. McKenzie. 1989. ""The use of QALYs in health care decision making."" Social Science and Medicine, Elsevier 28(4): 299-308, January. Rubin, D., G. Tasian and J. Huang. 2020. “[COVID-19 Outlook: Ringing the Alarm Bell for Epicenters, Waving the Caution Flag for Hotspots.](https://policylab.chop.edu/blog/covid-19-outlook-ringing-alarm-bell-epicenters-waving-caution-flag-hotspots)” Article, Children’s Hospital Philadelphia Policy Lab, 24 June 2020. Santaeulalia, I, F. Peinado, E. Sevillano and J. Mateo. 2020. “[Scandal over Covid-19 deaths at Madrid nursing homes sparks fierce political row](https://english.elpais.com/politics/2020-06-10/scandal-over-covid-19-deaths-at-madrid-nursing-homes-sparks-fierce-political-row.html)."" El País, Madrid, 10 Jun 2020. Spiegelhalter, D. 2020b. “[Coronavirus deaths: how does Britain compare with other countries?](https://www.theguardian.com/commentisfree/2020/apr/30/coronavirus-deaths-how-does-britain-compare-with-other-countries)” The Guardian, 20 April 2020 Spiegelhalter, D. 2020b. “[How much ‘normal’ risk does Covid represent?](https://medium.com/wintoncentre/how-much-normal-risk-does-covid-represent-4539118e1196)”, Winton Centre for Risk and Evidence Communication, Cambridge, 21 March, 2020. Tallack, C.  D. Finch, N. Mihaylova, C. Barclay and T. Watt. 2020. “[Understanding excess deaths: variation in the impact of COVID-19 between countries, regions and localities.](https://www.health.org.uk/news-and-comment/charts-and-infographics/understanding-excess-deaths-countries-regions-localities)” The Health Foundation, 4 June 2020. Tozer, J. 2020. “[Measuring the true toll of the pandemic](https://medium.economist.com/measuring-the-true-toll-of-the-pandemic-fa7e003b3ff4)”, The Economist, 24 April, 2020. Wu, J., A. McCann, J. Katz and E. Peltier. 2020. “[46,000 Missing Deaths: Tracking the True Toll of the Coronavirus Outbreak](https://www.nytimes.com/interactive/2020/04/21/world/coronavirus-missing-deaths.html)”, The New York Times, 30 April, 2020. R is the virus reproduction rate, which needs to be kept below 1 to avoid exponential growth of infections. Details on this can be found in the evidence of Prof. John Edmunds to the UK Science and Technology Parliamentary Select Committee on 7th May. He explained that while excess mortality data lag Covid-19 infections, the data are an important check on earlier estimates of the rate of spread of the virus. **_Data_** **_sources:_**_ _UK - [ONS](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/weeklyprovisionalfiguresondeathsregisteredinenglandandwales). Covid-19 deaths in other European countries - [European Centre for Disease Prevention and Control (ECDC)](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide). **_Notes: _**‘Lab-confirmed’ cases, in all countries except Belgium and France, where ‘lab-confirmed plus probable’ cases are used. UK taken from Week 13 (week ending 27th March) to Week 23 (week ending 5th June). For non-UK countries, we have assumed a one-week lag in Covid-19 death registrations. This means reported and excess deaths for France, Spain, Italy, and the Netherlands are taken from Weeks 10-20; and Covid-19 deaths from ECDC are taken from Weeks 11-21. For Belgium, we have taken Week 11-21 for reported and excess deaths; Covid-19 deaths are taken from Week 12-22. See webpage: “[National Center for Health Statistics](https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm)”, Centers for Disease Control and Prevention (CDC), US Government. EuroMOMO is a European mortality monitoring entity, aiming to detect and measure excess deaths related to seasonal influenza, pandemics and other public health threats. Official national mortality statistics are provided weekly from the 24 European countries and regions in the EuroMOMO collaborative network, supported by the European Centre for Disease Prevention and Control (ECDC) and the World Health Organization (WHO). **_Data sources:_**_ _The Economist (2020): “[Tracking Covid-19 excess deaths across countries](https://www.economist.com/graphic-detail/2020/04/16/tracking-covid-19-excess-deaths-across-countries)”, 16 April, 2020, and Tozer (2020): “[Measuring the true toll of the pandemic](https://medium.economist.com/measuring-the-true-toll-of-the-pandemic-fa7e003b3ff4)”, 24 April, 2020. For the Economist, Tozer measures excess deaths from the week the first 50 Covid deaths were reported, to around April 12. As of 15 May, The Economist’s J. Tozer and M. González publish the raw country data on [GitHub](https://github.com/TheEconomist/covid-19-excess-deaths-tracker/tree/master/output-data/excess-deaths). Also see “[Global coronavirus death toll could be 60% higher than reported](https://www.ft.com/content/6bd88b7d-3386-4543-b2e9-0d5c6fac846c?emailId=5ea6e9bcd26cbd000484719d&segmentId=2785c52b-1c00-edaa-29be-7452cf90b5a2)”, Financial Times, 26 April, 2020 and Wu et al. (2020): “[46,000 Missing Deaths: Tracking the True Toll of the Coronavirus Outbreak](https://www.nytimes.com/interactive/2020/04/21/world/coronavirus-missing-deaths.html)”, The New York Times, 30 April, 2020. See EuroMOMO webpage: “[Methods](https://www.euromomo.eu/how-it-works/methods/)”. The Poisson is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time if these events occur with a known constant mean rate and independently of the time since the last event. The calculation is described in Farrington et al. (1996). See EuroMOMO webpage: “Methods”. See Letters, The Guardian: [UK behind the curve in curbing Covid-19 deaths](https://www.theguardian.com/world/2020/may/04/uk-behind-the-curve-in-curbing-covid-19-deaths), 4 May 2020. Transmission and hence rates of infection are also influenced by factors like the nature of social distancing, availability and use of face masks, and cultural differences in the exercise of self-discipline and following of advice. For the Indonesian capital Jakarta, burial data are used by the Financial Times, [Burn-Murdoch ](https://www.ft.com/stream/e191658e-c66a-45bc-9bad-343bdc4210b3)et al. (2020). See El Pais: Santaeulalia et al. (2020); Catalan News: ACN (2020a, 2020b). See their method webpage, Table 1. See their metafile, Table 1. UK Office for National Statistics (ONS) Spiegelhalter (2020) suggests that for the age group 20-59, the increase in mortality risk compared to normal mortality risk for Covid-19-infected individuals is lower than for those aged 60 and over. See [Age-standardized mortality rate (per 100 000 population), ](https://apps.who.int/gho/data/node.wrapper.imr?x-id=78)WHO.See [Age-standardized mortality rate (per 100 000 population), ](https://apps.who.int/gho/data/node.wrapper.imr?x-id=78)WHO. See the [User guide to mortality statistics ](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/methodologies/userguidetomortalitystatisticsjuly2017#death-rates-ratios-and-standardisation)of the ONS, UK, and its report on [Deaths involving COVID-19 by localarea and socioeconomic deprivation.](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/deathsinvolvingcovid19bylocalareasanddeprivation/deathsoccurringbetween1marchand31may2020) “I used to think this figure would be quite big but I’ve reduced my estimate now. I’m not going to put a precise figure on it, but I definitely think the proportion of those who would have died over the next year anyway would be well below a quarter, maybe 5 to 15 percent, rather than 'less than a quarter'.” Kelly (2020), Financial Times. Stuart McDonald is an actuary with the Institute and Faculty of Actuaries and a founding member of the group of industry professionals creating a Covid-19 actuaries’ response group, launched in March 2020. For more detailed information on comorbidity, see Edwards and McDonald (2020). Tim Harford: [Quarantine, Test and Trace and BODMAS: Is it true that Covid-19 mostly kills people who woulddie soon anyway.](https://www.bbc.co.uk/sounds/play/m000kdr6), More or Less, Radio 4, BBC, 17 June 2020. **_Data sources: _**The P-scores and variant P-scores are calculated by the authors using the Human Mortality Database, see meta file: [https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf,](https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf) and the Office for National Statistics for the UK. Cumulative P-scores cover the weeks shown in Table 2. **_Notes:_**_ _(i) Incomplete figures for England which is not yet at normal levels. (ii) Deaths by week of registration versus deaths by week of occurrence: HMD use deaths by week of occurrence for all the above countries, except the UK, where deaths by week of registration are used, see section 4.2. **_Data sources:_**_ _The P-scores are calculated by the authors using the Human Mortality Database, see meta file: [https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf, ](https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf)and the Office for National Statistics for the UK. **_Notes:_**_ _(i) Deaths by week of registration versus deaths by week of occurrence: HMD use deaths by week of occurrence for all the above countries, except the UK, where deaths by week of registration are used, see section 4.2. (ii) The country ordering is by cumulative P-scores. The cumulative P-scores are shown in Figure 3 and cover the weeks shown in Table 2. **_Data source:_**_ _UK Office for National Statistics. **_Data source:_** Z-scores are extracted from the EuroMOMO website, 11-Jun-2020. The P-scores and variant P-scores are calculated by the authors using the Human Mortality Database, see meta file: [https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf](https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf), and the Office for National Statistics for the UK. Cumulative P-scores cover the weeks shown. **_Notes:_**  (i) The peak weeks for different countries are in **bold**. The peak weeks for the all age group category is the same for the UK countries. (ii) The country ordering is by cumulative P-scores. (iii) The ONS defines a week as ending on Friday; EuroMOMO define a week as ending on Sunday; for HMD definitions, it is also Sunday for the above countries, except for England and Wales, which is Friday, see the metafile. (iv) Deaths by week of registration versus deaths by week of occurrence:  EuroMOMO use deaths by occurrence and HMD use deaths by week of occurrence for the above countries, except in the case of the UK, where deaths by week of registration are used, see section 4.2.  (v) Revisions in the raw death count data: there have been recent large revisions in the Spanish death count, see section 4.2. (vi) Which weeks are chosen matter: for example, calculating Italy’s cumulative P-score for weeks 9 to 19, instead of weeks 10 to 20, reasonable to do since the pandemic struck first in Italy, gives Italy a P-score of 37 and a variant P-score of 35, putting Italy and Belgium neck and neck. See [Prison Population Rate](https://ourworldindata.org/grapher/prison-population-rate) on _Our World in Data_. If there were large changes over these five years in the fraction of the elderly population who were resident in care homes, ‘normal’ deaths could be adjusted for trends in the care home population. Thus, most excess deaths in Wales in the pandemic period were due to Covid-19, with some mitigating factors from fewer other deaths, e.g. from traffic accidents. This may reflect more transparent recording of Covid-19 deaths. Wales also had a much higher percentage of excess deaths from Covid-19 occurring in the hospitals. **_Data sources:_**_ _The P-scores are calculated by the authors using the Human Mortality Database, see meta file: [https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf,](https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf) and the Office for National Statistics for the UK. Cumulative P-scores cover the weeks shown in Table 2. The country ordering is by cumulative P-scores for the 85+ age group. **_Notes:_**_ _(i) Deaths by week of registration versus deaths by week of occurrence: HMD use deaths by week of occurrence for all the above countries, except the UK, where deaths by week of registration are used, see section 4.2. **_Data sources:_**_ _The P-scores are calculated by the authors using the Human Mortality Database, see meta file: [https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf, ](https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf)and the Office for National Statistics for the UK. Cumulative P-scores cover the weeks shown in Table 2. The country ordering is by cumulative P-scores for _each age group. _**_Notes:_**_ _(i) Deaths by week of registration versus deaths by week of occurrence: HMD use deaths by week of occurrence for the above countries, except in the case of the UK, where deaths by week of registration are used, see section 4.2. (ii) for France and Italy data for some weeks were absent. (iii) HMD only report for England and Wales. There are no separate data for England and Wales, and no data for the rest of the UK. **_Data sources: _**UK - [ONS](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/weeklyprovisionalfiguresondeathsregisteredinenglandandwales); England and Wales - [UK ONS](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/weeklyprovisionalfiguresondeathsregisteredinenglandandwales); Scotland - [National Records of Scotland](https://www.nrscotland.gov.uk/covid19stats); N.Ireland - [Northern Ireland Statistics and Research Agency (NISRA)](https://www.nisra.gov.uk/publications/weekly-deaths) **_Notes:_**_ _All UK countries taken from Week 13 (week ending 27th March) to Week 23 (week ending 5th June). A study which forecasts the one-year ahead mortality is Denaxas et al. (2020). They analyse daily death registry data for over 1000 Italian municipalities, which suggest that deaths registered as Covid capture only about half of excess deaths. They find strong evidence that locations where mass testing, contact tracing, and at-home care provision was introduced experienced lower numbers of excess deaths. At more granular levels, the weekly data can become noisy. Averages over longer periods are more informative. In the case of the ONS, the latest update of their research plans, dated 21 April, 2020, suggests only a limited agenda for investigating excess deaths: see the webpage, [Statement of upcoming analysis on deaths andcoronavirus (COVID-19).](https://www.ons.gov.uk/news/statementsandletters/statementofupcominganalysisondeathsandcoronaviruscovid19) See EuroMOMO webpage: “[Methods](https://www.euromomo.eu/how-it-works/methods/)”.",A pandemic primer on excess mortality statistics and their comparability across countries 1tTybpXlpzAD9hW18gDT7gLhfcdu819gx40xko8y8sik,breakdown-co2-aviation,article,"{""toc"": [{""slug"": ""the-richest-half-are-responsible-for-90-of-air-travel-co2-emissions"", ""text"": ""The richest half are responsible for 90% of air travel CO2 emissions"", ""title"": ""The richest half are responsible for 90% of air travel CO2 emissions"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Global aviation – both passenger flights and freight – emits around one billion tonnes of carbon dioxide (CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "") each year. This was equivalent to around 2.4% of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" emissions in 2018."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How do global aviation emissions break down?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart gives the answer. This data is sourced from the 2019 "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""International Council on Clean Transportation(ICCT)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" report on global aviation."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most emissions come from passenger flights – in 2018, they accounted for 81% of aviation’s emissions; the remaining 19% came from freight, the transport of goods."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Sixty percent of emissions from"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" passenger"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" flights come from international travel; the other 40% come from domestic (in-country) flights."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When we break passenger flight emissions down by travel distance, we get a (surprisingly) equal three-way split in emissions between short-haul (less than 1,500 kilometers); medium-haul (1,500 to 4,000 km); and long-haul (greater than 4,000 km) journeys."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""Global-breakdown-of-aviation-emissions.png"", ""parseErrors"": []}, {""text"": [{""text"": ""The richest half are responsible for 90% of air travel CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" emissions"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The global inequalities in how much people fly become clear when we compare aviation emissions across countries of different income levels. The ICCT split these emissions based on World Bank's four "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/world-banks-income-groups?year=latest"", ""children"": [{""text"": ""income groups"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A further study by Susanne Becek and Paresh Pant (2019) compared the contribution of each income group to global air travel emissions versus its share of world population."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This comparison is shown in the visualization."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The ‘richest’ half of the world (high and upper-middle-income countries) were responsible for 90% of air travel emissions."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Looking at specific income groups:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""Only 16% of the world population live in high-income countries yet the planes that take off in those countries account for almost two-thirds (62%) of passenger emissions;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Upper-middle income countries are home to 35% of the world population, and contribute 28% of emissions;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Lower-middle income countries are home to the largest share (40% of the world), yet the planes taking off there just account for 9%;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The poorest countries – which are home to 9% of the world's population – emit just 1%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""Inequalities-in-CO2-emissions-from-air-travel.png"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""9e3e99450388ffd269bd533e92fcdca385474a1f"": {""id"": ""9e3e99450388ffd269bd533e92fcdca385474a1f"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Becken, S. and P. Pant (2019). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://amadeus.com/en/insights/white-paper/airline-initiatives-to-reduce-climate-impact"", ""children"": [{""text"": ""Airline initiatives to reduce climate impact: ways to accelerate action"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (White paper)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""bf72ca5eeef1cd7556eb2946632d7ed107f922b2"": {""id"": ""bf72ca5eeef1cd7556eb2946632d7ed107f922b2"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Note that this is based on categorizations from the average income level of countries, and does not take into account variation in income "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""within "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""countries. If we were to look at this distribution based on the income level of individuals rather than countries, the inequality in aviation emissions would be even larger."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ec4cd3098c60b12ae2d571bf4b725a71e3352227"": {""id"": ""ec4cd3098c60b12ae2d571bf4b725a71e3352227"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Graver, B., Zhang, K., & Rutherford, D. (2019). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://theicct.org/sites/default/files/publications/ICCT_CO2-commercl-aviation-2018_20190918.pdf"", ""children"": [{""text"": ""CO2 emissions from commercial aviation, 2018"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""The International Council of Clean Transportation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Global inequalities in CO₂ emissions from aviation"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""Aviation accounts for 2.5% of global CO₂ emissions. Where do these emissions come from?"", ""dateline"": ""October 23, 2020"", ""subtitle"": ""Aviation accounts for 2.5% of global CO₂ emissions. Where do these emissions come from?"", ""featured-image"": ""Global-breakdown-of-aviation-emissions.png""}",1,2023-09-01 15:37:44,2020-10-23 10:30:00,2024-03-18 15:41:59,listed,ALBJ4LtdMLq3wp0gKMfJk474eJif_Kaf4elzt_Xauhryu3jKb0hydGr886h-vkzzyFLWdnJfYrSXZ4Mu004PTg,,"Global aviation – both passenger flights and freight – emits around one billion tonnes of carbon dioxide (CO2) each year. This was equivalent to around 2.4% of CO2 emissions in 2018. How do global aviation emissions break down? The chart gives the answer. This data is sourced from the 2019 _International Council on Clean Transportation(ICCT)_ report on global aviation.1 Most emissions come from passenger flights – in 2018, they accounted for 81% of aviation’s emissions; the remaining 19% came from freight, the transport of goods. Sixty percent of emissions from_ passenger_ flights come from international travel; the other 40% come from domestic (in-country) flights. When we break passenger flight emissions down by travel distance, we get a (surprisingly) equal three-way split in emissions between short-haul (less than 1,500 kilometers); medium-haul (1,500 to 4,000 km); and long-haul (greater than 4,000 km) journeys. ## The richest half are responsible for 90% of air travel CO2 emissions The global inequalities in how much people fly become clear when we compare aviation emissions across countries of different income levels. The ICCT split these emissions based on World Bank's four [income groups](https://ourworldindata.org/grapher/world-banks-income-groups?year=latest). A further study by Susanne Becek and Paresh Pant (2019) compared the contribution of each income group to global air travel emissions versus its share of world population.2 This comparison is shown in the visualization. The ‘richest’ half of the world (high and upper-middle income countries) were responsible for 90% of air travel emissions.3 Looking at specific income groups: * Only 16% of the world population live in high-income countries yet the planes that take off in those countries account for almost two-thirds (62%) of passenger emissions; * Upper-middle income countries are home to 35% of the world population, and contribute 28% of emissions; * Lower-middle income countries are home to the largest share (40% of the world), yet emit the planes taking off there just account for 9%; * The poorest countries – which are home to 9% of the world population – emit just 1%. Note that this is based on categorisations from the average income level of countries, and does not take account of variation in income _within _countries. If we were to look at this distribution based on the income level of individuals rather than countries, the inequality in aviation emissions would be even larger. Becken, S. and P. Pant (2019). [Airline initiatives to reduce climate impact: ways to accelerate action](https://amadeus.com/en/insights/white-paper/airline-initiatives-to-reduce-climate-impact) (White paper). Graver, B., Zhang, K., & Rutherford, D. (2019). [CO2 emissions from commercial aviation, 2018](https://theicct.org/sites/default/files/publications/ICCT_CO2-commercl-aviation-2018_20190918.pdf). _The International Council of Clean Transportation_.",Global inequalities in CO₂ emissions from aviation 1tTAuFQ-Y3iCNfVo-JVvj67bi3aciRG6_wUcxilxyUrc,malaria-past-prevalence,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""The animal that kills most people every year isn’t the one that first comes to mind. When it comes to killing humans, no other animal comes close: Mosquitoes kill almost half a million people per year. One death every minute."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Mosquitoes are so very dangerous to us because they carry lethal diseases. The half a million annual deaths are caused by Dengue, Japanese encephalitis, Yellow fever, some smaller diseases and the very worst of them: Malaria."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Malaria is a disease that is transmitted from person to person by infected mosquitoes. The bite of an infected Anopheles mosquito transmits a parasite that enters the victim’s blood system and travels into the person’s liver where the parasite reproduces. There the parasite causes a high fever that involves shaking chills and pain. In the worst cases malaria leads to coma and death."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""From half the world to a quarter of the world within one century"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Malaria "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/malaria#the-history-of-malaria"", ""children"": [{""text"": ""left its mark"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" on our history, our bodies, drinks, and for thousands of years the deaths of people in all corners of the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But in the last few generations, humanity gained ground in this long-lasting battle against the disease. The map shows in which regions of the world malaria is prevalent today (in purple) and where it was prevalent in the past. Just a few generations ago malaria was common in many more places around the world than it is today. Over the course of the 20"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""th"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}, {""text"": "" century the disease was eliminated in many populous regions of the world, saving the lives of millions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What the map makes clear is that malaria is not a tropical disease, but a disease that was eliminated everywhere except for the tropics. Historically malaria was prevalent in Europe and North America – poet Friedrich Schiller contracted the disease in Mannheim, Oliver Cromwell in Ireland, and Abraham Lincoln in Illinois."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since then the disease has been eliminated not only there, but also in East Asia and Australia and in many parts in the Caribbean, South America, and Africa."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The researchers estimate that historically – and up to around 1900 – our ancestors were at risk from malaria across about half of the world’s land surface (53%).  Since then the area where humans are at risk of malaria contracted to a quarter (27%)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The WHO reports that the global mortality rate has declined by 90% between 1900 and the end of the 20th century."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""Map of past prevalence of malaria"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""type"": ""heading"", ""level"": 5, ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Previous-prevalence-of-malaria-world-map.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Why are these parts not malarious anymore?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Three factors were responsible for this global reduction of malarious regions:"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""First, public health measures, especially the widespread use of insecticides to attack the mosquito. Second, the drainage of swampland for "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/yields-and-land-use-in-agriculture"", ""children"": [{""text"": ""expanding agricultural land"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" had the side effect of restricting the breeding grounds for mosquitoes. And third, social and economic development which not only made treatment available to those that were infected, but also led to improvements in housing conditions which lowered the chances of infection in the first place."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All three factors – insecticides, land use change, and economic development – were major reasons that Europe and the other regions shown in shades of yellow, orange, and red are free of malaria today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""21140f8ce414f8f37785724622598ad618bdf0b0"": {""id"": ""21140f8ce414f8f37785724622598ad618bdf0b0"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See Hay et al (2004) above and de Zulueta J. (1994) – Malaria and ecosystems: from prehistory to posteradication. In Parassitologia. 1994 August. 36(1-2):7-15. World Bank (2009) – World Development Report (2009) - Part I: Reshaping Economic Geography. Washington, DC: World Bank. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""http://documents.worldbank.org/curated/en/730971468139804495/pdf/437380REVISED01BLIC1097808213760720.pdf"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""62cfbf42c8151398e5138d90fb598e2a5698e1ea"": {""id"": ""62cfbf42c8151398e5138d90fb598e2a5698e1ea"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Mosquito-borne diseases include malaria, which is causing by far the most deaths, but also Zika Virus, Dengue Fever, Chikungunya, Yellow Fever, West Nile Virus, Japanese encephalitis, and Lymphatic filariasis, which caused around 60,000 deaths in 2016. See "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/causes-of-death#deaths-by-animal"", ""children"": [{""text"": ""https://ourworldindata.org/causes-of-death#deaths-by-animal"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""afc9ace05983444a0bcaf6a02980b09ed2e140af"": {""id"": ""afc9ace05983444a0bcaf6a02980b09ed2e140af"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The original map was published in Simon I Hay, Carlos A Guerra, Andrew J Tatem, Abdisalan M Noor, and Robert W Snow (2004) – The global distribution and population at risk of malaria: past, present, and future. In The Lancet Infectious Diseases 2004 June; 4(6): 327–336. DOI: 10.1016/S1473-3099(04)01043-6. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(04)01043-6/fulltext?version=printerFriendly"", ""children"": [{""text"": ""https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(04)01043-6/fulltext"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" I have digitized the Figure 1 using image tracing in Adobe Illustrator. The historical mapping of the prevalence of malaria is based on the pioneering work of Lysenko in the 1960s: Lysenko AJ, Semashko IN. Geography of malaria (1968) – A medico-geographic profile of an ancient disease. In: Lebedew AW, editor. Itogi Nauki: Medicinskaja Geografija. Academy of Sciences, USSR; Moscow: 1968. pp. 25–146. Lysenko AJ, Beljaev AE (1969) – An analysis of the geographical distribution of Plasmodium ovale. Bull World Health Organization; 40:383–94."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b12c3beb40c2778528595c4a6753d982ddf86de2"": {""id"": ""b12c3beb40c2778528595c4a6753d982ddf86de2"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""On the cause of Oliver Cromwell’s death see "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.olivercromwell.org/faqs8.htm"", ""children"": [{""text"": ""the FAQs at OliverCromwell.org"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", on Friedrich Schiller see "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.br.de/radio/bayern2/sendungen/radiowissen/deutsch-und-literatur/friedrich-schiller-thema100.html"", ""children"": [{""text"": ""Bayerischer Rundfunk here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", on Abraham Lincoln see "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.physical-lincoln.com/books.html"", ""children"": [{""text"": ""‘The Physical Lincoln"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""’. Other famous victims – although it is not always possible to diagnose the disease of historical figures – were German painter Albrecht Dürer, who contracted the disease in the Netherlands, and several popes, who died of the disease as malaria was very prevalent in Italy until recently."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e4f68a436a302ce7602948dd1a7d60ddeeec7f4e"": {""id"": ""e4f68a436a302ce7602948dd1a7d60ddeeec7f4e"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See \""Box 4.1 Malaria-related mortality in the 20th century\"" in the World Health Organization's "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.who.int/whr/1999/en/whr99_en.pdf"", ""children"": [{""text"": ""World Health Report (1999)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Malaria was common across half the world – since then it has been eliminated in many regions"", ""authors"": [""Max Roser""], ""excerpt"": ""Malaria has been eliminated from large parts of Europe, the Americas, East Asia, Australia, and the Caribbean."", ""dateline"": ""April 25, 2019"", ""subtitle"": """", ""featured-image"": ""malaria-past-prevalence-featured-image.png""}",1,2023-11-29 18:12:17,2019-04-25 11:42:55,2024-03-18 15:41:59,listed,ALBJ4LsWN1kUk-YCBgyGFiMRPh_7H4iKutXNN-MHSs2abFs2D_OutZrb9NNr7QIfn01VUym5gfONJ-kYCsMHVQ,,"The animal that kills most people every year isn’t the one that first comes to mind. When it comes to killing humans, no other animal comes close: Mosquitoes kill almost half a million people per year. One death every minute. Mosquitoes are so very dangerous to us because they carry lethal diseases. The half a million annual deaths are caused by Dengue, Japanese encephalitis, Yellow fever, some smaller diseases and the very worst of them: Malaria.1 Malaria is a disease that is transmitted from person to person by infected mosquitoes. The bite of an infected Anopheles mosquito transmits a parasite that enters the victim’s blood system and travels into the person’s liver where the parasite reproduces. There the parasite causes a high fever that involves shaking chills and pain. In the worst cases malaria leads to coma and death. # From half the world to a quarter of the world within one century Malaria [left its mark](https://ourworldindata.org/malaria#the-history-of-malaria) on our history, our bodies, drinks, and for thousands of years the deaths of people in all corners of the world. But in the last few generations, humanity gained ground in this long-lasting battle against the disease. The map shows in which regions of the world malaria is prevalent today (in purple) and where it was prevalent in the past. Just a few generations ago malaria was common in many more places around the world than it is today. Over the course of the 20th century the disease was eliminated in many populous regions of the world, saving the lives of millions. What the map makes clear is that malaria is not a tropical disease, but a disease that was eliminated everywhere except for the tropics. Historically malaria was prevalent in Europe and North America – poet Friedrich Schiller contracted the disease in Mannheim, Oliver Cromwell in Ireland, and Abraham Lincoln in Illinois.2 Since then the disease has been eliminated not only there, but also in East Asia and Australia and in many parts in the Caribbean, South America, and Africa. The researchers estimate that historically – and up to around 1900 – our ancestors were at risk from malaria across about half of the world’s land surface (53%).  Since then the area where humans are at risk of malaria contracted to a quarter (27%). The WHO reports that the global mortality rate has declined by 90% between 1900 and the end of the 20th century.3 ##### Map of past prevalence of malaria4 # Why are these parts not malarious anymore? Three factors were responsible for this global reduction of malarious regions:5 First, public health measures, especially the widespread use of insecticides to attack the mosquito. Second, the drainage of swampland for [expanding agricultural land](https://ourworldindata.org/yields-and-land-use-in-agriculture) had the side effect of restricting the breeding grounds for mosquitoes. And third, social and economic development which not only made treatment available to those that were infected, but also led to improvements in housing conditions which lowered the chances of infection in the first place. All three factors – insecticides, land use change, and economic development – were major reasons that Europe and the other regions shown in shades of yellow, orange, and red are free of malaria today. Mosquito-borne diseases include malaria, which is causing by far the most deaths, but also Zika Virus, Dengue Fever, Chikungunya, Yellow Fever, West Nile Virus, Japanese encephalitis, and Lymphatic filariasis, which caused around 60,000 deaths in 2016. See [https://ourworldindata.org/causes-of-death#deaths-by-animal](https://ourworldindata.org/causes-of-death#deaths-by-animal) On the cause of Oliver Cromwell’s death see [the FAQs at OliverCromwell.org](http://www.olivercromwell.org/faqs8.htm), on Friedrich Schiller see [Bayerischer Rundfunk here](https://www.br.de/radio/bayern2/sendungen/radiowissen/deutsch-und-literatur/friedrich-schiller-thema100.html), on Abraham Lincoln see [‘The Physical Lincoln](http://www.physical-lincoln.com/books.html)’. Other famous victims – although it is not always possible to diagnose the disease of historical figures – were German painter Albrecht Dürer, who contracted the disease in the Netherlands, and several popes, who died of the disease as malaria was very prevalent in Italy until recently. See ""Box 4.1 Malaria-related mortality in the 20th century"" in the World Health Organization's [World Health Report (1999)](https://www.who.int/whr/1999/en/whr99_en.pdf). The original map was published in Simon I Hay, Carlos A Guerra, Andrew J Tatem, Abdisalan M Noor, and Robert W Snow (2004) – The global distribution and population at risk of malaria: past, present, and future. In The Lancet Infectious Diseases 2004 June; 4(6): 327–336. DOI: 10.1016/S1473-3099(04)01043-6. [https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(04)01043-6/fulltext](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(04)01043-6/fulltext?version=printerFriendly) I have digitized the Figure 1 using image tracing in Adobe Illustrator. The historical mapping of the prevalence of malaria is based on the pioneering work of Lysenko in the 1960s: Lysenko AJ, Semashko IN. Geography of malaria (1968) – A medico-geographic profile of an ancient disease. In: Lebedew AW, editor. Itogi Nauki: Medicinskaja Geografija. Academy of Sciences, USSR; Moscow: 1968. pp. 25–146. Lysenko AJ, Beljaev AE (1969) – An analysis of the geographical distribution of Plasmodium ovale. Bull World Health Organization; 40:383–94. See Hay et al (2004) above and de Zulueta J. (1994) – Malaria and ecosystems: from prehistory to posteradication. In Parassitologia. 1994 August. 36(1-2):7-15. World Bank (2009) – World Development Report (2009) - Part I: Reshaping Economic Geography. Washington, DC: World Bank. Online [here](http://documents.worldbank.org/curated/en/730971468139804495/pdf/437380REVISED01BLIC1097808213760720.pdf).",Malaria was common across half the world – since then it has been eliminated in many regions 1tS43kJ8ckyvaSEOqBBf0WtUtJ3X7T_Yh8J66HQQZS80,cardiovascular-diseases,topic-page,"{""toc"": [], ""body"": [{""type"": ""topic-page-intro"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Cardiovascular diseases cover all diseases of the heart and blood vessels – including "", ""spanType"": ""span-simple-text""}, {""id"": ""heart-attack"", ""children"": [{""text"": ""heart attacks"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""id"": ""stroke"", ""children"": [{""text"": ""strokes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""id"": ""atherosclerosis"", ""children"": [{""text"": ""atherosclerosis"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""id"": ""ischemic-heart-disease"", ""children"": [{""text"": ""ischemic heart disease"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""id"": ""hypertensive-heart-disease"", ""children"": [{""text"": ""hypertensive diseases"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""id"": ""cardiomyopathy"", ""children"": [{""text"": ""cardiomyopathy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", and others."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These diseases tend to develop gradually with age, especially when people have risk factors like high blood pressure, smoking, alcohol use, poor diet, and air pollution."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Together, cardiovascular diseases are the most common "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/causes-of-death"", ""children"": [{""text"": ""cause of death"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" globally."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 2000, around 14 million people died from cardiovascular diseases globally, while in 2019, close to 18 million died."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The rising death toll is largely due to a growing and aging global population. 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This represents a reduction of almost three-quarters."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The dramatic "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/smoking-big-problem-in-brief"", ""children"": [{""text"": ""decline in smoking"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" has played a significant role in this reduction."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" But during the 20th and 21st centuries, we have also achieved major advances in screening, diagnosis and monitoring, and developed public health initiatives, emergency care, medical treatment, devices, and surgeries, that have helped reduce the consequences of cardiovascular diseases."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": [{""text"": ""What you should know about this data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 5, ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""This data comes from the World Health Organization (WHO)’s Mortality Database, which compiles deaths reported by each country annually, based on the "", ""spanType"": ""span-simple-text""}, {""id"": ""underlying-cause-of-death"", ""children"": [{""text"": ""underlying cause of death"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" listed on death certificates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Countries are only included in the WHO Mortality Database if at least 65% of the deaths that occurred in a given year were registered in "", ""spanType"": ""span-simple-text""}, {""id"": ""crvs"", ""children"": [{""text"": ""vital registration systems"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" and reported to the WHO."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The data has been "", ""spanType"": ""span-simple-text""}, {""id"": ""age_standardized"", ""children"": [{""text"": ""age-standardized"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", which allows for comparisons between different countries and over time, where populations have different age structures. 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F., Künzli, N., Mills, N., Pekkanen, J., Peters, A., Piepoli, M. F., Rajagopalan, S., & Storey, R. F. (2015). Expert position paper on air pollution and cardiovascular disease. European Heart Journal, 36(2), 83–93. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1093/eurheartj/ehu458"", ""children"": [{""text"": ""https://doi.org/10.1093/eurheartj/ehu458"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9d48876da03c3d58465a8ff21ef79cbd0cf0af15"": {""id"": ""9d48876da03c3d58465a8ff21ef79cbd0cf0af15"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Mensah, G. A., Wei, G. S., Sorlie, P. D., Fine, L. J., Rosenberg, Y., Kaufmann, P. G., Mussolino, M. E., Hsu, L. L., Addou, E., Engelgau, M. M., & Gordon, D. (2017). Decline in Cardiovascular Mortality: Possible Causes and Implications. Circulation Research, 120(2), 366–380. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1161/CIRCRESAHA.116.309115"", ""children"": [{""text"": ""https://doi.org/10.1161/CIRCRESAHA.116.309115"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Weisfeldt, M. L., & Zieman, S. J. (2007). Advances In The Prevention And Treatment Of Cardiovascular Disease. Health Affairs, 26(1), 25–37. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1377/hlthaff.26.1.25"", ""children"": [{""text"": ""https://doi.org/10.1377/hlthaff.26.1.25"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Gao, Y., Shah, L. M., Ding, J., & Martin, S. S. (2023). US Trends in Cholesterol Screening, Lipid Levels, and Lipid‐Lowering Medication Use in US Adults, 1999 to 2018. Journal of the American Heart Association, 12(3), e028205. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1161/JAHA.122.028205"", ""children"": [{""text"": ""https://doi.org/10.1161/JAHA.122.028205"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Jagannathan, R., Patel, S. A., Ali, M. K., & Narayan, K. M. V. (2019). Global Updates on Cardiovascular Disease Mortality Trends and Attribution of Traditional Risk Factors. Current Diabetes Reports, 19(7), 44. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1007/s11892-019-1161-2"", ""children"": [{""text"": ""https://doi.org/10.1007/s11892-019-1161-2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Joseph, P., Leong, D., McKee, M., Anand, S. S., Schwalm, J.-D., Teo, K., Mente, A., & Yusuf, S. (2017). Reducing the Global Burden of Cardiovascular Disease, Part 1: The Epidemiology and Risk Factors. Circulation Research, 121(6), 677–694. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1161/CIRCRESAHA.117.308903"", ""children"": [{""text"": ""https://doi.org/10.1161/CIRCRESAHA.117.308903"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Roth, G. A., Forouzanfar, M. H., Moran, A. E., Barber, R., Nguyen, G., Feigin, V. L., Naghavi, M., Mensah, G. A., & Murray, C. J. L. (2015). Demographic and Epidemiologic Drivers of Global Cardiovascular Mortality. New England Journal of Medicine, 372(14), 1333–1341. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1056/NEJMoa1406656"", ""children"": [{""text"": ""https://doi.org/10.1056/NEJMoa1406656"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""topic-page"", ""title"": ""Cardiovascular Diseases"", ""authors"": [""Saloni Dattani"", ""Veronika Samborska"", ""Hannah Ritchie"", ""Max Roser""], ""excerpt"": ""Cardiovascular diseases are the most common cause of death worldwide. Explore global data on cardiovascular diseases, their treatments, risk factors, and trends over time."", ""dateline"": ""December 14, 2023"", ""subtitle"": """", ""atom-title"": ""Cardiovascular diseases are the most common cause of death worldwide"", ""sticky-nav"": [{""text"": ""Introduction"", ""target"": ""#introduction""}, {""text"": ""Key Insights"", ""target"": ""#key-insights""}, {""text"": ""Research & Writing"", ""target"": ""#research-writing""}, {""text"": ""Charts"", ""target"": ""#all-charts""}, {""text"": ""Endnotes"", ""target"": ""#article-endnotes""}, {""text"": ""Cite This Work"", ""target"": ""#article-citation""}, {""text"": ""Reuse This Work"", ""target"": ""#article-licence""}], ""atom-excerpt"": ""Explore global data on cardiovascular diseases, their treatments, risk factors, and trends over time."", ""featured-image"": ""cardiovascular-diseases-thumbnail.png""}",1,2023-10-31 12:18:56,2023-12-14 12:56:52,2023-12-28 16:31:12,listed,ALBJ4LvrhdaKRsJaQl6iv3pzcB4sU0AWNT9iqhWHQDvfI3ZjvLo5TyLUXFiDHeLLfO8RC435cLKWu5jpx5ofSg,,"Cardiovascular diseases cover all diseases of the heart and blood vessels – including heart attacks and strokes, atherosclerosis, ischemic heart disease, hypertensive diseases, cardiomyopathy, and others. These diseases tend to develop gradually with age, especially when people have risk factors like high blood pressure, smoking, alcohol use, poor diet, and air pollution. Together, cardiovascular diseases are the most common [cause of death](https://ourworldindata.org/causes-of-death) globally. In 2000, around 14 million people died from cardiovascular diseases globally, while in 2019, close to 18 million died. The rising death toll is largely due to a growing and aging global population. Death _rates_ from cardiovascular diseases have actually fallen in many countries – as our ability to prevent and treat them has improved. Large declines in [smoking](https://ourworldindata.org/smoking); improvements in screening, diagnosis, and monitoring; and advances in medical treatments, public health initiatives, emergency care, and surgical procedures, have all helped to reduce the impact of cardiovascular diseases on people’s lives. Yet large disparities remain globally. The impact of cardiovascular diseases can be reduced much further with greater understanding and public health efforts. On this page, you will find global data on cardiovascular diseases, their risk factors and treatments, and their trends over time. ## Key Insights on Cardiovascular Diseases ### Cardiovascular diseases are the most common cause of death worldwide This chart shows what people died from globally in 2019. Each box represents a cause of death, and the size of each box is proportional to the number of deaths it caused. As you can see, heart diseases and other cardiovascular diseases are the most common causes of death, responsible for a third of all deaths globally, a total of around 18 million. Cardiovascular diseases are part of a larger group of diseases called non-communicable diseases, which are shown in blue in the visualization. These are diseases that tend to develop gradually over time and can’t be passed on to other people,1 and also includes cancers, chronic respiratory diseases, and other chronic diseases. Taken together, these non-communicable diseases are the cause of around three-quarters of all deaths globally. In this article, we cover this in more detail: ### undefined undefined https://docs.google.com/document/d/1UdKf375jChVw4SpFrhNaRJH0Ve7VDXtjFz_CP7mon54/edit ![](causes-of-death-2019-full.png) ### The global death toll from cardiovascular diseases has grown The total number of deaths from cardiovascular diseases has risen globally. In 2000, around 14 million people died from cardiovascular diseases globally, while in 2019, that figure was almost 18 million. This rise is partly due to a [growing](https://ourworldindata.org/population-growth) and [aging population](https://ourworldindata.org/age-structure), especially in Asia, as well as increases in risk factors such as obesity and diabetes.2 As some of these trends will continue in the coming decades – particularly with the [increasing number of elderly people](https://ourworldindata.org/explorers/population-and-demography?country=~OWID_WRL&Metric=Population+by+broad+age+group&Sex=Both+sexes&Age+group=Under+15+years&Projection+Scenario=Medium) across the world – cardiovascular diseases are likely to become a larger burden on healthcare systems. ### Death rates from cardiovascular diseases have declined in many countries The _number_ of deaths from cardiovascular diseases is increasing, but — as this chart shows — the death _rate_ has declined in many countries. This means that the risk of death from cardiovascular diseases now is lower than in the past among populations of the same size and age. In many countries, the decline in death rates has been large, as you can see in the chart. In the United States, for example, the age-standardized death rate from cardiovascular diseases was over 500 per 100,000 people in 1950 and declined to less than 150 in 2020. This represents a reduction of almost three-quarters. The dramatic [decline in smoking](https://ourworldindata.org/smoking-big-problem-in-brief) has played a significant role in this reduction.3 But during the 20th and 21st centuries, we have also achieved major advances in screening, diagnosis and monitoring, and developed public health initiatives, emergency care, medical treatment, devices, and surgeries, that have helped reduce the consequences of cardiovascular diseases.4 ### There are large disparities in death rates from cardiovascular diseases worldwide Death rates from cardiovascular diseases vary widely between countries. As shown in the map, they tend to be higher in Africa, Asia, Eastern Europe, and South America than in North America and Western Europe. In France, Australia, and Canada, the estimated death rate was less than 80 per 100,000 people in 2019. While in Afghanistan, Sudan, and Mongolia, it was more than 500. These wide differences arise from differences in risk factors – such as [smoking](https://ourworldindata.org/smoking), [alcohol consumption](https://ourworldindata.org/alcohol-consumption), and [air pollution](https://ourworldindata.org/air-pollution) – as well as lower healthcare access and investment to screen and treat cardiovascular diseases, which can require long-term management.2 ### A range of factors heighten the risk of cardiovascular diseases Different types of risk factors increase the risk of cardiovascular diseases. This includes behaviors such as [smoking](https://ourworldindata.org/smoking), [alcohol use](https://ourworldindata.org/alcohol-consumption), and [diets](https://ourworldindata.org/diet-compositions) high in sugar and sodium; clinical risk factors, such as high blood pressure and high LDL cholesterol; and environmental risks, such as [air pollution](https://ourworldindata.org/air-pollution) and [lead exposure](https://ourworldindata.org/lead-pollution).5 The chart shows the estimated number of deaths from cardiovascular diseases attributed to each risk factor. High blood pressure, or hypertension, is the number one risk factor. It’s estimated that it causes almost 10 million deaths from cardiovascular diseases annually.6 ## Related research and writing * https://ourworldindata.org/causes-of-death-treemap ,* https://ourworldindata.org/cardiovascular-diseases-types-and-death-tolls ,* https://ourworldindata.org/how-are-causes-of-death-registered-around-the-world ,* https://ourworldindata.org/how-do-researchers-estimate-the-death-toll-caused-by-each-risk-factor-whether-its-smoking-obesity-or-air-pollution ,* https://ourworldindata.org/smoking-big-problem-in-brief Jagannathan, R., Patel, S. A., Ali, M. K., & Narayan, K. M. V. (2019). Global Updates on Cardiovascular Disease Mortality Trends and Attribution of Traditional Risk Factors. Current Diabetes Reports, 19(7), 44. [https://doi.org/10.1007/s11892-019-1161-2](https://doi.org/10.1007/s11892-019-1161-2) Joseph, P., Leong, D., McKee, M., Anand, S. S., Schwalm, J.-D., Teo, K., Mente, A., & Yusuf, S. (2017). Reducing the Global Burden of Cardiovascular Disease, Part 1: The Epidemiology and Risk Factors. Circulation Research, 121(6), 677–694. [https://doi.org/10.1161/CIRCRESAHA.117.308903](https://doi.org/10.1161/CIRCRESAHA.117.308903) Roth, G. A., Forouzanfar, M. H., Moran, A. E., Barber, R., Nguyen, G., Feigin, V. L., Naghavi, M., Mensah, G. A., & Murray, C. J. L. (2015). Demographic and Epidemiologic Drivers of Global Cardiovascular Mortality. New England Journal of Medicine, 372(14), 1333–1341. [https://doi.org/10.1056/NEJMoa1406656](https://doi.org/10.1056/NEJMoa1406656) In these related charts, you can see trends in crude death rates from different causes over the twentieth century in [France](https://ourworldindata.org/grapher/historical-death-rates-from-each-cause-category-in-france) and the [United States](https://ourworldindata.org/grapher/death-rates-through-the-20th-century). The decline in smoking has a much quicker effect in alleviating the risks of cardiovascular diseases than of lung cancers and other cancers. Oza, S., Thun, M. J., Henley, S. J., Lopez, A. D., & Ezzati, M. (2011). How many deaths are attributable to smoking in the United States? Comparison of methods for estimating smoking-attributable mortality when smoking prevalence changes. Preventive Medicine, 52(6), 428–433. [https://doi.org/10.1016/j.ypmed.2011.04.007](https://doi.org/10.1016/j.ypmed.2011.04.007) This death toll is a population-attributable number, which means it is an estimate of the number of deaths that would be prevented if hypertension was absent in the entire population. Although communicable and non-communicable diseases are shown separately, it is now understood that infectious diseases contribute to several non-communicable diseases. This includes Helicobacter pylori and stomach cancer, human papillomavirus and cervical cancer, hepatitis C virus, and liver cancer, Chlamydia pneumoniae and atherosclerosis, Streptococcus pneumoniae and chronic respiratory diseases, and others. In addition, infectious diseases can increase the risk of dying from non-communicable diseases. For example, several respiratory pathogens, such as the influenza virus, increase the risk of heart attacks and strokes. Mercer, A. J. (2018). Updating the epidemiological transition model. Epidemiology and Infection, 146(6), 680–687. [https://doi.org/10.1017/S0950268818000572](https://doi.org/10.1017/S0950268818000572) Behrouzi, B., Bhatt, D. L., Cannon, C. P., Vardeny, O., Lee, D. S., Solomon, S. D., & Udell, J. A. (2022). Association of Influenza Vaccination With Cardiovascular Risk: A Meta-analysis. JAMA Network Open, 5(4), e228873. [https://doi.org/10.1001/jamanetworkopen.2022.8873](https://doi.org/10.1001/jamanetworkopen.2022.8873) Bittner, V. (2020). The New 2019 AHA/ACC Guideline on the Primary Prevention of Cardiovascular Disease. Circulation, 142(25), 2402–2404. [https://doi.org/10.1161/CIRCULATIONAHA.119.040625](https://doi.org/10.1161/CIRCULATIONAHA.119.040625) Jagannathan, R., Patel, S. A., Ali, M. K., & Narayan, K. M. V. (2019). Global Updates on Cardiovascular Disease Mortality Trends and Attribution of Traditional Risk Factors. Current Diabetes Reports, 19(7), 44. [https://doi.org/10.1007/s11892-019-1161-2](https://doi.org/10.1007/s11892-019-1161-2) Kaptoge, S., Pennells, L., De Bacquer, D., Cooney, M. T., Kavousi, M., Stevens, G., Riley, L. M., Savin, S., Khan, T., Altay, S., Amouyel, P., Assmann, G., Bell, S., Ben-Shlomo, Y., Berkman, L., Beulens, J. W., Björkelund, C., Blaha, M., Blazer, D. G., … Di Angelantonio, E. (2019). World Health Organization cardiovascular disease risk charts: Revised models to estimate risk in 21 global regions. The Lancet Global Health, 7(10), e1332–e1345. [https://doi.org/10.1016/S2214-109X(19)30318-3](https://doi.org/10.1016/S2214-109X(19)30318-3) Cosselman, K. E., Navas-Acien, A., & Kaufman, J. D. (2015). Environmental factors in cardiovascular disease. Nature Reviews Cardiology, 12(11), 627–642. [https://doi.org/10.1038/nrcardio.2015.152](https://doi.org/10.1038/nrcardio.2015.152) Newby, D. E., Mannucci, P. M., Tell, G. S., Baccarelli, A. A., Brook, R. D., Donaldson, K., Forastiere, F., Franchini, M., Franco, O. H., Graham, I., Hoek, G., Hoffmann, B., Hoylaerts, M. F., Künzli, N., Mills, N., Pekkanen, J., Peters, A., Piepoli, M. F., Rajagopalan, S., & Storey, R. F. (2015). Expert position paper on air pollution and cardiovascular disease. European Heart Journal, 36(2), 83–93. [https://doi.org/10.1093/eurheartj/ehu458](https://doi.org/10.1093/eurheartj/ehu458) Mensah, G. A., Wei, G. S., Sorlie, P. D., Fine, L. J., Rosenberg, Y., Kaufmann, P. G., Mussolino, M. E., Hsu, L. L., Addou, E., Engelgau, M. M., & Gordon, D. (2017). Decline in Cardiovascular Mortality: Possible Causes and Implications. Circulation Research, 120(2), 366–380. [https://doi.org/10.1161/CIRCRESAHA.116.309115](https://doi.org/10.1161/CIRCRESAHA.116.309115) Weisfeldt, M. L., & Zieman, S. J. (2007). Advances In The Prevention And Treatment Of Cardiovascular Disease. Health Affairs, 26(1), 25–37. [https://doi.org/10.1377/hlthaff.26.1.25](https://doi.org/10.1377/hlthaff.26.1.25) Gao, Y., Shah, L. M., Ding, J., & Martin, S. S. (2023). US Trends in Cholesterol Screening, Lipid Levels, and Lipid‐Lowering Medication Use in US Adults, 1999 to 2018. Journal of the American Heart Association, 12(3), e028205. [https://doi.org/10.1161/JAHA.122.028205](https://doi.org/10.1161/JAHA.122.028205) Jagannathan, R., Patel, S. A., Ali, M. K., & Narayan, K. M. V. (2019). Global Updates on Cardiovascular Disease Mortality Trends and Attribution of Traditional Risk Factors. Current Diabetes Reports, 19(7), 44. [https://doi.org/10.1007/s11892-019-1161-2](https://doi.org/10.1007/s11892-019-1161-2) Joseph, P., Leong, D., McKee, M., Anand, S. S., Schwalm, J.-D., Teo, K., Mente, A., & Yusuf, S. (2017). Reducing the Global Burden of Cardiovascular Disease, Part 1: The Epidemiology and Risk Factors. Circulation Research, 121(6), 677–694. [https://doi.org/10.1161/CIRCRESAHA.117.308903](https://doi.org/10.1161/CIRCRESAHA.117.308903) Roth, G. A., Forouzanfar, M. H., Moran, A. E., Barber, R., Nguyen, G., Feigin, V. L., Naghavi, M., Mensah, G. A., & Murray, C. J. L. (2015). Demographic and Epidemiologic Drivers of Global Cardiovascular Mortality. New England Journal of Medicine, 372(14), 1333–1341. [https://doi.org/10.1056/NEJMoa1406656](https://doi.org/10.1056/NEJMoa1406656)",Cardiovascular Diseases 1tCgkmXQk8cjdqzgQVs1-Tkj4zKdQ1526xR9nn7TuiMA,poverty-growth-needed,article,"{""toc"": [{""slug"": ""follow-up-post"", ""text"": ""Follow-up post:"", ""title"": ""Follow-up post:"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""The huge majority of the world today is very poor. About 85% of the world live on less than $30 per day and around 61% live on less than $10 per day."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" I believe, for reasons I’ll explain below, that if this should change it will require very substantial economic growth of the economies that are home to the poorest billions of people in the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The reason I wrote this text is that I believe some commentators on global poverty are not clear about the reality that very substantial growth is needed if people in poor countries should have a chance to leave poverty behind. I believe that if we do not express very clearly that economic growth is needed, we are damaging the prospects of the poorest people in the world to leave poverty behind."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I am therefore asking that everyone who finds global poverty unacceptable should be very clear that the majority of people in the world live in poor economies and that massive economic growth is needed to increase their incomes to a decent level."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Higher incomes are not an end in themselves. But to say that income growth only has an instrumental role is not to say that it is of little importance. A person's income does not measure their well-being; it measures whether goods and services they value remain out of reach or not. Because many of these goods and services matter for their wellbeing, income matters too."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Any person’s income depends on two factors, the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""average income"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" in the country they live in and the position that particular person has in that country’s "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""income distribution"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Both aspects can change so that fewer people are poor:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""average income can "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/gdp-per-capita-maddison-2020?tab=chart&yScale=linear&stackMode=absolute&country=Western%20Europe~East%20Asia~South%20and%20South-East%20Asia~Middle%20East~Eastern%20Europe~Latin%20America~Sub-Sahara%20Africa~USA~GBR~AUS®ion=World"", ""children"": [{""text"": ""increase"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" over time; that is called economic growth"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""and inequality can decline so that the poorest people get closer to the average income in that country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For anyone who is concerned about poverty, it is important to consider how much poverty can decline by either economic growth or lower inequality. Let’s look at each factor."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Reducing inequality within countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One possibility to reduce poverty is to redistribute income within that country so that the income of the poor rises. There are a number of ways this can be done: one way is that the government taxes the incomes of richer people and pays it out to the poor."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The largest poverty reduction that is possible via a reduction in inequality would be achieved by a country that achieves perfect equality so that no one is poorer than anyone else."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To understand how much the incomes of the poorest people can possibly increase via reduced inequality, it is therefore important to see data on average incomes across countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are two ways of measuring average income in a country:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""You can either start at the individual level and do a survey in which you ask people what their consumption or income is – this is plotted on the vertical axis in the chart. Households’ consumption is not the same thing as their income – but they are closely related; this is especially true of poor people who do not have the chance to save much."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""or you can start on the macro level and divide the size of the entire economy by the number of people in that country, and you end up with GDP per capita, plotted on the horizontal axis."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The two income concepts differ: GDP per capita is typically higher – it is a more comprehensive measure of income and includes, for example, government expenditure and also the imputed rental value of owner-occupied housing."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Angus Deaton (2005) gives a helpful overview of these differences."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Because they differ and because both are relevant data points for understanding a country’s average income, I’m showing both income measures so that you can study how much it matters to use one or the other."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/mean-daily-expenditure-per-capita-vs-gdp-per-capita-2011-ppp"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All measures in this chart are given in international-$, which means that it is adjusted for the price differences between countries. This adjustment is done in a way such that one international-dollar is equivalent to the purchasing power of one US-$ "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""in the US"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". The $13.13 average income of people in Peru, for example, means that the average Peruvian can purchase goods and services that would cost $13.13 in the US."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Many poorer people rely on subsistence farming and do not have a monetary income. To take this into account and make a fair comparison of their living standards, the statisticians that produce these figures estimate the monetary value of their home production and add it to their income/consumption. That is true of both measures shown in the chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As you can see, the majority of people in the world live in countries that are very poor. Even perfect equality in those countries would mean that billions of people around the world would live on extremely low incomes: $15 a day, $10 a day, even less than $5 a day."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When the average of people’s income in a country is that low, then the only way the majority of people can possibly leave poverty behind is when that country’s economy grows so that average incomes increase."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Reducing global inequality"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another possibility to reduce global poverty is to redistribute between countries. Money can be transferred from rich people in rich countries to poorer people in poor countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I am personally very much in favor of that:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""I donate 10% of my income to efforts that support people in poorer countries and have been doing this for many years (I donate via "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.givewell.org/"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://app.effectivealtruism.org/"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "")."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""More directly, I have given to poor people via "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.givedirectly.org/"", ""children"": [{""text"": ""GiveDirectly"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and believe that this a very good way to spend your money."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I am also in favor of increased spending on aid (if you are skeptical, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://twitter.com/s8mb/status/1331190082049601536"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" is a good thread by an aid skeptic about why it is good to spend on aid)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But I have two problems with the idea that we should reduce global poverty by global redistribution:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I think it is extremely optimistic to believe that large-scale global redistribution would be supported by those who live on more than the global average income."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" You would need to convince (or force) the richest hundreds of million people in the world to give up large shares of their income and I think only few people would be willing to do that. One concrete data point that makes me skeptical: most rich countries in the world "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/List_of_development_aid_country_donors"", ""children"": [{""text"": ""are not willing to achieve"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" the UN goal of spending even only 0.7% of their GDP on aid."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If it is hard to find political support for the goal of spending 0.7% of people’s incomes on aid, I very much do not believe that the majority of people who live on more than the average income would agree to give up much larger shares of their income."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are only two ways to increase the incomes of the poor: lower global inequality or economic growth for the poorest billions of the world. If someone is not in favor of economic growth for the poorer billions in the world, they are left with the option to reduce global inequality. I am in favor of reducing global inequality, but I find it extremely wrong to suggest that the only acceptable way to end global poverty is to reduce global inequality."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is wrong to make the chance of poor people leaving poverty behind conditional on an extremely optimistic scenario of the future of inequality."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Economic growth"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Economic growth in today’s rich countries over the last two hundred years is the reason that people in those countries are much less poor than people in the same places in the past or people in poor countries today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart on the top compares estimates of the income distribution in Madagascar (in green) and the UK (in blue) in 2018. The income differences between people in these two countries are extremely large. The current average income in Madagascar today is $1.50 per day."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This means that even the richer half of Madagascar’s population live on incomes between only $2 to $5 per day, much less than even poor people in the UK."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart at the bottom shows that back in 1800 the incomes in the UK were similarly low as Madagascar today. Since then the UK economy grew "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/englands-economy-over-the-long-run-gdp-vs-population?time=1800..latest"", ""children"": [{""text"": ""80-fold"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and the income distribution moved to the right so that the majority of British people left the poverty of the past behind."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Incomes-poor-and-rich-country-1800-and-2018.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Average incomes in Madagascar did not grow – GDP per capita is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/gdp-per-capita-maddison-2020?tab=chart&yScale=linear&stackMode=absolute&country=~MDG®ion=World"", ""children"": [{""text"": ""not higher"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" than three generations ago – and poverty, therefore, remains "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ophi.org.uk/wp-content/uploads/Madagascar1.pdf"", ""children"": [{""text"": ""extremely severe"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in Madagascar."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This needs to change if poor countries are to leave poverty behind. People in Madagascar only have a chance to leave poverty behind if their average incomes grow the way they did in the UK."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Economic growth is not enough to get people out of poverty. If the inequality of incomes increases, the poorest can be left behind. Fighting inequality matters too. But "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""without"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" economic growth, there is "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""no chance at all"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" to leave poverty behind. To make it possible that poverty can decline in Madagascar the average income must increase. Without economic growth there is no chance that the people in Madagascar – and other poor countries – can possibly leave poverty behind."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The income differences between rich and poor countries today are vast, as we have just seen. The country where a person lives explains "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""two-thirds"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of the variation of income differences between all people in the world – this is what inequality researcher Branko Milanovic "", ""spanType"": ""span-simple-text""}, {""url"": ""https://voxeu.org/article/income-inequality-and-citizenship"", ""children"": [{""text"": ""documents"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Where a person lives is more important for how poor or rich they will be than everything else put together."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Understanding how much the size of the economy matters for our own income is important for our own self-understanding and for our judgement of why it is that some people are poor and others are not. A person’s knowledge, skills, and how hard they work all matter for whether they are poor or not – but all these personal factors together matter much less than the factor that is entirely outside a person’s control: whether the place they happen to be born into has a large, productive economy or not."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Both the history of economic growth and the differences across the global income distribution today make this very clear: people are not poor because of who they are, poor people are poor because of the economy they happen to live in."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is true over time: The fact that a particular person in the Middle Ages was poor was not his or her failure, it was due to the fact that "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/breaking-the-malthusian-trap"", ""children"": [{""text"": ""almost all were very poor"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". They happened to be born at a time when the economy was not very productive and living standards were much lower than today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And it is true for places across the world today: The poorest billions of people live in very poor economies and as a consequence are very poor."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Conclusion"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Lowering inequality is an important goal, and it can help to reduce poverty, but it can not be the only way in which the world fights global poverty. The poorest people live in places where average incomes are very low, as we have seen in the chart above. I am in favor of lower inequality, but I also believe that anyone who is concerned about global poverty should be in favor of strong economic growth in the economies that are home to the poorest billions in the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For me personally, my income is not a major limiting factor to my freedom or well-being, and I am not concerned here with whether rich countries today should make it their goal to grow their economies. But I am concerned about the very low incomes of the majority of the world and believe that those economies that they are part of need to grow very substantially if there should be a chance that the poorest billions can leave poverty behind in the decades to come."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One important reason why the people in some places are poor is because they were "", ""spanType"": ""span-simple-text""}, {""url"": ""https://voxeu.org/article/economic-impact-colonialism"", ""children"": [{""text"": ""exploited by colonial powers"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that did not allow those economies to grow and instead impoverished them. The injustice of an extremely unequal world needs to end. While some places in the world have left the worst poverty "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/breaking-the-malthusian-trap"", ""children"": [{""text"": ""behind"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", the huge majority of the world still lives in countries where the average income is extremely low. Increasing average incomes "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""is"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" economic growth."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""That is why I think that everyone who is also concerned about global poverty should be clear and say without any hesitation that they are in favor of economic growth for the poorest billions in the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""Follow-up post:"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/poverty-minimum-growth-needed"", ""type"": ""prominent-link"", ""title"": ""How much economic growth is necessary to reduce global poverty substantially?"", ""thumbnail"": ""poverty-minimum-growth-needed-featured-image.png"", ""description"": ""In a follow up post I made the statements above more concrete and looked at the depth of global poverty today to get a quantitative sense of just how much the global income distribution would need to change to reduce global poverty substantially"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""30eed6c49c844ae08bcafa48bb053857e8cce9cf"": {""id"": ""30eed6c49c844ae08bcafa48bb053857e8cce9cf"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""According to World Bank Poverty and Inequality data – which you can "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/poverty-explorer-2011-vs-2017-ppp?tab=chart&facet=none&country=MDG~GBR&Indicator=Mean+income+or+consumption&International-%24=2011+prices&Poverty+line=%2410+per+day&Household+survey+data+type=Show+data+from+both+income+and+consumption+surveys"", ""children"": [{""text"": ""explore here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""45799df20a456e7876ca45e7c08edc693be30be7"": {""id"": ""45799df20a456e7876ca45e7c08edc693be30be7"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""According to World Bank Poverty and Inequality data – which you can "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/poverty-explorer-2011-vs-2017-ppp?tab=chart&facet=none&Metric=Share+in+poverty&International-%24=2011+prices&Poverty+line=%2430+per+day&Household+survey+data+type=Show+data+from+both+income+and+expenditure+surveys&country=~OWID_WRL"", ""children"": [{""text"": ""explore here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7ec578738d38461b035976c394836f258df50f18"": {""id"": ""7ec578738d38461b035976c394836f258df50f18"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""To get a global view of poverty we need to combine data from both income and consumption surveys since not all countries collect data on both."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""81c96b197accd4cd14d08125b450774cc61b30a2"": {""id"": ""81c96b197accd4cd14d08125b450774cc61b30a2"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Deaton, Angus. 2005. “Measuring Poverty in a Growing World (or Measuring Growth in a Poor World).” The Review of Economics and Statistics 87 (1): 1–19."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d95c9d32e26f8c1d4837571b6091a828dde3e8b9"": {""id"": ""d95c9d32e26f8c1d4837571b6091a828dde3e8b9"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Measured across the whole economy income must always equal expenditure, since savings, by definition, equals investment expenditure. But this is not true if we look only at households – it is mostly households saving and mostly firms and government making expenditure on investment goods."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There is generally a gap between GDP per capita and the averages found in both income and consumption surveys. But the reasons for the gap are different depending on which we are comparing."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""GDP includes many items that are typically not measured in household income surveys, such as an imputed rental value of owner-occupied housing, the retained earnings of firms and taxes on production such as VAT. The gap is even larger when GDP is compared to surveys of household consumption – the latter concept excluding both investment expenditure and government expenditure on public services such as education and health."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Other aggregates beyond GDP are available in the national accounts that are more comparable to the concepts applied in household income and consumption surveys. However, important differences still remain even here. For example, in addition to imputed rents, imputations for the value of certain financial services, such as bank accounts, are included in aggregate household consumption measured in national accounts, with no equivalent for these items recorded in the survey data. In many countries, the consumption of nonprofit institutions serving households (NPISH) is included as part of household consumption within national accounts, but not within household surveys."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On top of these conceptual differences are a range of mismeasurement problems that affect both sets of data. On this topic see Deaton (2005), and Pinkovskiy and Sala-i-Martin (2016)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Deaton, Angus. 2005. “Measuring Poverty in a Growing World (or Measuring Growth in a Poor World).” The Review of Economics and Statistics 87 (1): 1–1. Available online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.princeton.edu/deaton/publications/measuring-poverty-growing-world-or-measuring-growth-poor-world"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Pinkovskiy, Maxim, and Sala-i-Martin, Xavier. 2016. “Lights, Camera… Income! Illuminating the National Accounts-Household Surveys Debate.” The Quarterly Journal of Economics 131 (2): 579–631. Available online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.nber.org/papers/w19831"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f6f21f6f097270dc877a24fffa2a7c6546a44302"": {""id"": ""f6f21f6f097270dc877a24fffa2a7c6546a44302"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Again, where the global average income is depends on your definition of income:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""If you rely on household surveys, then the average income in the world is around int.-$16 per day ("", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/poverty-explorer-2011-vs-2017-ppp?tab=chart&facet=none&country=~OWID_WRL&Indicator=Mean+income+or+consumption&International-%24=2011+prices&Poverty+line=%2410+per+day&Household+survey+data+type=Show+data+from+both+income+and+consumption+surveys"", ""children"": [{""text"": ""same source"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" as above)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you find GDP per capita more relevant, then you find that it is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD"", ""children"": [{""text"": ""int.-$17,000"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", int.-$46.5 per day (but keep in mind that you then cannot compare it with people’s household income)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you are relying on the World Inequality Lab estimate for the global average income you find that it is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/incomes-across-distribution-wid?tab=chart&facet=none&country=~OWID_WRL&Indicator=Mean+income&Decile%2Fquantile=All+deciles&Income+measure=Before+tax&Period=Year"", ""children"": [{""text"": ""int.-$25,463"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", int.-$70 per day (again this cannot be compared with estimates from the other sources)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""The economies that are home to the poorest billions of people need to grow if we want global poverty to decline substantially"", ""authors"": [""Max Roser""], ""excerpt"": ""The majority of the world today is poor: 85% of the world live on less than $30 per day. We need economic growth to alleviate global poverty."", ""dateline"": ""February 22, 2021"", ""subtitle"": ""The majority of the world today is poor: 85% of the world live on less than $30 per day. If we are to alleviate global poverty, we need economic growth."", ""gray-section"": [{""type"": ""heading"", ""value"": {""text"": ""Note: The World Bank has updated its poverty and inequality data"", ""level"": ""4""}}, {""type"": ""text"", ""value"": ""The data in this article uses a previous release of the World Bank's poverty and inequality data in which incomes are expressed in 2011 international-$.""}, {""type"": ""text"", ""value"": ""The World Bank has since updated its methods, and now measures incomes in 2017 international-$. As part of this change, the International Poverty Line used to measure extreme poverty has also been updated: from $1.90 (in 2011 prices) to $2.15 (in 2017 prices).""}, {""type"": ""text"", ""value"": ""This has had little effect on our overall understanding of poverty and inequality around the world. But because of the change of units, many of the figures mentioned in this article will differ from the latest World Bank figures.""}, {""type"": ""text"", ""value"": ""Read more about the World Bank's updated methodology:""}, {""type"": ""list"", ""value"": [""From $1.90 to $2.15 a day: the updated International Poverty Line"", ""Explore the latest World Bank data on poverty and inequality""]}], ""featured-image"": ""poverty-growth-needed.png"", ""horizontal-rule"": {}}",1,2023-07-18 09:59:29,2021-02-22 20:31:54,2024-02-02 18:50:15,listed,ALBJ4LsO_yaF5CCX8kxViNQyUCxlDE2ZA9kSmPwMyPk2uyaeavlSmDNmnkN_TG5bYbKMEA6fteUG6osb8eZdjQ,,"The huge majority of the world today is very poor. About 85% of the world live on less than $30 per day and around 61% live on less than $10 per day.1 I believe, for reasons I’ll explain below, that if this should change it will require very substantial economic growth of the economies that are home to the poorest billions of people in the world. The reason I wrote this text is that I believe some commentators on global poverty are not clear about the reality that very substantial growth is needed if people in poor countries should have a chance to leave poverty behind. I believe that if we do not express very clearly that economic growth is needed, we are damaging the prospects of the poorest people in the world to leave poverty behind. I am therefore asking that everyone who finds global poverty unacceptable should be very clear that the majority of people in the world live in poor economies and that massive economic growth is needed to increase their incomes to a decent level. Higher incomes are not an end in themselves. But to say that income growth only has an instrumental role is not to say that it is of little importance. A person's income does not measure their well-being; it measures whether goods and services they value remain out of reach or not. Because many of these goods and services matter for their wellbeing, income matters too. Any person’s income depends on two factors, the _average income_ in the country they live in and the position that particular person has in that country’s _income distribution_. Both aspects can change so that fewer people are poor: * average income can [increase](https://ourworldindata.org/grapher/gdp-per-capita-maddison-2020?tab=chart&yScale=linear&stackMode=absolute&country=Western%20Europe~East%20Asia~South%20and%20South-East%20Asia~Middle%20East~Eastern%20Europe~Latin%20America~Sub-Sahara%20Africa~USA~GBR~AUS®ion=World) over time; that is called economic growth * and inequality can decline so that the poorest people get closer to the average income in that country. For anyone who is concerned about poverty, it is important to consider how much poverty can decline by either economic growth or lower inequality. Let’s look at each factor. # Reducing inequality within countries One possibility to reduce poverty is to redistribute income within that country so that the income of the poor rises. There are a number of ways this can be done: one way is that the government taxes the incomes of richer people and pays it out to the poor. The largest poverty reduction that is possible via a reduction in inequality would be achieved by a country that achieves perfect equality so that no one is poorer than anyone else. To understand how much the incomes of the poorest people can possibly increase via reduced inequality, it is therefore important to see data on average incomes across countries. There are two ways of measuring average income in a country: * You can either start at the individual level and do a survey in which you ask people what their consumption or income is – this is plotted on the vertical axis in the chart. Households’ consumption is not the same thing as their income – but they are closely related; this is especially true of poor people who do not have the chance to save much.2 * or you can start on the macro level and divide the size of the entire economy by the number of people in that country, and you end up with GDP per capita, plotted on the horizontal axis. The two income concepts differ: GDP per capita is typically higher – it is a more comprehensive measure of income and includes, for example, government expenditure and also the imputed rental value of owner-occupied housing.3 Angus Deaton (2005) gives a helpful overview of these differences.4 Because they differ and because both are relevant data points for understanding a country’s average income, I’m showing both income measures so that you can study how much it matters to use one or the other. All measures in this chart are given in international-$, which means that it is adjusted for the price differences between countries. This adjustment is done in a way such that one international-dollar is equivalent to the purchasing power of one US-$ _in the US_. The $13.13 average income of people in Peru, for example, means that the average Peruvian can purchase goods and services that would cost $13.13 in the US. Many poorer people rely on subsistence farming and do not have a monetary income. To take this into account and make a fair comparison of their living standards, the statisticians that produce these figures estimate the monetary value of their home production and add it to their income/consumption. That is true of both measures shown in the chart. As you can see, the majority of people in the world live in countries that are very poor. Even perfect equality in those countries would mean that billions of people around the world would live on extremely low incomes: $15 a day, $10 a day, even less than $5 a day. When the average of people’s income in a country is that low, then the only way the majority of people can possibly leave poverty behind is when that country’s economy grows so that average incomes increase. # Reducing global inequality Another possibility to reduce global poverty is to redistribute between countries. Money can be transferred from rich people in rich countries to poorer people in poor countries. I am personally very much in favor of that: * I donate 10% of my income to efforts that support people in poorer countries and have been doing this for many years (I donate via [here](https://www.givewell.org/) and [here](https://app.effectivealtruism.org/)). * More directly, I have given to poor people via [GiveDirectly](https://www.givedirectly.org/) and believe that this a very good way to spend your money. * I am also in favor of increased spending on aid (if you are skeptical, [here](https://twitter.com/s8mb/status/1331190082049601536) is a good thread by an aid skeptic about why it is good to spend on aid). But I have two problems with the idea that we should reduce global poverty by global redistribution: I think it is extremely optimistic to believe that large-scale global redistribution would be supported by those who live on more than the global average income.5 You would need to convince (or force) the richest hundreds of million people in the world to give up large shares of their income and I think only few people would be willing to do that. One concrete data point that makes me skeptical: most rich countries in the world [are not willing to achieve](https://en.wikipedia.org/wiki/List_of_development_aid_country_donors) the UN goal of spending even only 0.7% of their GDP on aid. If it is hard to find political support for the goal of spending 0.7% of people’s incomes on aid, I very much do not believe that the majority of people who live on more than the average income would agree to give up much larger shares of their income. There are only two ways to increase the incomes of the poor: lower global inequality or economic growth for the poorest billions of the world. If someone is not in favor of economic growth for the poorer billions in the world, they are left with the option to reduce global inequality. I am in favor of reducing global inequality, but I find it extremely wrong to suggest that the only acceptable way to end global poverty is to reduce global inequality. It is wrong to make the chance of poor people leaving poverty behind conditional on an extremely optimistic scenario of the future of inequality. # Economic growth Economic growth in today’s rich countries over the last two hundred years is the reason that people in those countries are much less poor than people in the same places in the past or people in poor countries today. The chart on the top compares estimates of the income distribution in Madagascar (in green) and the UK (in blue) in 2018. The income differences between people in these two countries are extremely large. The current average income in Madagascar today is $1.50 per day.6 This means that even the richer half of Madagascar’s population live on incomes between only $2 to $5 per day, much less than even poor people in the UK. The chart at the bottom shows that back in 1800 the incomes in the UK were similarly low as Madagascar today. Since then the UK economy grew [80-fold](https://ourworldindata.org/grapher/englands-economy-over-the-long-run-gdp-vs-population?time=1800..latest) and the income distribution moved to the right so that the majority of British people left the poverty of the past behind. Average incomes in Madagascar did not grow – GDP per capita is [not higher](https://ourworldindata.org/grapher/gdp-per-capita-maddison-2020?tab=chart&yScale=linear&stackMode=absolute&country=~MDG®ion=World) than three generations ago – and poverty, therefore, remains [extremely severe](https://www.ophi.org.uk/wp-content/uploads/Madagascar1.pdf) in Madagascar. This needs to change if poor countries are to leave poverty behind. People in Madagascar only have a chance to leave poverty behind if their average incomes grow the way they did in the UK. Economic growth is not enough to get people out of poverty. If the inequality of incomes increases, the poorest can be left behind. Fighting inequality matters too. But _without_ economic growth, there is _no chance at all_ to leave poverty behind. To make it possible that poverty can decline in Madagascar the average income must increase. Without economic growth there is no chance that the people in Madagascar – and other poor countries – can possibly leave poverty behind. The income differences between rich and poor countries today are vast, as we have just seen. The country where a person lives explains _two-thirds_ of the variation of income differences between all people in the world – this is what inequality researcher Branko Milanovic [documents](https://voxeu.org/article/income-inequality-and-citizenship). Where a person lives is more important for how poor or rich they will be than everything else put together. Understanding how much the size of the economy matters for our own income is important for our own self-understanding and for our judgement of why it is that some people are poor and others are not. A person’s knowledge, skills, and how hard they work all matter for whether they are poor or not – but all these personal factors together matter much less than the factor that is entirely outside a person’s control: whether the place they happen to be born into has a large, productive economy or not. Both the history of economic growth and the differences across the global income distribution today make this very clear: people are not poor because of who they are, poor people are poor because of the economy they happen to live in. This is true over time: The fact that a particular person in the Middle Ages was poor was not his or her failure, it was due to the fact that [almost all were very poor](https://ourworldindata.org/breaking-the-malthusian-trap). They happened to be born at a time when the economy was not very productive and living standards were much lower than today. And it is true for places across the world today: The poorest billions of people live in very poor economies and as a consequence are very poor. # Conclusion Lowering inequality is an important goal, and it can help to reduce poverty, but it can not be the only way in which the world fights global poverty. The poorest people live in places where average incomes are very low, as we have seen in the chart above. I am in favor of lower inequality, but I also believe that anyone who is concerned about global poverty should be in favor of strong economic growth in the economies that are home to the poorest billions in the world. For me personally, my income is not a major limiting factor to my freedom or well-being, and I am not concerned here with whether rich countries today should make it their goal to grow their economies. But I am concerned about the very low incomes of the majority of the world and believe that those economies that they are part of need to grow very substantially if there should be a chance that the poorest billions can leave poverty behind in the decades to come. One important reason why the people in some places are poor is because they were [exploited by colonial powers](https://voxeu.org/article/economic-impact-colonialism) that did not allow those economies to grow and instead impoverished them. The injustice of an extremely unequal world needs to end. While some places in the world have left the worst poverty [behind](https://ourworldindata.org/breaking-the-malthusian-trap), the huge majority of the world still lives in countries where the average income is extremely low. Increasing average incomes _is_ economic growth. That is why I think that everyone who is also concerned about global poverty should be clear and say without any hesitation that they are in favor of economic growth for the poorest billions in the world. --- ## Follow-up post: ### How much economic growth is necessary to reduce global poverty substantially? In a follow up post I made the statements above more concrete and looked at the depth of global poverty today to get a quantitative sense of just how much the global income distribution would need to change to reduce global poverty substantially https://ourworldindata.org/poverty-minimum-growth-needed According to World Bank Poverty and Inequality data – which you can [explore here](https://ourworldindata.org/explorers/poverty-explorer-2011-vs-2017-ppp?tab=chart&facet=none&Metric=Share+in+poverty&International-%24=2011+prices&Poverty+line=%2430+per+day&Household+survey+data+type=Show+data+from+both+income+and+expenditure+surveys&country=~OWID_WRL). To get a global view of poverty we need to combine data from both income and consumption surveys since not all countries collect data on both. Measured across the whole economy income must always equal expenditure, since savings, by definition, equals investment expenditure. But this is not true if we look only at households – it is mostly households saving and mostly firms and government making expenditure on investment goods. There is generally a gap between GDP per capita and the averages found in both income and consumption surveys. But the reasons for the gap are different depending on which we are comparing. GDP includes many items that are typically not measured in household income surveys, such as an imputed rental value of owner-occupied housing, the retained earnings of firms and taxes on production such as VAT. The gap is even larger when GDP is compared to surveys of household consumption – the latter concept excluding both investment expenditure and government expenditure on public services such as education and health. Other aggregates beyond GDP are available in the national accounts that are more comparable to the concepts applied in household income and consumption surveys. However, important differences still remain even here. For example, in addition to imputed rents, imputations for the value of certain financial services, such as bank accounts, are included in aggregate household consumption measured in national accounts, with no equivalent for these items recorded in the survey data. In many countries, the consumption of nonprofit institutions serving households (NPISH) is included as part of household consumption within national accounts, but not within household surveys. On top of these conceptual differences are a range of mismeasurement problems that affect both sets of data. On this topic see Deaton (2005), and Pinkovskiy and Sala-i-Martin (2016). Deaton, Angus. 2005. “Measuring Poverty in a Growing World (or Measuring Growth in a Poor World).” The Review of Economics and Statistics 87 (1): 1–1. Available online [here](https://scholar.princeton.edu/deaton/publications/measuring-poverty-growing-world-or-measuring-growth-poor-world). Pinkovskiy, Maxim, and Sala-i-Martin, Xavier. 2016. “Lights, Camera… Income! Illuminating the National Accounts-Household Surveys Debate.” The Quarterly Journal of Economics 131 (2): 579–631. Available online [here](https://www.nber.org/papers/w19831). Deaton, Angus. 2005. “Measuring Poverty in a Growing World (or Measuring Growth in a Poor World).” The Review of Economics and Statistics 87 (1): 1–19. Again, where the global average income is depends on your definition of income: * If you rely on household surveys, then the average income in the world is around int.-$16 per day ([same source](https://ourworldindata.org/explorers/poverty-explorer-2011-vs-2017-ppp?tab=chart&facet=none&country=~OWID_WRL&Indicator=Mean+income+or+consumption&International-%24=2011+prices&Poverty+line=%2410+per+day&Household+survey+data+type=Show+data+from+both+income+and+consumption+surveys) as above). * If you find GDP per capita more relevant, then you find that it is [int.-$17,000](https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD), int.-$46.5 per day (but keep in mind that you then cannot compare it with people’s household income). * If you are relying on the World Inequality Lab estimate for the global average income you find that it is [int.-$25,463](https://ourworldindata.org/explorers/incomes-across-distribution-wid?tab=chart&facet=none&country=~OWID_WRL&Indicator=Mean+income&Decile%2Fquantile=All+deciles&Income+measure=Before+tax&Period=Year), int.-$70 per day (again this cannot be compared with estimates from the other sources). According to World Bank Poverty and Inequality data – which you can [explore here](https://ourworldindata.org/explorers/poverty-explorer-2011-vs-2017-ppp?tab=chart&facet=none&country=MDG~GBR&Indicator=Mean+income+or+consumption&International-%24=2011+prices&Poverty+line=%2410+per+day&Household+survey+data+type=Show+data+from+both+income+and+consumption+surveys).",The economies that are home to the poorest billions of people need to grow if we want global poverty to decline substantially 1t5b9foM14E-4ib1jCz3ZozaidP7KAgnawPKuJYX1oKo,the-share-of-democracies-has-recently-stagnated-but-remains-near-its-historical-high,data-insight,"{""body"": [{""size"": ""narrow"", ""type"": ""image"", ""filename"": ""countries-democracies-autocracies-row-desktop.png"", ""parseErrors"": [], ""smallFilename"": ""countries-democracies-autocracies-row-mobile.png""}, {""type"": ""text"", ""value"": [{""text"": ""Over the last twenty years, the share of countries that are democracies has remained relatively stable."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Relying on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/regimes-of-the-world-data"", ""children"": [{""text"": ""data from Varieties of Democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", which we just updated, the chart shows that around half of all countries are democracies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world remains close to the historical high in the early 2000s and is much more democratic than 50 years ago; only 20% of countries were democracies in the early 1970s."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, the chart shows smaller changes within democratic regimes: the share of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""liberal"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" democracies, which grant additional individual and minority rights and constrain their governments, has decreased over the last decade."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While democracy has remained fairly resilient over the last few decades, this recent stagnation and limited rollback stresses that progress on increasing political rights is neither linear nor guaranteed."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/less-democratic"", ""children"": [{""text"": ""Read more about the recent changes in democracy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" →"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""data-insight"", ""title"": ""The share of democracies has recently stagnated but remains near its historical high"", ""authors"": [""Bastian Herre""], ""approved-by"": ""Ed"", ""grapher-url"": ""https://ourworldindata.org/grapher/countries-democracies-autocracies-row?time=1973..latest""}",1,2024-05-02 09:09:35,2024-05-14 05:45:46,2024-05-13 07:57:58,unlisted,ALBJ4LvPF0UXrE-Fze4oW7-nyunZfBb6xyr-g4lhdd__aONHdbSL4pQ7jXitmGfNf7WlP6ZpfN28gYTf1hqKXw,," Over the last twenty years, the share of countries that are democracies has remained relatively stable. Relying on [data from Varieties of Democracy](https://ourworldindata.org/regimes-of-the-world-data), which we just updated, the chart shows that around half of all countries are democracies. The world remains close to the historical high in the early 2000s and is much more democratic than 50 years ago; only 20% of countries were democracies in the early 1970s. However, the chart shows smaller changes within democratic regimes: the share of _liberal_ democracies, which grant additional individual and minority rights and constrain their governments, has decreased over the last decade. While democracy has remained fairly resilient over the last few decades, this recent stagnation and limited rollback stresses that progress on increasing political rights is neither linear nor guaranteed. [Read more about the recent changes in democracy](https://ourworldindata.org/less-democratic) →",The share of democracies has recently stagnated but remains near its historical high 1t40rD1GaWXDn9ESSFP92Fw07wZgssedL4PyNuYN66Yk,data-page-texts-per-capita-consumption-based-co-emissions-global-carbon-project,fragment,"{""toc"": [{""slug"": ""undefined-are-emissions-from-aviation-and-shipping-included"", ""text"": ""Are emissions from aviation and shipping included?"", ""title"": ""Are emissions from aviation and shipping included?"", ""isSubheading"": false}, {""slug"": ""undefined-why-are-consumption-based-emissions-only-available-from-1990-why-are-they-not-available-for-all-countries"", ""text"": ""Why are consumption-based emissions only available from 1990? Why are they not available for all countries?"", ""title"": ""Why are consumption-based emissions only available from 1990? 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If a country's consumption-based emissions are higher than its production emissions it is a net importer of carbon dioxide. If its consumption-based emissions are lower, then it is a net exporter. * Per capita emissions represent the emissions of an average person in a country or region – they are total emissions divided by population. * Consumption-based emissions are not available for all countries because not all countries have sufficient, high-quality trade data. But those without complete data are a small fraction (3%) of the global total. * This data measures Carbon dioxide (CO₂) emissions from fossil fuels and industry and does not include emissions from land use change, deforestation, soils, or vegetation. * Emissions from international aviation and shipping are not included in any country or region’s emissions. They are only included in the global total emissions. # descriptionFromSource # faqs ## Are emissions from aviation and shipping included? Emissions from domestic aviation and shipping are included in each country’s total. Emissions from international aviation and shipping are not included in any country or region’s total. This is because there is no international agreement on how these emissions should be allocated: should they, for example, be allocated to the country of origin or destination? In our [related article](https://ourworldindata.org/carbon-footprint-flying) we look at a separate dataset on emissions from aviation. They are, however, included in the global total. You also [find it here](https://ourworldindata.org/grapher/annual-co-emissions-by-region) as a separate category. ## Why are consumption-based emissions only available from 1990? Why are they not available for all countries? To calculate consumption-based emissions we need detailed trade data between countries and the emissions intensity (the amount of CO2 emitted per dollar spent) across many industries and sectors in each country. Prior to 1990, there is insufficient high-quality, high-resolution data to produce these calculations. For this same reason – insufficient high-resolution trade data – it is not currently possible to calculate consumption-based emissions for all countries. It is mostly high-income and major economies that are included. Consumption-based emissions also always lag production-based emissions by one year. For example, when production-based emissions for 2020 were released, the latest year for consumption-based emissions was 2019. This is because the required resolution of trade data was not yet available for 2020. # _OLDdatasetDescription _ This is a collection of data about CO2 and other greenhouse gas emissions, at the national and global level, prepared by Our World in Data based on data published by the Global Carbon Project. The Global Carbon Project is an organisation that seeks to quantify global greenhouse gas emissions and their causes. The annually published Global Carbon Budget is a report produced by a community researchers to announce a global carbon budget quantifying carbon dioxide (CO2) emissions for the prior year. The dataset is available at [https://globalcarbonbudget.org/archive/](https://globalcarbonbudget.org/archive/) . All documentation can be found in P. Friedlingstein et al., Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811-4900, 2022: [https://doi.org/10.5194/essd-14-4811-2022](https://doi.org/10.5194/essd-14-4811-2022) Notes on the preparation of this dataset: * We ingest the original data from various files produced by the Global Carbon Project. * We harmonize country names and create region aggregates following our definitions. * Each country's share of the global population is calculated using Our World in Data’s population dataset, based on different sources. * Data on global emissions has been converted by Our World in Data from tonnes of carbon to tonnes of carbon dioxide (CO₂) using a conversion factor of 3.664. * Our World in Data have renamed bunker fuels as ""International transport"" for improved clarity, which includes emissions from international aviation and shipping. * Variables include each country, region and World Bank income group's share of the global population; production-based (territorial); and consumption-based (trade-adjusted) carbon dioxide emissions. * Emissions from the Kuwaiti oil fires in 1991 have been included as part of Kuwait's emissions for that year. # variableProcessingInfo The emissions data provided by the source is expressed in tonnes of carbon. We convert this to tonnes of carbon dioxide (CO₂) using a conversion factor of 3.664 (the mass of one CO2 molecule relative to that of a carbon atom). We calculate emissions per capita using _Our World in Data’s_ reference population dataset. # sourceDescription1 _The Global Carbon Budget 2022 has over 105 contributors from 80 organisations and 18 countries. It was founded by the Global Carbon Project international science team to track the trends in global carbon emissions and sinks and is a key measure of progress towards the goals of the Paris Agreement. It’s widely recognised as the most comprehensive report of its kind. The 2022 report was published at COP27 in Egypt on Friday 11th November._ [Text from_ _[Global Carbon Budget website](https://globalcarbonbudget.org/carbonbudget/)] The dataset is available at [https://globalcarbonbudget.org/archive/](https://globalcarbonbudget.org/archive/). Documentation of the dataset can be found in P. Friedlingstein et al., Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811-4900, 2022: [https://doi.org/10.5194/essd-14-4811-2022](https://doi.org/10.5194/essd-14-4811-2022). # sourceDescription2 Many population datasets cover a specific period – for example, the UN publishes data from 1950 onwards. However, few maintain very long-term datasets that are continually updated to the present day. Our team builds and maintains a long-run dataset on population by country, region, and for the world, based on three key sources: * 10,000 BCE to 1799: [HYDE version 3.2](https://www.pbl.nl/en/image/links/hyde) * 1800 to 1949: Gapminder’s [Population version 7](https://www.gapminder.org/data/documentation/gd003) * 1950 onwards: [UN World Population Prospects (2022)](https://population.un.org/wpp/Download/Standard/Population) * For former countries: Gapminder’s [Systema Globalis](https://github.com/open-numbers/ddf--gapminder--systema_globalis) In all sources we rely on, historical population estimates are based on today’s geographical borders. You can find the complete list of the sources used for each particular country and year [here](https://ourworldindata.org/grapher/sources-population-dataset). The scripts that produce this long-run dataset can be accessed [in our GitHub repository](https://github.com/owid/etl/tree/master/etl/steps/data/garden/demography/2023-03-31/population).",Data page texts – Per capita consumption-based CO₂ emissions | Global Carbon Project 1szX11tRqr-NDnar2J7gZBQWMCFB4JFj5BEw0RkReFkU,estimating-total-global-paralytic-polio-cases,article,"{""toc"": [{""slug"": ""adapted-methodology-from-tebbens-et-al-2010"", ""text"": ""Adapted methodology from Tebbens et al. (2010)"", ""title"": ""Adapted methodology from Tebbens et al. (2010)"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""results-comparison-of-reported-cases-vs-estimated-cases"", ""text"": ""Results: comparison of reported cases vs. estimated cases"", ""title"": ""Results: comparison of reported cases vs. estimated cases"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""results-estimated-cases-by-region"", ""text"": ""Results: estimated cases by region"", ""title"": ""Results: estimated cases by region"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""This article was first published in April 2018. It was last updated in May 2022."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this post we explain how we estimate the number of cases of paralytic polio by country and region. To summarize, we apply a method by Tebbens et al. (2010) to the reported cases, using two measures of testing quality."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The number of reported cases of paralytic polio can be an underestimate of the number of actual cases for several reasons. People with acute flaccid paralysis (AFP) may not be seen by doctors or healthcare workers and reported as suspected cases of polio. They may not have samples taken, or taken in time, to detect the presence of poliovirus. Or their samples may not be tested for poliovirus and reported."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Adapted methodology from Tebbens et al. (2010)"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In their paper, Tebbens et al. (2010) introduced a method to adjust for under-detection."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Their method uses two indicators: non-polio AFP rate and AFP cases with adequate stool collection."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The non-polio AFP rate is the rate at which cases of AFP from non-polio causes are detected and reported. This indicates whether AFP is being detected and reported sufficiently."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The metric called 'AFP cases with adequate stool collection' is the share of suspected cases that have testable stool samples taken from them. To test for the poliovirus, two samples need to be taken between 24–48 hours apart, within 14 days of the onset of paralysis."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Tebbens et al. used these two indicators to derive a 'correction factor'. Then, the number of reported cases was multiplied by this correction factor to estimate the number of actual cases."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In their paper, they estimated cases from 1980 to 2009 for countries that received GPEI support. However, here we adapt this method by applying it to all countries that reported data to the WHO from 1980 to 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Up to 1995:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" Surveillance data from the WHO was not available, so they applied a correction factor of 7 for all countries that received GPEI support to account for under-detection."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Between 1996 and 2000"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": Polio surveillance was rapidly expanding, and surveillance data was provided to researchers by the WHO. This means Tebbens et al. (2010) applied a correction factor for the countries that received GPEI support based on the surveillance data to account for under-detection. They applied a correction factor of 7 when a country reported a non-polio AFP rate <1 or an adequate stool collection <60%. They applied a correction factor of 2 when a country reported a non-polio AFP rate <2 or an adequate stool collection <80%. Otherwise, they applied a correction factor of 1.11."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since this data is not publicly available, we apply a correction factor of 1.11 to all countries during this time period to match their average correction factor. However, as noted above, we apply this to all countries, including those that did not receive GPEI support."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Post-2000:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" After this, they applied a correction factor of 7 when a country reported a non-polio AFP rate <1 or an adequate stool collection <60%. They applied a correction factor of 2 when a country reported a non-polio AFP rate <2 or an adequate stool collection <80%. Otherwise, they applied a correction factor of 1.11."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""They also made two exceptions in footnote (a) of Table 1, justifying their use of a correction factor of 1.11 for China from 1989–1992 and Oman in 1988 because they had large active investigations into outbreaks."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Results: comparison of reported cases vs. estimated cases"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this chart you can compare the estimated number of polio cases with the number of reported cases."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is important to note that these are rough estimates of the number of paralytic polio cases based on metrics of testing quality (non-polio AFP rate and AFP cases with adequate stool collection). Data on testing quality by country is not available before 2000."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""side-by-side"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/the-difference-between-the-reported-and-the-estimated-actual-number-of-paralytic-polio-cases"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Results: estimated cases by region"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart shows the results of this method by region. Using the adapted methodology based on Tebbens et al. (2010), there were around 370,000 paralytic polio cases worldwide in 1980. Since then, the number of cases has declined in all regions. Today, the world is very close to the goal of eradicating this disease globally."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""side-by-side"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/number-of-estimated-paralytic-polio-cases-by-world-region"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""9e617f45e189c215a55b64a800f646b2422b6f1d"": {""id"": ""9e617f45e189c215a55b64a800f646b2422b6f1d"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Duintjer Tebbens, R. J., Pallansch, M. A., Cochi, S. L., Wassilak, S. G. F., Linkins, J., Sutter, R. W., Aylward, R. B., & Thompson, K. M. (2010). Economic analysis of the global polio eradication initiative. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Vaccine"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""29"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(2), 334–343. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/j.vaccine.2010.10.026"", ""children"": [{""text"": ""https://doi.org/10.1016/j.vaccine.2010.10.026"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d087cda7569384783dfa80171f8d3eb67ae9453b"": {""id"": ""d087cda7569384783dfa80171f8d3eb67ae9453b"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Their stool samples should then be processed in a GPEI-accredited laboratory for the presence of the poliovirus. If they test positive, they are considered a 'confirmed case' of polio. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""WHO-recommended surveillance standard of poliomyelitis"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". (n.d.). World Health Organization. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20210423203212/https://www.who.int/immunization/monitoring_surveillance/burden/vpd/surveillance_type/active/poliomyelitis_standards/en/"", ""children"": [{""text"": ""https://web.archive.org/web/20210423203212/https://www.who.int/immunization/monitoring_surveillance/burden/vpd/surveillance_type/active/poliomyelitis_standards/en/"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Estimation of the number of paralytic polio cases by region"", ""authors"": [""Saloni Dattani"", ""Fiona Spooner""], ""excerpt"": ""Only a fraction of all polio cases are reported. Here we apply a method by Tebbens et al. (2010) to estimate the actual number of global polio cases."", ""dateline"": ""April 20, 2022"", ""subtitle"": ""Only a fraction of all polio cases are reported. Here we apply a method by Tebbens et al. (2010) to estimate the actual number of global polio cases."", ""sidebar-toc"": false, ""featured-image"": ""reported-vs-estimated-total-number-of-paralytic-polio-cases-globally.png""}",1,2024-02-15 08:51:28,2022-04-20 09:00:00,2024-02-15 08:54:45,unlisted,ALBJ4Lt0XP8Tum7rKtb9Oa1xRuhc0rrSKN4vwZYdyoifhBQ1rB1pvkS28g508g_q82gtMh-p67IDsXzo4uvvnw,,"This article was first published in April 2018. It was last updated in May 2022. In this post we explain how we estimate the number of cases of paralytic polio by country and region. To summarize, we apply a method by Tebbens et al. (2010) to the reported cases, using two measures of testing quality. The number of reported cases of paralytic polio can be an underestimate of the number of actual cases for several reasons. People with acute flaccid paralysis (AFP) may not be seen by doctors or healthcare workers and reported as suspected cases of polio. They may not have samples taken, or taken in time, to detect the presence of poliovirus. Or their samples may not be tested for poliovirus and reported. ## Adapted methodology from Tebbens et al. (2010) In their paper, Tebbens et al. (2010) introduced a method to adjust for under-detection.1 Their method uses two indicators: non-polio AFP rate and AFP cases with adequate stool collection. The non-polio AFP rate is the rate at which cases of AFP from non-polio causes are detected and reported. This indicates whether AFP is being detected and reported sufficiently. The metric called 'AFP cases with adequate stool collection' is the share of suspected cases that have testable stool samples taken from them. To test for the poliovirus, two samples need to be taken between 24–48 hours apart, within 14 days of the onset of paralysis.2 Tebbens et al. used these two indicators to derive a 'correction factor'. Then, the number of reported cases was multiplied by this correction factor to estimate the number of actual cases. In their paper, they estimated cases from 1980 to 2009 for countries that received GPEI support. However, here we adapt this method by applying it to all countries that reported data to the WHO from 1980 to 2020. **Up to 1995:** Surveillance data from the WHO was not available, so they applied a correction factor of 7 for all countries that received GPEI support to account for under-detection. **Between 1996 and 2000**: Polio surveillance was rapidly expanding, and surveillance data was provided to researchers by the WHO. This means Tebbens et al. (2010) applied a correction factor for the countries that received GPEI support based on the surveillance data to account for under-detection. They applied a correction factor of 7 when a country reported a non-polio AFP rate <1 or an adequate stool collection <60%. They applied a correction factor of 2 when a country reported a non-polio AFP rate <2 or an adequate stool collection <80%. Otherwise, they applied a correction factor of 1.11. Since this data is not publicly available, we apply a correction factor of 1.11 to all countries during this time period to match their average correction factor. However, as noted above, we apply this to all countries, including those that did not receive GPEI support. **Post-2000:** After this, they applied a correction factor of 7 when a country reported a non-polio AFP rate <1 or an adequate stool collection <60%. They applied a correction factor of 2 when a country reported a non-polio AFP rate <2 or an adequate stool collection <80%. Otherwise, they applied a correction factor of 1.11. They also made two exceptions in footnote (a) of Table 1, justifying their use of a correction factor of 1.11 for China from 1989–1992 and Oman in 1988 because they had large active investigations into outbreaks. ## Results: comparison of reported cases vs. estimated cases In this chart you can compare the estimated number of polio cases with the number of reported cases. It is important to note that these are rough estimates of the number of paralytic polio cases based on metrics of testing quality (non-polio AFP rate and AFP cases with adequate stool collection). Data on testing quality by country is not available before 2000. ## Results: estimated cases by region This chart shows the results of this method by region. Using the adapted methodology based on Tebbens et al. (2010), there were around 370,000 paralytic polio cases worldwide in 1980. Since then, the number of cases has declined in all regions. Today, the world is very close to the goal of eradicating this disease globally. Duintjer Tebbens, R. J., Pallansch, M. A., Cochi, S. L., Wassilak, S. G. F., Linkins, J., Sutter, R. W., Aylward, R. B., & Thompson, K. M. (2010). Economic analysis of the global polio eradication initiative. _Vaccine_, _29_(2), 334–343. [https://doi.org/10.1016/j.vaccine.2010.10.026](https://doi.org/10.1016/j.vaccine.2010.10.026) Their stool samples should then be processed in a GPEI-accredited laboratory for the presence of the poliovirus. If they test positive, they are considered a 'confirmed case' of polio. _WHO-recommended surveillance standard of poliomyelitis_. (n.d.). World Health Organization. [https://web.archive.org/web/20210423203212/https://www.who.int/immunization/monitoring_surveillance/burden/vpd/surveillance_type/active/poliomyelitis_standards/en/](https://web.archive.org/web/20210423203212/https://www.who.int/immunization/monitoring_surveillance/burden/vpd/surveillance_type/active/poliomyelitis_standards/en/)",Estimation of the number of paralytic polio cases by region 1swoRvLSiP8Hk_hqyXTszMSe3WwV2DKbdKF65JX1eSn8,global-deforestation-peak,article,"{""toc"": [{""slug"": ""undefined-deforestation-rates-are-still-high-across-the-tropics"", ""text"": ""Deforestation rates are still high across the tropics"", ""title"": ""Deforestation rates are still high across the tropics"", ""isSubheading"": false}, {""slug"": ""undefined-forest-definitions-and-comparisons-to-other-datasets"", ""text"": ""Forest definitions and comparisons to other datasets"", ""title"": ""Forest definitions and comparisons to other datasets"", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Since the end of the last great ice age – 10,000 years ago – the world has lost one-third of its forests."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Two billion hectares of forest – an area twice the size of the United States – has been cleared to grow crops, raise livestock, and use for fuelwood."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In a "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://ourworldindata.org/deforestation#the-world-has-lost-one-third-of-its-forests-but-an-end-of-deforestation-is-possible"", ""children"": [{""text"": ""previous post"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""text"": "" we looked at this change in global forests over the long-run. What this showed was that although humans have been deforesting the planet for millennia, the rate of forest loss accelerated rapidly in the last few centuries. Half of global forest loss occurred between 8,000BC and 1900; the other half was lost in the last century alone."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To understand this more recent loss of forest, let’s zoom in on the last 300 years. The world lost 1.5 billion hectares of forest over that period. That’s an area 1.5-times the size of the United States."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart we see the decadal losses and gains in global forest cover. On the horizontal axis we have time, spanning from 1700 to 2020; on the vertical axis we have the decadal change in forest cover. The taller the bar, the larger the change in forest area. This is measured in hectares, which is equivalent to 10,000 m²."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Forest loss measures the net change in forest cover: the loss in forests due to deforestation plus any expansion of forest through afforestation."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Unfortunately there is no single source that provides consistent and transparent data on deforestation rates over this period of time. Methodologies change over time, and estimates – especially in earlier periods – are highly uncertain. This means I’ve had to use two separate datasets to show this change over time. As we’ll see, they produce different estimates of deforestation for an overlapping decade – the 1980s – which suggests that these are not directly comparable. I do not recommend combining them into a single series, but the overall trends are still applicable and tell us an important story about deforestation over the last three centuries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The first series of data comes from Williams (2006), who estimates deforestation rates from 1700 to 1995."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Due to poor data resolution, these are often given as average rates over longer periods – for example, annual average rates are given over the period from 1700 to 1849, and 1920 to 1949. That’s why these rates look strangely consistent over long period of time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The second series comes from the UN Food and Agriculture Organization (FAO). It produces a new assessment of global forests every 5 years."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""global-deforestation-1700s.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The rate and location of forest loss changed a lot. From 1700 to 1850, 19 million hectares were being cleared every decade. That’s around half the size of Germany."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It was most temperate forests across Europe and North America that were being lost at this time. Population growth meant that today’s rich countries needed more and more resources such as land for agriculture, wood for energy, and for construction."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Moving into the 20th century there was a stepwise change in demand for agricultural land and energy from wood. Deforestation rates accelerated. And the hotspot of deforestation changed from the equivalent to the area of South Africa. This increase was mostly driven by tropical deforestation as countries across Asia and Latin America."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Global forest loss appears to have reached its peak in the 1980s. The two sources do not agree on the magnitude of this loss: Williams (2006) estimates a loss of 150 million hectares – an area half the size of India – during that decade."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Interestingly, the UN FAO 1990 report also estimated that "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""deforestation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" in tropical ‘developing’ countries was 154 million hectares. But, it estimated that regrowth of old forests offset some of these losses, leading to a net loss of 102 million hectares."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The latest UN Forest Resources Assessment estimates that the net loss in forests has declined in the last three decades, from 78 million hectares in the 1990s to 47 million hectares in the 2010s."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This data maps an expected pathway based on what we know from how human-forest interactions evolve."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we explore in more detail in our "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://ourworldindata.org/deforestation#forest-transitions-why-do-we-lose-then-regain-forests"", ""children"": [{""text"": ""related article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""text"": "", countries tend to follow a predictable development in forest cover, a U-shaped curve."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" They lose forests as populations grow and demand for agricultural land and fuel increases, but eventually they reach the so-called ‘forest transition point’ where they begin to regrow more forests than they lose."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""That is what has happened in temperate regions: they have gone through a period of high deforestation rates, before a slowing and reversal of this trend."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, many countries – particularly in the tropics and sub-tropics – are still moving through this transition. Deforestation rates are still very high."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""Deforestation rates are still high across the tropics"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Large areas of forest are still being lost in the tropics today. This is particularly tragic because these are regions with very high concentrations of biodiversity."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Let’s look at estimates of deforestation from the latest UN Forest report. This shows us raw deforestation rates, without any adjustment for the regrowth or plantation of forests, which is arguably not as good for ecosystems or carbon storage."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is shown in the chart below."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We can see that the UN does estimate that deforestation rates have fallen since the 1990s. However, there was very little progress from the 1990s to 2000s, and an estimated 26% drop in rates in the 2010s. In 2022, the FAO published "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.fao.org/forest-resources-assessment/remote-sensing/fra-2020-remote-sensing-survey/en/"", ""children"": [{""text"": ""a separate assessment"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" based on Remote Sensing methods; it did not report data for the 1990s, but also estimated a 29% reduction in deforestation rates from early 2000s to the 2010s."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""narrow"", ""type"": ""image"", ""filename"": ""global-deforestation-1990s-onwards.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is progress, but it needs to happen much faster. The world is still losing large amounts of primary forests every year. To put these numbers in context: during the 1990s and first decade of the 2000s, an area almost the size of India was deforested."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Even with the ‘improved’ rates in the 2010s, this still amounted to an area around twice the size of Spain."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The regrowth of forests is a positive development. In the chart below, we see how this affects the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""net change"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" in global forests. Forest recovery and plantation ‘offsets’ a lot of deforestation such that the net losses are around half the rates of deforestation alone."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""narrow"", ""type"": ""image"", ""filename"": ""global-deforestation-net-loss.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But we should be cautious here: it’s often not the case that the ‘positives’ of regrowing one hectare of forest offset the ‘losses’ of one hectare of deforestation. Cutting down one hectare of rich, tropical rainforest cannot be completely offset by the plantation of forest in a temperate country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Forest expansion is positive, but does not negate the need to finally end deforestation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The history of deforestation is a tragic one, in which we not only lost these wild and beautiful landscapes but also the wildlife within them. But, the fact that forest transitions are possible should give us confidence that a positive future is possible. Many countries have not only ended deforestation, but actually achieved substantial reforestation. It will be possible for our generation to achieve the same on the global scale and bring the 10,000 year history of forest loss to an end."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we want to end deforestation we need to understand where and why it’s happening; where countries are within their transition; and what can be done to accelerate their progress through it. We need to pass the transition point as soon as possible, while minimising the amount of forest we lose along the way."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://ourworldindata.org/what-are-drivers-deforestation"", ""children"": [{""text"": ""this article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""text"": "" I look at what’s driving deforestation: that helps us understand what we need to do to solve it."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""Forest definitions and comparisons to other datasets"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There is no universal definition of what a ‘forest’ is. That means there are a range of estimates of forest area, and how this has changed over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this article, in the recent period I have used data from the UN’s Global Forest Resources Assessment (2020). The UN carries out these global forest stocktakes every five years. These forest figures are widely-used in research, policy, and international targets, such as in the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://unstats.un.org/sdgs/metadata/files/Metadata-15-01-01.pdf"", ""children"": [{""text"": ""Sustainable Development Goals"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The UN FAO has a very specific definition of a forest. It’s “land spanning more than 0.5 hectares with trees higher than 0.5 meters and a canopy cover of more than 10%, or trees able to reach these thresholds in situ.”"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In other words, it has criteria for the area that must be covered (0.5 hectares), the minimum height of trees (0.5 meters) and a density of at least 10%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Compare this to the UN Framework on Climate Change (UNFCCC), which uses forest estimates to calculate land use carbon emissions, and for its REDD+ programme, where low-to-middle income countries can receive finance for verified projects that prevent or reduce deforestation. It "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.un-redd.org/glossary/forest"", ""children"": [{""text"": ""defines a forest"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" as having a density of 10-30%, a minimum tree height of 2-5 meters, and a smaller area of 0.1 hectares."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It’s not just forest definitions that vary between sources. What is measured (and not measured) differs too. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.globalforestwatch.org/"", ""children"": [{""text"": ""Global Forest Watch"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" is an interactive online dashboard that tracks ‘tree loss’ and ‘forest loss’ across the world. It measures this in real-time, and can provide better estimates of year-to-year variations in rates of tree loss."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, the UN FAO and Global Forest Watch do not measure the same thing."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The UN FAO measures "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""deforestation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" based on how land is used. It measures the permanent conversion of forested land to another use, such as pasture, croplands, or urbanization. Temporary changes in forest cover, such as losses through wildfire, or small-scale shifting agriculture are not included in deforestation figures, because it is assumed that they will regrow. If the use of land has not changed, it is not considered deforestation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Global Forest Watch (GFW) measures temporary changes in forests. It can detect changes in land "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""cover"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", but does not differentiate the underlying land use. All deforestation would be considered tree loss, but a lot of tree loss would not be considered as deforestation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As GFW describes in its definition of ‘forest loss’: “Loss” indicates the removal or mortality of tree cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.”"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We therefore cannot directly compare these sources. This "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/"", ""children"": [{""text"": ""article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""text"": "" from Global Forest Watch gives a good overview of the differences between the UN FAO and GFW methods."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since GFW uses satellite imagery, its methods continually improve. This makes its ability to detect changes in forest cover even stronger. But it also means that comparisons over time are more difficult. It currently warns against comparing pre-2015 and post-2015 data since there was a significant methodological change at that time. Note that this is also a problem in UN FAO reports, as I’ll soon explain."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What data from GFW makes clear is that forest loss across the tropics is still very high, and in the last few years, little progress has been made. Since UN FAO reports are only published in 5-year intervals, they miss these shorter-term fluctuations in forest loss. The GFW’s shorter-interval stocktakes of how countries are doing will become increasingly valuable."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One final point to note is that UN FAO estimates have also changed over time, with improved methods and better access to data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I looked at how net forest loss rates in the 1990s were reported across five UN reports: 2000, 2005, 2010, 2015 and 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Estimated rates changed in each successive report:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""children"": [{""url"": ""https://www.fao.org/forest-resources-assessment/past-assessments/fra-2000/en/"", ""children"": [{""text"": ""2000 report"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "":"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" Net losses of 92 million hectares"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""url"": ""https://www.fao.org/forest-resources-assessment/past-assessments/fra-2005/en/"", ""children"": [{""text"": ""2005 report"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "": "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""89 million hectares"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""url"": ""https://www.fao.org/forest-resources-assessment/past-assessments/fra-2010/en/"", ""children"": [{""text"": ""2010 report"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "": "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""83 million hectares"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""url"": ""https://www.fao.org/forest-resources-assessment/past-assessments/fra-2015/en/"", ""children"": [{""text"": ""2015 report"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "":"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" 72 million hectares"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""url"": ""https://www.fao.org/documents/card/en/c/ca9825en"", ""children"": [{""text"": ""2020 report"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "":"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" 78 million hectares"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This should not affect the overall trends reported in the latest report: the UN FAO should – as far as is possible – apply the same methodology to its 1990s, 2000s, and 2010s estimates. However, it does mean we should be cautious about comparing absolute magnitudes across different reports."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is one challenge in presenting 1980 figures in the main visualization in this article. Later reports have not updated 1980 figures, so we have to rely on estimates from earlier reports. We don’t know whether 1980s rates would also be lower with the UN FAO’s most recent adjustments. If so, this would mean the reductions in net forest loss from the 1980s to 1990s were lower than is shown from available data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""This article was first published in early 2021. It was updated in September 2023 with further discussion on deforestation trends and comparisons to other datasets."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Update note"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""18892caaa433a12ac0b4a54145beb8674d5faa22"": {""id"": ""18892caaa433a12ac0b4a54145beb8674d5faa22"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Williams, M. (2003). Deforesting the earth: from prehistory to global crisis. University of Chicago Press."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""36a20ada263ee7c29c71d28283b0e50754e7e51d"": {""id"": ""36a20ada263ee7c29c71d28283b0e50754e7e51d"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Year-to-year data on forest change comes with several issues: either data at this resolution is not available, or year-to-year changes can be highly variable. For this reason, data sources – including the UN Food and Agriculture Organization – tend to aggregate annual losses as the average over five-year or decadal periods."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""5818873a27b3ebcc1f49c18faa2f9235ecb7f91b"": {""id"": ""5818873a27b3ebcc1f49c18faa2f9235ecb7f91b"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""It estimated that the net change in forests "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""without "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""plantations was 121 million hectares. With plantations included – as is standard for the UN’s forest assessments – this was 102 million hectares."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""5d12ab047d75178bbcc112b6cad72c8cd264452d"": {""id"": ""5d12ab047d75178bbcc112b6cad72c8cd264452d"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The area of Spain is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_area"", ""children"": [{""text"": ""around"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" 51 million hectares. Double this area is around 102 million hectares – a little under 110 million hectares."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""8fdcb6ee175c635ad1d24bb6f1b6a3e5ba5c0f00"": {""id"": ""8fdcb6ee175c635ad1d24bb6f1b6a3e5ba5c0f00"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The area of India is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_area"", ""children"": [{""text"": ""around"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" 330 million hectares. The combined losses in the 1990s and 2000s was 309 million hectares. Just 6% less than the size of India."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9fa7ed021bf5aa77ff25a0efa7dab28ec2460d80"": {""id"": ""9fa7ed021bf5aa77ff25a0efa7dab28ec2460d80"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Hosonuma, N., Herold, M., De Sy, V., De Fries, R. S., Brockhaus, M., Verchot, L., … & Romijn, E. (2012). An assessment of deforestation and forest degradation drivers in developing countries. Environmental Research Letters, 7(4), 044009."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""af27edfab8343ce4546c8312f6dfdc311bd11835"": {""id"": ""af27edfab8343ce4546c8312f6dfdc311bd11835"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Mather, A. S., Fairbairn, J., & Needle, C. L. (1999). The course and drivers of the forest transition: the case of France. Journal of Rural Studies, 15(1), 65-90."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Mather, A. S., & Needle, C. L. (2000). The relationships of population and forest trends. Geographical Journal, 166(1), 2-13."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b74d8ece7c166d408c05888527c13ef76866a577"": {""id"": ""b74d8ece7c166d408c05888527c13ef76866a577"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The data for 1990 to 2000 is from the altest assessment: the UN’s Global Forest Resources Assessment 2020."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""FAO (2020). Global Forest Resources Assessment 2020: Main report. Rome. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.4060/ca9825en.{/ref"", ""children"": [{""text"": ""https://doi.org/10.4060/ca9825en"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""eb91b18be65a52e8010372b98c682156faf0d146"": {""id"": ""eb91b18be65a52e8010372b98c682156faf0d146"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Estimates vary, but most date the end of the last great ice age to around 11,700 years ago."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Kump, L. R., Kasting, J. F., & Crane, R. G. (2004). The Earth System (Vol. 432). Upper Saddle River, NJ: Pearson Prentice Hall."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Global deforestation peaked in the 1980s. Can we bring it to an end?"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""There was a marked acceleration in deforestation in the 20th century. But, global deforestation peaked in the 1980s. Since then, it has slowed."", ""dateline"": ""September 19, 2023"", ""subtitle"": ""There was a marked acceleration in deforestation in the 20th century. But, global deforestation peaked in the 1980s. Since then, it has slowed."", ""featured-image"": ""FEATURED-IMAGE-Global-forest-loss-since-ice-age.png""}",1,2023-09-19 10:24:02,2021-02-19 12:00:00,2024-03-18 15:41:59,listed,ALBJ4LvVpdPrcbjyp5_0cXDj29znDAbIeMg20Awco-1iEHLjZUjRegFvuVGgQbF5w9Ws0I4eMLA37L9EFngfXw,,"Since the end of the last great ice age – 10,000 years ago – the world has lost one-third of its forests.1 Two billion hectares of forest – an area twice the size of the United States – has been cleared to grow crops, raise livestock, and use for fuelwood. In a **[previous post](https://ourworldindata.org/deforestation#the-world-has-lost-one-third-of-its-forests-but-an-end-of-deforestation-is-possible)** we looked at this change in global forests over the long-run. What this showed was that although humans have been deforesting the planet for millennia, the rate of forest loss accelerated rapidly in the last few centuries. Half of global forest loss occurred between 8,000BC and 1900; the other half was lost in the last century alone. To understand this more recent loss of forest, let’s zoom in on the last 300 years. The world lost 1.5 billion hectares of forest over that period. That’s an area 1.5-times the size of the United States. In the chart we see the decadal losses and gains in global forest cover. On the horizontal axis we have time, spanning from 1700 to 2020; on the vertical axis we have the decadal change in forest cover. The taller the bar, the larger the change in forest area. This is measured in hectares, which is equivalent to 10,000 m². Forest loss measures the net change in forest cover: the loss in forests due to deforestation plus any expansion of forest through afforestation.2 Unfortunately there is no single source that provides consistent and transparent data on deforestation rates over this period of time. Methodologies change over time, and estimates – especially in earlier periods – are highly uncertain. This means I’ve had to use two separate datasets to show this change over time. As we’ll see, they produce different estimates of deforestation for an overlapping decade – the 1980s – which suggests that these are not directly comparable. I do not recommend combining them into a single series, but the overall trends are still applicable and tell us an important story about deforestation over the last three centuries. The first series of data comes from Williams (2006), who estimates deforestation rates from 1700 to 1995.3 Due to poor data resolution, these are often given as average rates over longer periods – for example, annual average rates are given over the period from 1700 to 1849, and 1920 to 1949. That’s why these rates look strangely consistent over long period of time. The second series comes from the UN Food and Agriculture Organization (FAO). It produces a new assessment of global forests every 5 years.4 The rate and location of forest loss changed a lot. From 1700 to 1850, 19 million hectares were being cleared every decade. That’s around half the size of Germany. It was most temperate forests across Europe and North America that were being lost at this time. Population growth meant that today’s rich countries needed more and more resources such as land for agriculture, wood for energy, and for construction.5 Moving into the 20th century there was a stepwise change in demand for agricultural land and energy from wood. Deforestation rates accelerated. And the hotspot of deforestation changed from the equivalent to the area of South Africa. This increase was mostly driven by tropical deforestation as countries across Asia and Latin America. Global forest loss appears to have reached its peak in the 1980s. The two sources do not agree on the magnitude of this loss: Williams (2006) estimates a loss of 150 million hectares – an area half the size of India – during that decade. Interestingly, the UN FAO 1990 report also estimated that _deforestation_ in tropical ‘developing’ countries was 154 million hectares. But, it estimated that regrowth of old forests offset some of these losses, leading to a net loss of 102 million hectares.6 The latest UN Forest Resources Assessment estimates that the net loss in forests has declined in the last three decades, from 78 million hectares in the 1990s to 47 million hectares in the 2010s. This data maps an expected pathway based on what we know from how human-forest interactions evolve. As we explore in more detail in our **[related article](https://ourworldindata.org/deforestation#forest-transitions-why-do-we-lose-then-regain-forests)**, countries tend to follow a predictable development in forest cover, a U-shaped curve.7 They lose forests as populations grow and demand for agricultural land and fuel increases, but eventually they reach the so-called ‘forest transition point’ where they begin to regrow more forests than they lose. That is what has happened in temperate regions: they have gone through a period of high deforestation rates, before a slowing and reversal of this trend. However, many countries – particularly in the tropics and sub-tropics – are still moving through this transition. Deforestation rates are still very high. --- ## Deforestation rates are still high across the tropics Large areas of forest are still being lost in the tropics today. This is particularly tragic because these are regions with very high concentrations of biodiversity. Let’s look at estimates of deforestation from the latest UN Forest report. This shows us raw deforestation rates, without any adjustment for the regrowth or plantation of forests, which is arguably not as good for ecosystems or carbon storage. This is shown in the chart below. We can see that the UN does estimate that deforestation rates have fallen since the 1990s. However, there was very little progress from the 1990s to 2000s, and an estimated 26% drop in rates in the 2010s. In 2022, the FAO published [a separate assessment](https://www.fao.org/forest-resources-assessment/remote-sensing/fra-2020-remote-sensing-survey/en/) based on Remote Sensing methods; it did not report data for the 1990s, but also estimated a 29% reduction in deforestation rates from early 2000s to the 2010s. This is progress, but it needs to happen much faster. The world is still losing large amounts of primary forests every year. To put these numbers in context: during the 1990s and first decade of the 2000s, an area almost the size of India was deforested.8 Even with the ‘improved’ rates in the 2010s, this still amounted to an area around twice the size of Spain.9 The regrowth of forests is a positive development. In the chart below, we see how this affects the _net change_ in global forests. Forest recovery and plantation ‘offsets’ a lot of deforestation such that the net losses are around half the rates of deforestation alone. But we should be cautious here: it’s often not the case that the ‘positives’ of regrowing one hectare of forest offset the ‘losses’ of one hectare of deforestation. Cutting down one hectare of rich, tropical rainforest cannot be completely offset by the plantation of forest in a temperate country. Forest expansion is positive, but does not negate the need to finally end deforestation. The history of deforestation is a tragic one, in which we not only lost these wild and beautiful landscapes but also the wildlife within them. But, the fact that forest transitions are possible should give us confidence that a positive future is possible. Many countries have not only ended deforestation, but actually achieved substantial reforestation. It will be possible for our generation to achieve the same on the global scale and bring the 10,000 year history of forest loss to an end. If we want to end deforestation we need to understand where and why it’s happening; where countries are within their transition; and what can be done to accelerate their progress through it. We need to pass the transition point as soon as possible, while minimising the amount of forest we lose along the way. In **[this article](https://ourworldindata.org/what-are-drivers-deforestation)** I look at what’s driving deforestation: that helps us understand what we need to do to solve it. --- ## Forest definitions and comparisons to other datasets There is no universal definition of what a ‘forest’ is. That means there are a range of estimates of forest area, and how this has changed over time. In this article, in the recent period I have used data from the UN’s Global Forest Resources Assessment (2020). The UN carries out these global forest stocktakes every five years. These forest figures are widely-used in research, policy, and international targets, such as in the [Sustainable Development Goals](https://unstats.un.org/sdgs/metadata/files/Metadata-15-01-01.pdf). The UN FAO has a very specific definition of a forest. It’s “land spanning more than 0.5 hectares with trees higher than 0.5 meters and a canopy cover of more than 10%, or trees able to reach these thresholds in situ.” In other words, it has criteria for the area that must be covered (0.5 hectares), the minimum height of trees (0.5 meters) and a density of at least 10%. Compare this to the UN Framework on Climate Change (UNFCCC), which uses forest estimates to calculate land use carbon emissions, and for its REDD+ programme, where low-to-middle income countries can receive finance for verified projects that prevent or reduce deforestation. It [defines a forest](https://www.un-redd.org/glossary/forest) as having a density of 10-30%, a minimum tree height of 2-5 meters, and a smaller area of 0.1 hectares. It’s not just forest definitions that vary between sources. What is measured (and not measured) differs too. [Global Forest Watch](https://www.globalforestwatch.org/) is an interactive online dashboard that tracks ‘tree loss’ and ‘forest loss’ across the world. It measures this in real-time, and can provide better estimates of year-to-year variations in rates of tree loss. However, the UN FAO and Global Forest Watch do not measure the same thing. The UN FAO measures _deforestation_ based on how land is used. It measures the permanent conversion of forested land to another use, such as pasture, croplands, or urbanization. Temporary changes in forest cover, such as losses through wildfire, or small-scale shifting agriculture are not included in deforestation figures, because it is assumed that they will regrow. If the use of land has not changed, it is not considered deforestation. Global Forest Watch (GFW) measures temporary changes in forests. It can detect changes in land _cover_, but does not differentiate the underlying land use. All deforestation would be considered tree loss, but a lot of tree loss would not be considered as deforestation. As GFW describes in its definition of ‘forest loss’: “Loss” indicates the removal or mortality of tree cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.” We therefore cannot directly compare these sources. This **[article](https://www.globalforestwatch.org/blog/data-and-research/global-forest-watch-and-the-forest-resources-assessment-explained-in-5-graphics-2/)** from Global Forest Watch gives a good overview of the differences between the UN FAO and GFW methods. Since GFW uses satellite imagery, its methods continually improve. This makes its ability to detect changes in forest cover even stronger. But it also means that comparisons over time are more difficult. It currently warns against comparing pre-2015 and post-2015 data since there was a significant methodological change at that time. Note that this is also a problem in UN FAO reports, as I’ll soon explain. What data from GFW makes clear is that forest loss across the tropics is still very high, and in the last few years, little progress has been made. Since UN FAO reports are only published in 5-year intervals, they miss these shorter-term fluctuations in forest loss. The GFW’s shorter-interval stocktakes of how countries are doing will become increasingly valuable. --- One final point to note is that UN FAO estimates have also changed over time, with improved methods and better access to data. I looked at how net forest loss rates in the 1990s were reported across five UN reports: 2000, 2005, 2010, 2015 and 2020. Estimated rates changed in each successive report: * **[2000 report](https://www.fao.org/forest-resources-assessment/past-assessments/fra-2000/en/)****:** Net losses of 92 million hectares * **[2005 report](https://www.fao.org/forest-resources-assessment/past-assessments/fra-2005/en/)****: **89 million hectares * **[2010 report](https://www.fao.org/forest-resources-assessment/past-assessments/fra-2010/en/)****: **83 million hectares * **[2015 report](https://www.fao.org/forest-resources-assessment/past-assessments/fra-2015/en/)****:** 72 million hectares * **[2020 report](https://www.fao.org/documents/card/en/c/ca9825en)****:** 78 million hectares This should not affect the overall trends reported in the latest report: the UN FAO should – as far as is possible – apply the same methodology to its 1990s, 2000s, and 2010s estimates. However, it does mean we should be cautious about comparing absolute magnitudes across different reports. This is one challenge in presenting 1980 figures in the main visualization in this article. Later reports have not updated 1980 figures, so we have to rely on estimates from earlier reports. We don’t know whether 1980s rates would also be lower with the UN FAO’s most recent adjustments. If so, this would mean the reductions in net forest loss from the 1980s to 1990s were lower than is shown from available data. Williams, M. (2003). Deforesting the earth: from prehistory to global crisis. University of Chicago Press. Year-to-year data on forest change comes with several issues: either data at this resolution is not available, or year-to-year changes can be highly variable. For this reason, data sources – including the UN Food and Agriculture Organization – tend to aggregate annual losses as the average over five-year or decadal periods. It estimated that the net change in forests _without _plantations was 121 million hectares. With plantations included – as is standard for the UN’s forest assessments – this was 102 million hectares. The area of Spain is [around](https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_area) 51 million hectares. Double this area is around 102 million hectares – a little under 110 million hectares. The area of India is [around](https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_area) 330 million hectares. The combined losses in the 1990s and 2000s was 309 million hectares. Just 6% less than the size of India. Hosonuma, N., Herold, M., De Sy, V., De Fries, R. S., Brockhaus, M., Verchot, L., … & Romijn, E. (2012). An assessment of deforestation and forest degradation drivers in developing countries. Environmental Research Letters, 7(4), 044009. Mather, A. S., Fairbairn, J., & Needle, C. L. (1999). The course and drivers of the forest transition: the case of France. Journal of Rural Studies, 15(1), 65-90. Mather, A. S., & Needle, C. L. (2000). The relationships of population and forest trends. Geographical Journal, 166(1), 2-13. The data for 1990 to 2000 is from the altest assessment: the UN’s Global Forest Resources Assessment 2020. FAO (2020). Global Forest Resources Assessment 2020: Main report. Rome. [https://doi.org/10.4060/ca9825en](https://doi.org/10.4060/ca9825en.{/ref). Estimates vary, but most date the end of the last great ice age to around 11,700 years ago. Kump, L. R., Kasting, J. F., & Crane, R. G. (2004). The Earth System (Vol. 432). Upper Saddle River, NJ: Pearson Prentice Hall.",Global deforestation peaked in the 1980s. Can we bring it to an end? 1svZ66NfvVDcuQ6vKgc_C90gkeNGyRyxKVMbIPD31A60,world-population-growth-past-future,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""One of the big lessons from the demographic history of countries is that periods of rapid population growth are temporary. For many countries, the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/world-population-growth#the-demographic-transition-why-is-rapid-population-increase-a-temporary-phenomenon"", ""children"": [{""text"": ""demographic transition"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" has already ended, and as the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/fertility-rate#the-global-fertility-rate-has-halved-in-the-last-50-years"", ""children"": [{""text"": ""global fertility rate has now halved"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" we know that the world as a whole is approaching the end of rapid population growth."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This visualization presents an overview of the global demographic transition, based on estimates from the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://population.un.org/wpp/"", ""children"": [{""text"": ""2022 data release from the UN Population Division"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""2019-Revision-–-World-Population-Growth-1700-2100.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we explore at the beginning of the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/world-population-growth"", ""children"": [{""text"": ""topic page on population growth"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", the global population grew only very slowly up to 1700 – only 0.04% per year. In the many millennia up to that point in history "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/child-mortality-in-the-past"", ""children"": [{""text"": ""very high mortality of children"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" counteracted high fertility. The world was in the first stage of the demographic transition."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Once "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/child-mortality-global-overview"", ""children"": [{""text"": ""health improved and mortality declined"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" things changed quickly. Particularly over the course of the 20th century: Over the last 100 years global population more than quadrupled. As we see in the chart, the rise of the global population got steeper and steeper and you have just lived through the steepest increase of that curve. This also means that your existence is a tiny part of the reason why that curve is so steep."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The 7-fold increase of the world population over the course of two centuries amplified humanity’s impact on the natural environment. To provide space, food, and resources for a large world population in a way that is sustainable into the distant future is without question one of the large, serious challenges for our generation. We should not make the mistake of underestimating the task ahead of us. Yes, I expect new generations "", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20190315075445/https://www.bloomberg.com/opinion/articles/2019-03-14/want-to-help-fight-climate-change-have-more-children"", ""children"": [{""text"": ""to contribute"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", but for now, it is upon us to provide for them. Population growth is still fast: every year, 134 million are born, and 58 million die."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The difference is the number of people that we add to the world population in a year: 76 million."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Where do we go from here?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In pink, you see the annual population growth rate (that is, the percentage change in population per year) of the global population. It peaked around half a century ago. Peak population growth was reached in 1963 with an annual growth of 2.3%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since then the increase of the world population has slowed and today grows by 0.9% per year. This slowdown of population growth was not only predictable but "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/future-population-growth#how-accurate-have-past-population-projections-been"", ""children"": [{""text"": ""predicted"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Just as expected by demographers, the world as a whole is experiencing the closing of a massive demographic transition."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart also shows how the United Nations envision the end of the global demographic transition. As population growth continues to decline, the curve representing the world population is getting less and less steep."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Towards the end of the century, the UN expects the global population to reach its peak at around 10.4 billion. After this point, the UN demographers project global population growth to become negative, so that the world population starts to fall slowly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is hard to know the population dynamics beyond 2100. It will depend on the fertility rate and – as we discuss in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/fertility-rate#fertility-is-first-falling-with-development-and-then-rising-with-development"", ""children"": [{""text"": ""our entry on fertility rates"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – fertility first falls with development, and then rises with development. The question will be whether it will rise above an average of 2 children per woman."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world enters the last phase of the demographic transition and this means we will not repeat the past. The global population has quadrupled over the course of the 20th century, but it will not double anymore over the course of this century."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world population will reach a size that, compared to "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/world-population-growth#how-has-world-population-growth-changed-over-time"", ""children"": [{""text"": ""humanity’s history"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", will be extraordinary; if the UN projections are accurate (they have a "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/future-population-growth#how-accurate-have-past-population-projections-been"", ""children"": [{""text"": ""good track record"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""), the world population will have increased more than 10-fold over the span of 250 years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We are on the way to a new balance. The big global demographic transition that the world entered more than two centuries ago is then coming to an end. This new equilibrium is different from the one in the past when it was the very high mortality that kept population growth in check. In the new balance, it will be low fertility that keeps population changes small."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""61b443b151a3a653221d1a2e2d81c68503f8334f"": {""id"": ""61b443b151a3a653221d1a2e2d81c68503f8334f"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This was the annual figure in 2019, before the high mortality years of 2020 and 2021."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Two centuries of rapid global population growth will come to an end"", ""authors"": [""Max Roser"", ""Hannah Ritchie""], ""excerpt"": ""Global population has increased rapidly over the past century. This period of rapid growth is temporary: the world is entering a new equilibrium and rapid population growth is coming to an end."", ""dateline"": ""March 18, 2023"", ""subtitle"": ""Global population has increased rapidly over the past century. This period of rapid growth is temporary: the world is entering a new equilibrium and rapid population growth is coming to an end."", ""sidebar-toc"": false, ""featured-image"": ""FEATURED-IMAGE-World-Population-Growth.png""}",1,2023-12-02 02:15:04,2023-03-18 10:00:00,2024-03-18 15:41:59,listed,ALBJ4LtB0pjl569ltHLCXAyUNj-f0gm_3mSscZmc0aJS0hJIB0XgsGU3K1bZqj_1pIMD3-wCE08pS14tP6Eo0A,,"One of the big lessons from the demographic history of countries is that periods of rapid population growth are temporary. For many countries, the [demographic transition](https://ourworldindata.org/world-population-growth#the-demographic-transition-why-is-rapid-population-increase-a-temporary-phenomenon) has already ended, and as the [global fertility rate has now halved](https://ourworldindata.org/fertility-rate#the-global-fertility-rate-has-halved-in-the-last-50-years) we know that the world as a whole is approaching the end of rapid population growth. This visualization presents an overview of the global demographic transition, based on estimates from the [2022 data release from the UN Population Division](https://population.un.org/wpp/). As we explore at the beginning of the [topic page on population growth](https://ourworldindata.org/world-population-growth), the global population grew only very slowly up to 1700 – only 0.04% per year. In the many millennia up to that point in history [very high mortality of children](https://ourworldindata.org/child-mortality-in-the-past) counteracted high fertility. The world was in the first stage of the demographic transition. Once [health improved and mortality declined](https://ourworldindata.org/child-mortality-global-overview) things changed quickly. Particularly over the course of the 20th century: Over the last 100 years global population more than quadrupled. As we see in the chart, the rise of the global population got steeper and steeper and you have just lived through the steepest increase of that curve. This also means that your existence is a tiny part of the reason why that curve is so steep. The 7-fold increase of the world population over the course of two centuries amplified humanity’s impact on the natural environment. To provide space, food, and resources for a large world population in a way that is sustainable into the distant future is without question one of the large, serious challenges for our generation. We should not make the mistake of underestimating the task ahead of us. Yes, I expect new generations [to contribute](https://web.archive.org/web/20190315075445/https://www.bloomberg.com/opinion/articles/2019-03-14/want-to-help-fight-climate-change-have-more-children), but for now, it is upon us to provide for them. Population growth is still fast: every year, 134 million are born, and 58 million die.1 The difference is the number of people that we add to the world population in a year: 76 million. Where do we go from here? In pink, you see the annual population growth rate (that is, the percentage change in population per year) of the global population. It peaked around half a century ago. Peak population growth was reached in 1963 with an annual growth of 2.3%. Since then the increase of the world population has slowed and today grows by 0.9% per year. This slowdown of population growth was not only predictable but [predicted](https://ourworldindata.org/future-population-growth#how-accurate-have-past-population-projections-been). Just as expected by demographers, the world as a whole is experiencing the closing of a massive demographic transition. This chart also shows how the United Nations envision the end of the global demographic transition. As population growth continues to decline, the curve representing the world population is getting less and less steep. Towards the end of the century, the UN expects the global population to reach its peak at around 10.4 billion. After this point, the UN demographers project global population growth to become negative, so that the world population starts to fall slowly. It is hard to know the population dynamics beyond 2100. It will depend on the fertility rate and – as we discuss in [our entry on fertility rates](https://ourworldindata.org/fertility-rate#fertility-is-first-falling-with-development-and-then-rising-with-development) – fertility first falls with development, and then rises with development. The question will be whether it will rise above an average of 2 children per woman. The world enters the last phase of the demographic transition and this means we will not repeat the past. The global population has quadrupled over the course of the 20th century, but it will not double anymore over the course of this century. The world population will reach a size that, compared to [humanity’s history](https://ourworldindata.org/world-population-growth#how-has-world-population-growth-changed-over-time), will be extraordinary; if the UN projections are accurate (they have a [good track record](https://ourworldindata.org/future-population-growth#how-accurate-have-past-population-projections-been)), the world population will have increased more than 10-fold over the span of 250 years. We are on the way to a new balance. The big global demographic transition that the world entered more than two centuries ago is then coming to an end. This new equilibrium is different from the one in the past when it was the very high mortality that kept population growth in check. In the new balance, it will be low fertility that keeps population changes small. This was the annual figure in 2019, before the high mortality years of 2020 and 2021.",Two centuries of rapid global population growth will come to an end 1suQJtHnrIPA0zawZe1uCeM5rHZPOvUlZKq4fQnj41O0,smallholder-food-production,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""It is often claimed that smallholder farmers produce 70% or even 80% of the world’s food. This claim has even been made by the United Nations Food and Agriculture Organization (UN FAO)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It has been a linchpin for agricultural and development policies. But it is wrong. Recent studies suggest that this figure is too high: smallholder farmers produce around one-third of the world’s food, less than half of what these headlines claim."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A key problem is that some use the terms ‘family farms’ and ‘smallholder farms’ interchangeably. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Family"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" farms "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""do"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" produce around 80% of the world’s food. These farms can be of any size, and should not be confused with smallholders."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most (84%) of the world’s 570 million farms are smallholdings; that is, farms less than two hectares in size."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Many smallholder farmers are some of the "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/extreme-poverty"", ""children"": [{""text"": ""poorest people"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in the world. Tragically, and somewhat paradoxically, they are also those who often go hungry."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A shift towards small-scale farming can be an important stage of a country’s development, especially if it has a large working age population. But, it’s gruelling work with poor returns: small farms can achieve good yields but need lots of human labor and input."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Labor productivity is low."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is why countries move beyond a workforce of farmers: younger people get an education, "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/urbanization"", ""children"": [{""text"": ""move towards cities"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and try to secure a job with higher levels of productivity and income. A country cannot leave deep poverty behind when most of the population work as smallholder farmers."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The UN Food and Agriculture Organization (FAO) has made incorrect claims about the world’s reliance on smallholder farmers in the past. One of its reports states that “small-scale farmers produce over 70% of the world’s food needs.”"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In other reports it "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.donorplatform.org/publication-agenda-2030/un-decade-of-family-farming-2019-2028-global-action-plan.html"", ""children"": [{""text"": ""has said"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that smallholder and family farms (which raises issues of how these terms are defined) produce 70-80% of the world’s food."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This would mean that small farms produce nearly all of the world’s food. This has become a zombie statistic: one that has been repeated by many other organizations despite there being no evidence to support it."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A key problem is that organizations – including the UN FAO – often use the terms ‘small farms’ and ‘family farms’ interchangeably. But they cannot, and should not be. As we will see later, these definitions give us very different estimates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This confusion creates several problems. First, it creates a misunderstanding; one that might convince us that a world of smallholder farmers is what we need. If they produced nearly all of the world’s food, perhaps that is a future we would want to maintain. Second, it might make us concerned about the future of the global food system if countries move towards larger farms. As countries get richer, the average farm size "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/average-farm-size-vs-gdp"", ""children"": [{""text"": ""tends to increase"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". If nearly all of the world’s food came from small farms, perhaps we should be worried about this development."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Is this concern justified? Researchers provide us with a better answer to this question of how much of the world’s food smallholders really produce."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How much of the world’s food do smallholder farmers really produce?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Several studies have tried to answer this question. The most extensive and recent comes from the work of Vincent Ricciardi and colleagues."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" They produced the first open dataset on global food production, mapped by farm size."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It covers 154 crop types across 55 countries. It not only covers the amount produced across different farm sizes, but also the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""types"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of crops and what they are used for – whether they are eaten as food, used as animal feed, or for other uses such as biofuels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows their findings. This shows the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""cumulative"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" total of three metrics – agricultural land; crop production; and food supply – with increasing farm size. So the top row of bars show the global total across farms less than one hectare; the second bar shows farms up to two hectares etc."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""How-much-of-the-worlds-food-do-smallholders-produce.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Smallholder farms are those that are less than two hectares."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" That’s the top two bars, which are shaded in blue."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Smallholder farmers produce 29% of the world’s crops, measured in kilocalories."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Less than half of previous claims."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""They do so using around one-quarter (24%) of the world’s agricultural land. They account for a bit more crop production than land use because smaller farms tend to achieve higher yields."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This is very labor-intensive work; smaller farms get higher land productivity, but lower labor productivity."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These farms account for an even greater share of the world’s food supply – one-third (32%) of it."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This is because smaller farms tend to "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/farm-size#how-does-the-allocation-of-crops-vary-by-farm-size"", ""children"": [{""text"": ""allocate a larger share"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of their crops towards food, rather than animal feed or biofuels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To get to the 70-80% figure that was previously reported, we would need to include farms all the way up to 100, or even 200 hectares. These results shown here are in line with other studies which agree that the figure of 70-80% is much too high."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""So while one-third of the world’s food is still a large share, it’s less than half of the widely-cited claim."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The claim that "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""family "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""farms produce 70-80% of the world’s food "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""is"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" likely to be true. A recent study by Sarah Lowder, Marco Sanchez, and Raffaele Bertini agrees with the conclusion that small farms produce one-third of the world’s food."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" But they also estimate the share produced on family farms. The definition of a family farm is broad: it’s one that is operated by an individual or group of individuals, where most labor is supplied by the family. This means they can be of any size – many family farms are large. Orders of magnitude larger than our under-two-hectare smallholders."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""They find that family farms produce around 80% of the world’s food. To be clear: small farms produce one-third of the world’s food. Family farms – of any size – produce 80%. These terms should not be used interchangeably because they are very different."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Increasing the productivity of smallholder farming is a crucial step in countries transitioning from poverty to middle-incomes. Raising the output and incomes of smallholder farmers should be an important focus, even if they produced very little of the world’s food. This is because most of the world’s farms are smallholders, and they are some of the poorest people in the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We should avoid the romanticization of a future where most still spend their time working the fields for small returns. 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It made the claim that ‘peasants’ grow at least 70% of the world's food. How they got to this figure is not clear."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The UN FAO has also built many policy reports around this claim. The UN FAO declared 2014 the ‘Year of Family Farming’ with a focus on developing agricultural policies and support mechanisms for smallholder farmers. It later launched the UN decade of family farming, which runs from 2019 to 2028. What the definition of a ‘family farm’ is, is not clear."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One of the Sustainable Development Goals: Target 2.3 is to “Double the productivity and incomes of small-scale food producers” by 2030. 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Sometimes it’s defined as smallholder farms; other times as ‘family farms’; sometimes as food or crop production; and other times as agricultural land."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""ETC -group, 2009. Who Will Feed Us? Questions for the Food and Climate Crises."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Wolfenson, K. D. M. (2013). Coping with the food and agriculture challenge: smallholders’ agenda. Food and Agriculture Organisation of the United Nations, Rome.FAO, 2014. 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"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Global Food Security"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", 17, 64-72."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f09bf2e5126410adb04a9087fe49893a729bbacc"": {""id"": ""f09bf2e5126410adb04a9087fe49893a729bbacc"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This is the universal standard for smallholder farms, but of course, two hectares is a somewhat arbitrary cut-off."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f11c3060eb5acd26ef87444610b13956c00d46ce"": {""id"": ""f11c3060eb5acd26ef87444610b13956c00d46ce"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Ricciardi, V., Mehrabi, Z., Wittman, H., James, D., & Ramankutty, N. (2021). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.nature.com/articles/s41893-021-00699-2"", ""children"": [{""text"": ""Higher yields and more biodiversity on smaller farms"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Nature Sustainability"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", 1-7."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Smallholders produce one-third of the world’s food, less than half of what many headlines claim"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""Most of the world's farmers are smallholders. They are also often the poorest. How much of the world's food do they produce?"", ""dateline"": ""August 6, 2021"", ""subtitle"": ""Most of the world's farmers are smallholders. They are also often the poorest. How much of the world's food do they produce?"", ""featured-image"": ""smallholders-thumbnail.png""}",1,2023-07-28 12:36:28,2021-08-06 10:00:00,2024-03-18 15:41:59,listed,ALBJ4LvL7skpKcB8ZOK0NxeGKslygciXXdNqNDLba-0vWpeBYvfa0yDxq3Ysw1S-SXGnqy4mo7_nHs1RSeWSIg,,"** **It is often claimed that smallholder farmers produce 70% or even 80% of the world’s food. This claim has even been made by the United Nations Food and Agriculture Organization (UN FAO). It has been a linchpin for agricultural and development policies. But it is wrong. Recent studies suggest that this figure is too high: smallholder farmers produce around one-third of the world’s food, less than half of what these headlines claim. A key problem is that some use the terms ‘family farms’ and ‘smallholder farms’ interchangeably. _Family_ farms _do_ produce around 80% of the world’s food. These farms can be of any size, and should not be confused with smallholders. Most (84%) of the world’s 570 million farms are smallholdings; that is, farms less than two hectares in size.1 Many smallholder farmers are some of the [poorest people](http://ourworldindata.org/extreme-poverty) in the world. Tragically, and somewhat paradoxically, they are also those who often go hungry. A shift towards small-scale farming can be an important stage of a country’s development, especially if it has a large working age population. But, it’s gruelling work with poor returns: small farms can achieve good yields but need lots of human labor and input.2 Labor productivity is low. This is why countries move beyond a workforce of farmers: younger people get an education, [move towards cities](http://ourworldindata.org/urbanization), and try to secure a job with higher levels of productivity and income. A country cannot leave deep poverty behind when most of the population work as smallholder farmers. The UN Food and Agriculture Organization (FAO) has made incorrect claims about the world’s reliance on smallholder farmers in the past. One of its reports states that “small-scale farmers produce over 70% of the world’s food needs.”3 In other reports it [has said](https://www.donorplatform.org/publication-agenda-2030/un-decade-of-family-farming-2019-2028-global-action-plan.html) that smallholder and family farms (which raises issues of how these terms are defined) produce 70-80% of the world’s food.4 This would mean that small farms produce nearly all of the world’s food. This has become a zombie statistic: one that has been repeated by many other organizations despite there being no evidence to support it.5 A key problem is that organizations – including the UN FAO – often use the terms ‘small farms’ and ‘family farms’ interchangeably. But they cannot, and should not be. As we will see later, these definitions give us very different estimates. This confusion creates several problems. First, it creates a misunderstanding; one that might convince us that a world of smallholder farmers is what we need. If they produced nearly all of the world’s food, perhaps that is a future we would want to maintain. Second, it might make us concerned about the future of the global food system if countries move towards larger farms. As countries get richer, the average farm size [tends to increase](https://ourworldindata.org/grapher/average-farm-size-vs-gdp). If nearly all of the world’s food came from small farms, perhaps we should be worried about this development. Is this concern justified? Researchers provide us with a better answer to this question of how much of the world’s food smallholders really produce. # How much of the world’s food do smallholder farmers really produce? Several studies have tried to answer this question. The most extensive and recent comes from the work of Vincent Ricciardi and colleagues.6 They produced the first open dataset on global food production, mapped by farm size.7 It covers 154 crop types across 55 countries. It not only covers the amount produced across different farm sizes, but also the _types_ of crops and what they are used for – whether they are eaten as food, used as animal feed, or for other uses such as biofuels. The chart shows their findings. This shows the _cumulative_ total of three metrics – agricultural land; crop production; and food supply – with increasing farm size. So the top row of bars show the global total across farms less than one hectare; the second bar shows farms up to two hectares etc. Smallholder farms are those that are less than two hectares.8 That’s the top two bars, which are shaded in blue. Smallholder farmers produce 29% of the world’s crops, measured in kilocalories.9 Less than half of previous claims. They do so using around one-quarter (24%) of the world’s agricultural land. They account for a bit more crop production than land use because smaller farms tend to achieve higher yields.2 This is very labor-intensive work; smaller farms get higher land productivity, but lower labor productivity. These farms account for an even greater share of the world’s food supply – one-third (32%) of it.10 This is because smaller farms tend to [allocate a larger share](https://ourworldindata.org/farm-size#how-does-the-allocation-of-crops-vary-by-farm-size) of their crops towards food, rather than animal feed or biofuels. To get to the 70-80% figure that was previously reported, we would need to include farms all the way up to 100, or even 200 hectares. These results shown here are in line with other studies which agree that the figure of 70-80% is much too high.11 So while one-third of the world’s food is still a large share, it’s less than half of the widely-cited claim. The claim that _family _farms produce 70-80% of the world’s food _is_ likely to be true. A recent study by Sarah Lowder, Marco Sanchez, and Raffaele Bertini agrees with the conclusion that small farms produce one-third of the world’s food.12 But they also estimate the share produced on family farms. The definition of a family farm is broad: it’s one that is operated by an individual or group of individuals, where most labor is supplied by the family. This means they can be of any size – many family farms are large. Orders of magnitude larger than our under-two-hectare smallholders. They find that family farms produce around 80% of the world’s food. To be clear: small farms produce one-third of the world’s food. Family farms – of any size – produce 80%. These terms should not be used interchangeably because they are very different. Increasing the productivity of smallholder farming is a crucial step in countries transitioning from poverty to middle-incomes. Raising the output and incomes of smallholder farmers should be an important focus, even if they produced very little of the world’s food. This is because most of the world’s farms are smallholders, and they are some of the poorest people in the world. We should avoid the romanticization of a future where most still spend their time working the fields for small returns. That would be a future where hundreds of millions continue to live in poverty. --- Lowder, S. K., Skoet, J., & Raney, T. (2016). [The number, size, and distribution of farms, smallholder farms, and family farms worldwide](https://www.sciencedirect.com/science/article/pii/S0305750X15002703). _World Development_, 87, 16-29. Ricciardi, V., Mehrabi, Z., Wittman, H., James, D., & Ramankutty, N. (2021). [Higher yields and more biodiversity on smaller farms](https://www.nature.com/articles/s41893-021-00699-2). _Nature Sustainability_, 1-7. Wolfenson, K. D. M. (2013). Coping with the food and agriculture challenge: smallholders’ agenda. Food and Agriculture Organisation of the United Nations, Rome. FAO, 2014. The State of Food and Agriculture 2014: Innovation in Family Farming Food and Agriculture Organization of the United Nations. The first UN report to make this claim seems to cite a 2009 report by the environmental activist organization, [ETC group](https://en.wikipedia.org/wiki/ETC_Group_(eco-justice)). It made the claim that ‘peasants’ grow at least 70% of the world's food. How they got to this figure is not clear. The UN FAO has also built many policy reports around this claim. The UN FAO declared 2014 the ‘Year of Family Farming’ with a focus on developing agricultural policies and support mechanisms for smallholder farmers. It later launched the UN decade of family farming, which runs from 2019 to 2028. What the definition of a ‘family farm’ is, is not clear. One of the Sustainable Development Goals: Target 2.3 is to “Double the productivity and incomes of small-scale food producers” by 2030. The Paris climate agreement includes important clauses on mitigation and adaptation support for small-scale farmers. The National Geographic [repeated it](https://www.nationalgeographic.com/environment/article/photos-farms-agriculture-national-farmers-day). Even the multinational company, Bayer, used it to [make similar claims](https://twitter.com/bayer/status/1053178962560589824). Not only has it been repeated, its definition has also been stretched along the way. Sometimes it’s defined as smallholder farms; other times as ‘family farms’; sometimes as food or crop production; and other times as agricultural land. ETC -group, 2009. Who Will Feed Us? Questions for the Food and Climate Crises. Wolfenson, K. D. M. (2013). Coping with the food and agriculture challenge: smallholders’ agenda. Food and Agriculture Organisation of the United Nations, Rome.FAO, 2014. The State of Food and Agriculture 2014: Innovation in Family Farming Food and Agriculture Organization of the United Nations. Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., & Chookolingo, B. (2018). [How much of the world's food do smallholders produce?](https://www.sciencedirect.com/science/article/pii/S2211912417301293). _Global Food Security_, 17, 64-72. Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., & Chookolingo, B. (2018). An open-access dataset of crop production by farm size from agricultural censuses and surveys. _Data in brief_, 19, 1970-1988. This is the universal standard for smallholder farms, but of course, two hectares is a somewhat arbitrary cut-off. The authors provide confidence intervals of 28% to 31%. The authors provide confidence intervals of 30% to 34%. Herrero, M., Thornton, P. K., Power, B., Bogard, J. R., Remans, R., Fritz, S., ... & Havlík, P. (2017). [Farming and the geography of nutrient production for human use: a transdisciplinary analysis](https://www.sciencedirect.com/science/article/pii/S2542519617300074). _The Lancet Planetary Health_, 1(1), e33-e42. Samberg, L. H., Gerber, J. S., Ramankutty, N., Herrero, M., & West, P. C. (2016). [Subnational distribution of average farm size and smallholder contributions to global food production](https://iopscience.iop.org/article/10.1088/1748-9326/11/12/124010/meta). _Environmental Research Letters_, 11(12), 124010. Graeub, B. E., Chappell, M. J., Wittman, H., Ledermann, S., Kerr, R. B., & Gemmill-Herren, B. (2016). [The state of family farms in the world](https://www.sciencedirect.com/science/article/pii/S0305750X15001217). _World Development_, 87, 1-15. Lowder, S. K., Sánchez, M. V., & Bertini, R. (2021). [Which farms feed the world and has farmland become more concentrated?](https://www.sciencedirect.com/science/article/pii/S0305750X2100067X). _World Development_, _142_, 105455.","Smallholders produce one-third of the world’s food, less than half of what many headlines claim" 1stO0TN11i5tDFMD9EUawQqC5lNQfueyRmimUsGSij-o,population-growth-over-time,article,"{""toc"": [{""slug"": ""how-has-the-world-population-growth-rate-changed"", ""text"": ""How has the world population growth rate changed?"", ""title"": ""How has the world population growth rate changed?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""other-ways-to-visualize-population-growth"", ""text"": ""Other ways to visualize population growth"", ""title"": ""Other ways to visualize population growth"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-long-did-it-take-for-the-world-population-to-double"", ""text"": ""How long did it take for the world population to double?"", ""title"": ""How long did it take for the world population to double?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""how-long-did-it-take-for-the-world-population-to-increase-by-one-billion"", ""text"": ""How long did it take for the world population to increase by one billion?"", ""title"": ""How long did it take for the world population to increase by one billion?"", ""supertitle"": """", ""isSubheading"": true}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""The world population has changed dramatically over the last few centuries. Let’s examine long-run population data to understand this change and how quickly the world’s population is growing today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows the increasing number of people living on our planet over the last 12,000 years. This is a mind-boggling change: the world population today is around 2,000 times the size of what it was 12,000 ago when it was around 4 million — less than half of the current population of London."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What is striking about this chart is, of course, that almost all of this growth happened just very recently. Historical demographers estimate that around 1800, the world population was only around 1 billion people. This implies that, on average, the population grew very slowly over this long time from 10,000 BCE to 1700 (by 0.04% annually). After 1800, this changed fundamentally: the world population was around 1 billion in 1800 and is now around 8 billion — 8 times larger."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Around 108 billion people have ever lived on our planet. This means that today’s population size makes up 6.5% of the total number of people ever born."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For a long period from the appearance of modern Homo sapiens up to the starting point of this chart in 10,000 BCE, it is estimated that the total world population was often well under one million."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In this period, our species was often seriously threatened by extinction."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can explore the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/world-population-1750-2015-and-un-projection-until-2100"", ""children"": [{""text"": ""interactive visualization"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of this change."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""world-population-10000BC.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""How has the world population growth rate changed?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We looked at the absolute change in the global population over time. But what about the rate of population growth?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The global population growth rate peaked long ago. The chart shows that global population growth peaked in 1962 and 1963 with an annual growth rate of 2.2%; however, since then, world population growth has halved."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For the last half-century, the population growth rate has been declining. The UN projects that this decline will continue in the coming decades."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A common question we’re asked is: is the global population growing exponentially? The answer is no."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For population growth to be exponential, the growth rate would have to be the same over time (e.g., 2% growth yearly). In absolute terms, this would result in an exponential increase in the number of people. That’s because we’d be multiplying an ever-larger number of people by the same 2%. 2% of this year’s population would be larger than 2% last year, and so on; this means the population would grow exponentially."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, as this chart shows, the growth rate has been falling since the 1960s. This means the world population is not growing exponentially."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""2019-Revision-–-World-Population-Growth-1700-2100.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""Other ways to visualize population growth"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""How long did it take for the world population to double?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are a few other ways to contextualize this period of population growth."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this chart, we show the time it took the world population to double. On the vertical axis, we have the years it took to double; on the horizontal axis, we have the year that a population level was reached. Hover over each point to see the population change (for example, from 0.5 to 1 billion). Note that this uses data from the 2019 revision of the UN’s World Population Prospects: we are working on an updated version with the latest data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the past, the population grew slowly: it took nearly seven centuries for the population to double from 0.25 billion (in the early 9th century) to 0.5 billion in the middle of the 16th century. As the growth rate slowly climbed, the population doubling time fell but remained in the order of centuries into the first half of the 20th century. Things sped up considerably in the middle of the 20th century."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The fastest doubling of the world population happened between 1950 and 1987: from 2.5 to 5 billion people in just 37 years — the population doubled within a little more than one generation. This period was marked by a peak population growth of 2.1% in 1962."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since then, population growth has been slowing, and the doubling time alongside it. In this visualization, we have used the UN projections to show how the doubling time is projected to change until the end of this century. By the 2080s, it will once again have taken approximately 100 years for the population to double to a predicted 10.4 billion."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""Time-it-took-for-the-world-population-to-double.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""How long did it take for the world population to increase by one billion?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This visualization provides an additional perspective on population growth: the number of years it took to add one billion to the global population. This is based on the 2022 revision of world population estimates from the UN Population Division."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This visualization shows again how the population growth rate has changed dramatically over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It wasn’t until 1805 that the world reached its first billion; it then took another 120 years to reach two billion. By the third billion, this period had reduced to 35 years, reduced further to 14 years to reach four. The fastest growth period occurred from 1974 to 2011, taking only 12 to 13 years to increase by one billion for the 5th, 6th, and 7th."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world has now surpassed this peak rate of growth, and the period between each billion is expected to continue rising. It’s estimated to take approximately 14 years to reach nine billion in 2037 and 21 years to reach 10 billion in 2058."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The latest UN medium projection expects that the world will not reach 11 billion people this century: it projects the population to peak at 10.4 billion in 2086 before falling again."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""Time-taken-to-increase-population-by-one-billion.png"", ""hasOutline"": false, ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""3b9fd5e83245e8e518dfcf1030e78d440029d2f1"": {""id"": ""3b9fd5e83245e8e518dfcf1030e78d440029d2f1"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See, for example, Kremer (1993) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://faculty.econ.ucdavis.edu/faculty/gclark/210a/readings/kremer1993.pdf"", ""children"": [{""text"": ""Population growth and technological change: one million BC to 1990"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". In the Quarterly Journal of Economics, Vol. 108, No. 3, 681-716."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""5ea8c4653ef8efa24f98e5f8612dee358fde8545"": {""id"": ""5ea8c4653ef8efa24f98e5f8612dee358fde8545"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""As per 2011 estimates from Carl Haub (2011), “"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.prb.org/howmanypeoplehaveeverlivedonearth/"", ""children"": [{""text"": ""How Many People Have Ever Lived on Earth?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” Population Reference Bureau."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""6a392391e65032ac964da4b0e9d8eb593271063c"": {""id"": ""6a392391e65032ac964da4b0e9d8eb593271063c"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This data comes from the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://population.un.org/wpp/"", ""children"": [{""text"": ""UN World Population Division"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a8d69411057d24592045d24b5ec7669ff5cee382"": {""id"": ""a8d69411057d24592045d24b5ec7669ff5cee382"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""John Hawks, Keith Hunley, Sang-Hee Lee, Milford Wolpoff; "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1093/oxfordjournals.molbev.a026233"", ""children"": [{""text"": ""Population Bottlenecks and Pleistocene Human Evolution"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", Molecular Biology and Evolution, Volume 17, Issue 1, 1 January 2000, Pages 2–22."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""How has world population growth changed over time?"", ""authors"": [""Max Roser"", ""Hannah Ritchie""], ""excerpt"": ""The world population has increased rapidly in recent centuries. But this is slowing."", ""dateline"": ""June 1, 2023"", ""subtitle"": ""The world population has increased rapidly in recent centuries. But this is slowing."", ""featured-image"": ""Population-Featured-Image.png""}",1,2023-07-07 10:32:40,2023-06-01 11:45:15,2023-12-28 16:31:11,unlisted,ALBJ4LsmloLgpzOe9oFIYhxjg-Ae8HOvhXbshRjbDC7sijq6P1xQ-iaKpktUpDoei18frugzzZUjkVTP64D7WA,,"The world population has changed in dramatic ways over the last few centuries. Let’s take a look at long-run data on population to understand this change, and how quickly the world population is growing today. The chart shows the increasing number of people living on our planet over the last 12,000 years. A mind-boggling change: the world population today is around 2,000 times the size of what it was 12,000 ago when the world population was around 4 million – less than half of the current population of London. What is striking about this chart is of course that almost all of this growth happened just very recently. Historical demographers estimate that around the year 1800 the world population was only around 1 billion people. This implies that on average the population grew very slowly over this long time from 10,000 BCE to 1700 (by 0.04% annually). After 1800 this changed fundamentally: the world population was around 1 billion in the year 1800 and is now, at around 8 billion, 8 times larger. Around 108 billion people have ever lived on our planet. This means that today’s population size makes up 6.5% of the total number of people ever born.1 For the long period from the appearance of modern Homo sapiens up to the starting point of this chart in 10,000 BCE it is estimated that the total world population was often well under one million.2 In this period our species was often seriously threatened by extinction.3 You can explore the [interactive visualization](https://ourworldindata.org/grapher/world-population-1750-2015-and-un-projection-until-2100) of this change. ## How has the world population growth rate changed? We looked at the absolute change in the global population over time. But what about the rate of population growth? The global population growth rate peaked long ago. The chart shows that global population growth reached a peak in 1962 and 1963 with an annual growth rate of 2.2%; but since then, world population growth has halved.4 For the last half-century we have lived in a world in which the population growth rate has been declining. The UN projects that this decline will continue in the coming decades. A common question we’re asked is: is the global population growing exponentially? The answer is no. For population growth to be exponential, the growth rate would have be the same over time (e.g. 2% growth every year). In absolute terms, this would result in an exponential increase in the number of people. That’s because we’d be multiplying an ever-larger number of people by the same 2%. 2% of the population this year would be larger than 2% last year, and so on; this means the population would grow exponentially. But, as we see in this chart, since the 1960s the growth rate has been falling. This means the world population is not growing exponentially. ## Other ways to visualize population growth ### How long did it take for the world population to double? There are few other ways to contextualize this period of population growth. In this chart we show the time it took the world population to double. On the y-axis we have the number of years to double; on the x-axis we have the year that a population level was reached. Hover over each point to see the population change (for example, from 0.5 to 1 billion). In the past the population grew slowly: it took nearly seven centuries for the population to double from 0.25 billion (in the early 9th century) to 0.5 billion in the middle of the 16th century. As the growth rate slowly climbed, the population doubling time fell but remained in the order of centuries into the first half of the 20th century. Things sped up considerably in the middle of the 20th century. The fastest doubling of the world population happened between 1950 and 1987: a doubling from 2.5 to 5 billion people in just 37 years — the population doubled within a little more than one generation. This period was marked by a peak population growth of 2.1% in 1962. Since then, population growth has been slowing, and along with it the doubling time. In this visualisation we have used the UN projections to show how the doubling time is projected to change until the end of this century. By the 2080s, it will once again have taken approximately 100 years for the population to double to a predicted 10.4 billion. ### How long did it take for the world population to increase by one billion? This visualization provides an additional perspective on population growth: the number of years it took to add one billion to the global population. This is based on the 2022 revision of world population estimates from the UN Population Division. This visualization shows again how the population growth rate has changed dramatically over time. It wasn’t until 1805 that the world reached its first billion; it then took another 120 years to reach two billion. By the third billion, this period had reduced to 35 years, reduced further to 14 years to reach four. The period of fastest growth occurred from 1974 to 2011, taking only 12 to 13 years to increase by one billion for the 5th, 6th, and 7th. The world has now surpassed this peak rate of growth, and the period between each billion is expected to continue to rise. It’s estimated to take approximately 14 years to reach nine billion in 2024, and a further 21 years to reach 10 billion in 2058. The latest UN medium projection expects that the world will not reach 11 billion people this century: it projects the population to peak at 10.4 billion in 2086 before falling again. See for example Kremer (1993) – [Population growth and technological change: one million BC to 1990](https://faculty.econ.ucdavis.edu/faculty/gclark/210a/readings/kremer1993.pdf). In the Quarterly Journal of Economics, Vol. 108, No. 3, 681-716. As per 2011 estimates from Carl Haub (2011), “[How Many People Have Ever Lived on Earth?](https://www.prb.org/howmanypeoplehaveeverlivedonearth/)” Population Reference Bureau. This data comes from the [UN World Population Division](https://population.un.org/wpp/). John Hawks, Keith Hunley, Sang-Hee Lee, Milford Wolpoff; [Population Bottlenecks and Pleistocene Human Evolution](https://doi.org/10.1093/oxfordjournals.molbev.a026233), Molecular Biology and Evolution, Volume 17, Issue 1, 1 January 2000, Pages 2–22.",How has world population growth changed over time? 1srs-VlnCNd1lMZBiuzNRUBsT41wWnGWddFRMiZ0jNaE,causes-of-death,topic-page,"{""toc"": [], ""body"": [{""type"": ""topic-page-intro"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""What are people dying from?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This question is essential to guide decisions in public health, and find ways to save lives."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Many leading causes of death receive little mainstream attention. If news reports reflected what children died from, they would say that around 1,400 young children die from diarrheal diseases, 1,000 die from malaria, and 1,900 from respiratory infections – "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""every day"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This can change. Over time, death rates from these causes have declined across the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A better understanding of the causes of death has led to the development of technologies, preventative measures, and better healthcare, reducing the chances of dying from a wide range of different causes, across all age groups."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the past, infectious diseases dominated. But death rates from infectious diseases have fallen quickly – faster than other causes. This has led to a shift in the leading causes of death. Now, non-communicable diseases – such as "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/cardiovascular-diseases"", ""children"": [{""text"": ""cardiovascular diseases"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/cancer"", ""children"": [{""text"": ""cancers"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – are the most common causes of death globally."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""More progress is possible, and the impact of causes of death can fall further."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On this page, you will find global data and research on leading causes of death and how they can be prevented. This includes the number of people dying from each cause, their death rates, how they differ between age groups, and their trends over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This data can also help understand the burden of disease more broadly, and offer a lens to see the impacts of healthcare and medicine, habits and behaviours, environmental factors, health infrastructure, and more."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": [], ""relatedTopics"": [{""url"": ""https://ourworldindata.org/life-expectancy"", ""text"": ""Life expectancy"", ""type"": ""topic-page-intro-related-topic""}, {""url"": ""https://ourworldindata.org/child-mortality"", ""text"": ""Child and infant mortality"", ""type"": ""topic-page-intro-related-topic""}, {""url"": ""https://ourworldindata.org/maternal-mortality"", ""text"": ""Maternal mortality"", ""type"": ""topic-page-intro-related-topic""}, {""url"": ""https://ourworldindata.org/burden-of-disease"", ""text"": ""Burden of disease"", ""type"": ""topic-page-intro-related-topic""}]}, {""type"": ""key-insights"", ""heading"": ""Key Insights on Causes of Death"", ""insights"": [{""type"": ""key-insight-slide"", ""title"": ""Globally, non-communicable diseases are the most common causes of death"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The chart shows what people died from globally, in 2019. Each box represents one cause, and its size is proportional to the number of deaths it caused."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The most common causes of death globally — shown in blue — were from ‘non-communicable diseases’."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This includes "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/cardiovascular-diseases"", ""children"": [{""text"": ""cardiovascular diseases"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/cancer"", ""children"": [{""text"": ""cancer"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and chronic respiratory diseases. They tend to develop gradually over time and aren’t infectious themselves."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Heart diseases were the most common cause, responsible for a third of all deaths globally. Cancers were in second, causing almost one-in-five deaths. Taken together, heart diseases and cancers are the cause of every second death."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In red are infectious diseases, which are responsible for around 1-in-7 deaths. These include pneumonia, diarrheal diseases, tuberculosis, HIV/AIDS, and malaria."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A smaller share – around 4% – was from neonatal and maternal deaths. A similar share was from accidents."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Violent deaths were less common, with 1.3% dying from suicide and less than 1% dying from interpersonal violence such as homicide or battle deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this article, we cover this in more detail:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1UdKf375jChVw4SpFrhNaRJH0Ve7VDXtjFz_CP7mon54/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""text"": [{""text"": [{""text"": ""What you should know about this data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 5, ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""This data comes from the most recent publication of the Global Burden of Disease study by the Institute for Health Metrics and Evaluation (IHME) in 2019 and the Global Terrorism Database."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These estimates assign each death a single cause, based on data on the ‘underlying cause of death’ listed on death certificates, verbal autopsies, and statistical modeling. This is a simplification, as people often have multiple diseases or injuries that contribute to their death, which may also be listed on death certificates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart shows data on causes of death globally for 2019, the year before the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/coronavirus"", ""children"": [{""text"": ""Covid-19 pandemic"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" started."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}], ""filename"": ""causes-of-death-2019-full.png""}, {""type"": ""key-insight-slide"", ""title"": ""Millions of young children die from preventable causes each year"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Every child’s death is a tragedy. Globally, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/child-mortality-big-problem-in-brief"", ""children"": [{""text"": ""the scale of child mortality is immense"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "": five million children under five die yearly. That’s around 14,000 each day."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, you can see what they die from. The size of each box corresponds to the number of children under five years old who die from each cause."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The most common causes for young children are different from the leading causes across the entire population – which was shown in the previous key insight."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Infectious diseases were most common – they kill an estimated 2.2 million children annually. They include pneumonia, diarrheal diseases, malaria, and meningitis."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Next were birth disorders, such as preterm birth, neonatal asphyxia (suffocation), and trauma, which caused an estimated 1.9 million child deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Several other causes, such as heart abnormalities and malnutrition, were responsible for around 100,000 deaths each."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These figures are astonishing because many of these causes are preventable. With vaccination, basic medication, rehydration treatment, nutrition supplementation, and neonatal healthcare, a large share of child deaths could be prevented."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this article, we cover this in more detail:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/what-are-children-dying-from-and-what-can-we-do-about-it"", ""type"": ""prominent-link"", ""title"": ""What are children dying from and what can we do about it?"", ""thumbnail"": ""Child-deaths-treemap-smaller.jpg"", ""description"": ""Here we look at the number of children dying by each cause – from pneumonia to diarrheal diseases, malaria and malnutrition. We also present the range of interventions that are available to prevent children from dying."", ""parseErrors"": []}, {""text"": [{""text"": [{""text"": ""What you should know about this data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 5, ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""This data comes from the most recent publication by the Institute for Health Metrics and Evaluation (IHME) in 2019 (at the time of writing in August 2023)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These estimates assign each death a single cause, based on data on the ‘underlying cause of death’ listed on death certificates, verbal autopsies, and statistical modeling. This is a simplification, as people often have multiple diseases or injuries that contribute to their death, which may also be listed on death certificates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart shows data on causes of death globally for 2019, the year before the Covid-19 pandemic started."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}], ""filename"": ""causes-of-death-children-under-5-treemap-final.png""}, {""type"": ""key-insight-slide"", ""title"": ""Causes of death have changed over time and vary by age"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""What people die from has changed dramatically over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, you can see long-run historical trends in death rates in France."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This is shown across age groups (on the y-axis) between 1925 and 1999 (on the x-axis). The colors represent the death rates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As you can see, the rates of some causes of death rise exponentially with age; the shades are much darker along the vertical axis."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Take respiratory diseases as an example. In 1999, people aged 40 to 44 had a death rate 24 times higher than those aged 20 to 24. For those aged 60 to 64, it was almost 440 times higher."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In contrast, external causes, which include accidents, violence, falls, and suicides, tend to rise more slowly with age. The shades get darker slowly along the vertical axis."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Causes of death have also changed "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""over time"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The effects of major events – such as World War Two and the AIDS epidemic – are visible on the chart. They led to large surges in death rates from external causes and infectious diseases."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another example is that death rates from infectious diseases and respiratory diseases have declined over time – especially from the mid-20th century onwards, with the rise of antibiotics, vaccines, and public healthcare."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": [{""text"": ""What you should know about this data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 5, ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""The underlying data for this chart comes from the Institut National d'Études Démographiques, which covers causes of death nationally in France between 1925 and 1999."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Causes of death were categorized into categories according to the 9th edition of the International Classification of Diseases (ICD-9) manual. Data from recent years comes from the 10th edition of the International Classification of Diseases (ICD-10) manual and has not been harmonized with older data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Annual mortality rates are shown for five-year age bands."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This data has been processed by Our World in Data. To recreate this chart, you can find "", ""spanType"": ""span-simple-text""}, {""url"": ""https://github.com/owid/notebooks/tree/main/SaloniDattani/Causes-of-death/Mortality-by-cause-age-1925-1999-France"", ""children"": [{""text"": ""scripts"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" here."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}], ""filename"": ""cause-of-death-age-and-period-mortality-rate-France.png""}, {""url"": ""https://ourworldindata.org/grapher/death-rate-from-communicable-vs-non-communicable-diseases?country=African+Region+%28WHO%29~European+Region+%28WHO%29~Region+of+the+Americas+%28WHO%29~South-East+Asia+Region+%28WHO%29~Western+Pacific+Region+%28WHO%29~Eastern+Mediterranean+Region+%28WHO%29"", ""type"": ""key-insight-slide"", ""title"": ""Death rates from communicable and non-communicable disease vary widely around the world"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Some causes of death are far more common in some parts of the world than others."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In poorer countries in Africa and Asia – where "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/water-access"", ""children"": [{""text"": ""clean water"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/sanitation"", ""children"": [{""text"": ""sanitation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and access to healthcare are lacking – people are much more likely to die from infectious diseases, maternal, neonatal, and nutritional causes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, in wealthier countries, people are much more likely to die from 'non-communicable diseases' instead, which include heart diseases and cancers."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is because of two related points."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""First, infectious diseases are much more common in poorer countries, and treatment is often lacking."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Second, deaths from infectious diseases were much more common in wealthier countries in the past. As these causes of death are reduced or eliminated, people tend to live longer and die from other causes instead."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Therefore, the data needs to be ‘"", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/age-standardization"", ""children"": [{""text"": ""age-standardized"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""’ to see how causes of death vary between countries, among people of the same age. In the chart, you can see an age-standardized comparison of these causes of death."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As you can see, countries with higher death rates from communicable diseases "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""also"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" tend to have higher death rates from non-communicable diseases."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This results from poorer healthcare, income, and living standards, which affect the chances of surviving many kinds of diseases."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can also move the slider to see how they have "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/death-rate-from-communicable-vs-non-communicable-diseases?time=1990..latest"", ""children"": [{""text"": ""shifted over time"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Annual death rates have been reduced over time for both categories – but they have dropped faster for communicable diseases."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": [{""text"": ""What you should know about this data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 5, ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""This data comes from the publication by the Institute for Health Metrics and Evaluation (IHME) in 2019."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These estimates assign each death a single cause, based on data on the ‘underlying cause of death’ listed on death certificates, verbal autopsies, and statistical modeling. This is a simplification, as people often have multiple diseases or injuries that contribute to their death, which may also be listed on death certificates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart shows data on causes of death globally for 2019, the year before the Covid-19 pandemic started."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}]}, {""url"": ""https://ourworldindata.org/grapher/share-of-deaths-cause-is-registered"", ""type"": ""key-insight-slide"", ""title"": ""Underlying data on causes of death is limited in many countries"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The most common way to track and understand causes of death is to rely on data from death certificates – where doctors describe the chain of events that led to each person’s death and the disease or injury that caused it."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These are registered in Vital Registration systems and shared with the World Health Organization annually."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, in many countries, this data is lacking. You can see this in the chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is because of several factors – a lack of doctors, nurses, medical records, and hospitals, and a poorly functioning Vital Registration system."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Instead, our understanding of causes of death in poor countries often comes from other studies, such as ‘verbal autopsies’, which are not conducted regularly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To improve our understanding of causes of death worldwide, we need better-functioning Vital Registration systems, medical records, and training for doctors and nurses to collect data where it’s lacking."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this article, we cover this in more detail:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/how-are-causes-of-death-registered-around-the-world"", ""type"": ""prominent-link"", ""title"": ""How are causes of death registered around the world?"", ""thumbnail"": ""Cause-of-death-registration-thumbnail.png"", ""description"": ""When people die, the cause of their death is usually officially registered in their country’s national system. How is the cause determined?"", ""parseErrors"": []}, {""text"": [{""text"": [{""text"": ""What you should know about this data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 5, ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""The number of deaths in each country is estimated based on data from censuses, household surveys, and historical trends."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}]}, {""url"": ""https://ourworldindata.org/grapher/number-of-deaths-by-risk-factor"", ""type"": ""key-insight-slide"", ""title"": ""A range of risk factors affect the chances of death"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""In the data we present on causes of death, we show each death as caused by a "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""single"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" disease, event, or injury."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, people have often been exposed to various risk factors earlier in life, affecting their chances of premature death."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These risk factors can include behaviors – such as "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/smoking"", ""children"": [{""text"": ""smoking"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and low exercise – and environmental factors, such as "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/air-pollution"", ""children"": [{""text"": ""air pollution"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/water-access"", ""children"": [{""text"": ""unsafe water"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and pathogens."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Researchers try to estimate the number of deaths caused by each risk factor, using available data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For example, researchers estimate the number of deaths that could be prevented if no one smoked, by using estimates of the risk caused by smoking, and the levels of smoking in the population."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, you can see the estimated number of deaths caused by a range of risk factors."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows that risk factors such as high blood pressure, smoking, and air pollution are each estimated to cause millions of deaths yearly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These risk factors are not exclusive: people can be exposed to multiple risk factors, and some are related to each other. Therefore, the numbers do not sum up to the total number of deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this article, we cover this in more detail:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/how-do-researchers-estimate-the-death-toll-caused-by-each-risk-factor-whether-its-smoking-obesity-or-air-pollution"", ""type"": ""prominent-link"", ""title"": ""How do researchers estimate the death toll caused by each risk factor, whether it’s smoking, obesity or air pollution?"", ""thumbnail"": ""risk-ratios-thumbnail.png"", ""description"": ""Risk factors are important to understand because they can help us identify how to save lives. How do researchers estimate their impact?"", ""parseErrors"": []}, {""text"": [{""text"": [{""text"": ""What you should know about this data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 5, ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""These estimates come from the Global Burden of Disease study by the Institute for Health Metrics and Evaluation (IHME) in 2019."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The researchers estimate the number of deaths caused by each risk factor in several steps. First, they estimate the increased mortality risk caused by each risk factor. This is measured relative to a theoretical minimum (for example, the absence of the risk factor or its reduction to an optimum level). Next, they estimate the number of people exposed to the risk factor. Finally, they combine these to estimate the number of deaths caused by the risk factor."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The number of deaths attributed to each risk factor does not sum up to the total number of deaths. This is because risk factors are not mutually exclusive: people may be exposed to multiple risk factors, and the number of deaths caused by each risk factor is calculated separately."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the linked article above, this is explained in more detail."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}]}], ""parseErrors"": []}, {""more"": {""heading"": ""Understanding data on causes of death"", ""articles"": [{""value"": {""url"": ""https://ourworldindata.org/risk-ratios-odds-ratios-risk-differences-how-do-researchers-calculate-the-risk-from-a-risk-factor"", ""title"": ""Risk ratios, odds ratios, risk differences: How do researchers calculate the risks from risk factors?"", ""authors"": [""Saloni Dattani""]}}, {""value"": {""url"": ""https://ourworldindata.org/how-do-researchers-estimate-the-death-toll-caused-by-each-risk-factor-whether-its-smoking-obesity-or-air-pollution"", ""title"": ""How do researchers estimate the death toll caused by each risk factor, whether it’s smoking, obesity, or air pollution?"", ""authors"": [""Saloni Dattani""]}}, {""value"": {""url"": ""https://ourworldindata.org/why-isnt-it-possible-to-sum-up-the-deaths-from-different-risk-factors"", ""title"": ""Why isn’t it possible to sum up the death toll from risk factors?"", ""authors"": [""Saloni Dattani""]}}]}, ""rows"": [{""heading"": ""Deaths from infectious diseases"", ""articles"": [{""value"": {""url"": ""https://ourworldindata.org/influenza-deaths"", ""title"": ""How many people die from the flu?"", ""authors"": [""Saloni Dattani""], ""filename"": ""Flu-deaths-thumbnail.png""}}, {""value"": {""url"": ""https://ourworldindata.org/malaria-introduction"", ""title"": ""Malaria: One of the leading causes of child deaths, but progress is possible and you can contribute to it"", ""authors"": [""Max Roser""], ""filename"": ""malaria-map-thumbnail.png""}}, {""value"": {""url"": ""https://ourworldindata.org/childhood-diarrheal-diseases"", ""title"": ""More than half a million children die from diarrhea each year. How do we prevent this?"", ""authors"": [""Bernadeta Dadonaite""], ""filename"": ""deaths-from-diarrheal-diseases-by-age-1.png""}}, {""value"": {""url"": ""https://ourworldindata.org/oral-rehydration-therapy"", ""title"": ""Oral rehydration therapy: a low-tech solution that has saved millions of lives"", ""authors"": [""Bernadeta Dadonaite""], ""filename"": ""children-who-receive-ors thumbnail.png""}}, {""value"": {""url"": ""https://ourworldindata.org/rotavirus-vaccine"", ""title"": ""Rotavirus vaccine – an effective tool that prevents children dying from diarrhea"", ""authors"": [""Bernadeta Dadonaite and Hannah Ritchie""], ""filename"": ""avertable-deaths-from-rotavirus-with-full-vaccine-coverage thumbnail.png""}}, {""value"": {""url"": ""https://ourworldindata.org/child-deaths-from-pneumonia"", ""title"": ""Pneumonia — no child should die from a disease we can prevent"", ""authors"": [""Bernadeta Dadonaite""], ""filename"": ""child-pneumonia-thumbnail.png""}}, {""value"": {""url"": ""https://ourworldindata.org/art-lives-saved"", ""title"": ""Antiretroviral therapy has saved millions of lives from AIDS and could save more"", ""authors"": [""Bernadeta Dadonaite""], ""filename"": ""hivaids-deaths-and-averted-due-to-art-1.png""}}, {""value"": {""url"": ""https://ourworldindata.org/smallpox-is-the-only-human-disease-to-be-eradicated-heres-how-the-world-achieved-it"", ""title"": ""Smallpox is the only human disease to be eradicated - 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"", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1001/jamanetworkopen.2022.8873"", ""children"": [{""text"": ""https://doi.org/10.1001/jamanetworkopen.2022.8873"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""427755e5350c902ade291e014e6511e274ecb668"": {""id"": ""427755e5350c902ade291e014e6511e274ecb668"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Armstrong, G. L. (1999). Trends in Infectious Disease Mortality in the United States During the 20th Century. JAMA, 281(1), 61. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1001/jama.281.1.61"", ""children"": [{""text"": ""https://doi.org/10.1001/jama.281.1.61"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Wise, D. A. (Ed.). (2004). Perspectives on the economics of aging. University of Chicago Press. Chapter 9. Cutler, D., & Meara, E. (2001). Changes in the Age Distribution of Mortality Over the 20th Century (No. w8556; p. w8556). National Bureau of Economic Research. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.3386/w8556"", ""children"": [{""text"": ""https://doi.org/10.3386/w8556"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Drevenstedt, G. L., Crimmins, E. M., Vasunilashorn, S., & Finch, C. E. (2008). The rise and fall of excess male infant mortality. Proceedings of the National Academy of Sciences, 105(13), 5016–5021. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1073/pnas.0800221105"", ""children"": [{""text"": ""https://doi.org/10.1073/pnas.0800221105"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Jayachandran, S., Lleras-Muney, A., & Smith, K. V. (2010). Modern Medicine and the Twentieth Century Decline in Mortality: Evidence on the Impact of Sulfa Drugs. American Economic Journal: Applied Economics, 2(2), 118–146. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1257/app.2.2.118"", ""children"": [{""text"": ""https://doi.org/10.1257/app.2.2.118"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""444488eb3995df871dff048e67a827f2909a7c9d"": {""id"": ""444488eb3995df871dff048e67a827f2909a7c9d"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This chart was inspired by related figures in the paper:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Schöley, J., & Willekens, F. (2017). Visualizing compositional data on the Lexis surface. Demographic Research, 36, 627–658. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.4054/DemRes.2017.36.21"", ""children"": [{""text"": ""https://doi.org/10.4054/DemRes.2017.36.21"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""48fc9abf851662ed57cbae834a131aec01c85654"": {""id"": ""48fc9abf851662ed57cbae834a131aec01c85654"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Murray, C. J. L., Aravkin, A. Y., Zheng, P., Abbafati, C., Abbas, K. M., Abbasi-Kangevari, M., Abd-Allah, F., Abdelalim, A., Abdollahi, M., Abdollahpour, I., Abegaz, K. H., Abolhassani, H., Aboyans, V., Abreu, L. G., Abrigo, M. R. M., Abualhasan, A., Abu-Raddad, L. J., Abushouk, A. I., Adabi, M., … Lim, S. S. (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258), 1223–1249. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/S0140-6736(20)30752-2"", ""children"": [{""text"": ""https://doi.org/10.1016/S0140-6736(20)30752-2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""cb5f2fe028fa67253225c00ca52c93179898c4b4"": {""id"": ""cb5f2fe028fa67253225c00ca52c93179898c4b4"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Out of these, death rates from falls have an especially strong age gradient."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Rockett, I. R. H., Regier, M. D., Kapusta, N. D., Coben, J. H., Miller, T. R., Hanzlick, R. L., Todd, K. H., Sattin, R. W., Kennedy, L. W., Kleinig, J., & Smith, G. S. (2012). Leading Causes of Unintentional and Intentional Injury Mortality: United States, 2000–2009. American Journal of Public Health, 102(11), e84–e92. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.2105/AJPH.2012.300960"", ""children"": [{""text"": ""https://doi.org/10.2105/AJPH.2012.300960"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Remund, A., Camarda, C. G., & Riffe, T. (2018). A Cause-of-Death Decomposition of Young Adult Excess Mortality. Demography, 55(3), 957–978. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1007/s13524-018-0680-9"", ""children"": [{""text"": ""https://doi.org/10.1007/s13524-018-0680-9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Rockett, I. R., Regier, M. D., Kapusta, N. D., Coben, J. H., Miller, T. R., Hanzlick, R. L., Todd, K. H., Sattin, R. W., Kennedy, L. W., Kleinig, J., & others. (2012). Leading causes of unintentional and intentional injury mortality: United States, 2000–2009. American Journal of Public Health, 102(11), e84–e92."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Percentage of Deaths from External Causes,* by Age Group† - United States, 2017. (2019). MMWR. Morbidity and Mortality Weekly Report, 68(32), 710. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.15585/mmwr.mm6832a7"", ""children"": [{""text"": ""https://doi.org/10.15585/mmwr.mm6832a7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e485350565c41c7138a2e3e1161b2cadb571087d"": {""id"": ""e485350565c41c7138a2e3e1161b2cadb571087d"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Mercer, A. J. (2018). Updating the epidemiological transition model. Epidemiology and Infection, 146(6), 680–687. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1017/S0950268818000572"", ""children"": [{""text"": ""https://doi.org/10.1017/S0950268818000572"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Vaupel, J. W. (2010). Biodemography of human ageing. Nature, 464(7288), 536–542. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1038/nature08984"", ""children"": [{""text"": ""https://doi.org/10.1038/nature08984"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Vaupel, J. W., & Yashin, A. I. (1985). Heterogeneity’s Ruses: Some Surprising Effects of Selection on Population Dynamics. The American Statistician, 39(3), 176–185. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1080/00031305.1985.10479424"", ""children"": [{""text"": ""https://doi.org/10.1080/00031305.1985.10479424"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Schöley, J., & Willekens, F. (2017). Visualizing compositional data on the Lexis surface. Demographic Research, 36, 627–658. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.4054/DemRes.2017.36.21"", ""children"": [{""text"": ""https://doi.org/10.4054/DemRes.2017.36.21"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f934bee1078365ce880ce132f2abe8810bb35a49"": {""id"": ""f934bee1078365ce880ce132f2abe8810bb35a49"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For other risk factors, like obesity, they estimate the number of deaths that could be prevented if the risk factor was reduced to an ‘optimal level’, such as a BMI range."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""topic-page"", ""title"": ""Causes of Death"", ""authors"": [""Saloni Dattani"", ""Fiona Spooner"", ""Hannah Ritchie"", ""Max Roser""], ""excerpt"": ""To find ways to save lives, it’s essential to know what people are dying from. Explore global data and research on causes of death."", ""dateline"": ""September 5, 2023"", ""subtitle"": """", ""atom-title"": ""We published a new topic page on causes of death"", ""sticky-nav"": [{""text"": ""Introduction"", ""target"": ""#introduction""}, {""text"": ""Key Insights"", ""target"": ""#key-insights""}, {""text"": ""Research & Writing"", ""target"": ""#research-writing""}, {""text"": ""Charts"", ""target"": ""#all-charts""}, {""text"": ""Endnotes"", ""target"": ""#article-endnotes""}, {""text"": ""Cite This Work"", ""target"": ""#article-citation""}, {""text"": ""Reuse This Work"", ""target"": ""#article-licence""}], ""atom-excerpt"": ""To find ways to save lives, it’s essential to know what people are dying from. Explore global data and research on causes of death."", ""featured-image"": ""causes-of-death-thumbnail.png""}",1,2023-07-05 18:26:06,2023-09-05 11:19:59,2023-12-28 16:31:11,listed,ALBJ4LvPEIjq8daySrr9rSbE0b2X4FwzoGNK3E8B_DceHHyNJIcXIda6ga8_34kerjrvO-IbPoqKzOXlMMBzpQ,,"What are people dying from? This question is essential to guide decisions in public health, and find ways to save lives. Many leading causes of death receive little mainstream attention. If news reports reflected what children died from, they would say that around 1,400 young children die from diarrheal diseases, 1,000 die from malaria, and 1,900 from respiratory infections – _every day_. This can change. Over time, death rates from these causes have declined across the world. A better understanding of the causes of death has led to the development of technologies, preventative measures, and better healthcare, reducing the chances of dying from a wide range of different causes, across all age groups. In the past, infectious diseases dominated. But death rates from infectious diseases have fallen quickly – faster than other causes. This has led to a shift in the leading causes of death. Now, non-communicable diseases – such as heart diseases and cancers – are the most common causes of death globally. More progress is possible, and the impact of causes of death can fall further. On this page, you will find global data and research on leading causes of death and how they can be prevented. This includes the number of people dying from each cause, their death rates, how they differ between age groups, and their trends over time. This data can also help understand the burden of disease more broadly, and offer a lens to see the impacts of healthcare and medicine, habits and behaviours, environmental factors, health infrastructure, and more. ## Key Insights on Causes of Death ### Globally, non-communicable diseases are the most common causes of death The chart shows what people died from globally, in 2019. Each box represents one cause, and its size is proportional to the number of deaths it caused. The most common causes of death globally — shown in blue — were from ‘non-communicable diseases’. This includes heart disease, cancer, and chronic respiratory diseases. They tend to develop gradually over time and aren’t infectious themselves.1 Heart diseases were the most common cause, responsible for a third of all deaths globally. Cancers were in second, causing almost one-in-five deaths. Taken together, heart diseases and cancers are the cause of every second death. In red are infectious diseases, which are responsible for around 1-in-7 deaths. These include pneumonia, diarrheal diseases, tuberculosis, HIV/AIDS, and malaria. A smaller share – around 4% – was from neonatal and maternal deaths. A similar share was from accidents. Violent deaths were less common, with 1.3% dying from suicide and less than 1% dying from interpersonal violence such as homicide or battle deaths. In this article, we cover this in more detail: ### undefined undefined https://docs.google.com/document/d/1UdKf375jChVw4SpFrhNaRJH0Ve7VDXtjFz_CP7mon54/edit ![](causes-of-death-2019-full.png) ### Millions of young children die from preventable causes each year Every child’s death is a tragedy. Globally, [the scale of child mortality is immense](https://ourworldindata.org/child-mortality-big-problem-in-brief): five million children under five die yearly. That’s around 14,000 each day. In the chart, you can see what they die from. The size of each box corresponds to the number of children under five years old who die from each cause. The most common causes for young children are different from the leading causes across the entire population – which was shown in the previous key insight. Infectious diseases were most common – they kill an estimated 2.2 million children annually. They include pneumonia, diarrheal diseases, malaria, and meningitis. Next were birth disorders, such as preterm birth, neonatal asphyxia (suffocation), and trauma, which caused an estimated 1.9 million child deaths. Several other causes, such as heart abnormalities and malnutrition, were responsible for around 100,000 deaths each. These figures are astonishing because many of these causes are preventable. With vaccination, basic medication, rehydration treatment, nutrition supplementation, and neonatal healthcare, a large share of child deaths could be prevented. In this article, we cover this in more detail: ### What are children dying from and what can we do about it? Here we look at the number of children dying by each cause – from pneumonia to diarrheal diseases, malaria and malnutrition. We also present the range of interventions that are available to prevent children from dying. https://ourworldindata.org/what-are-children-dying-from-and-what-can-we-do-about-it ![](causes-of-death-children-under-5-treemap-final.png) ### Causes of death have changed over time and vary by age What people die from has changed dramatically over time. In the chart, you can see long-run historical trends in death rates in France.2 This is shown across age groups (on the y-axis) between 1925 and 1999 (on the x-axis). The colors represent the death rates. As you can see, the rates of some causes of death rise exponentially with age; the shades are much darker along the vertical axis. Take respiratory diseases as an example. In 1999, people aged 40 to 44 had a death rate 24 times higher than those aged 20 to 24. For those aged 60 to 64, it was almost 440 times higher. In contrast, external causes, which include accidents, violence, falls, and suicides, tend to rise more slowly with age. The shades get darker slowly along the vertical axis.3 Causes of death have also changed _over time_. The effects of major events – such as World War Two and the AIDS epidemic – are visible on the chart. They led to large surges in death rates from external causes and infectious diseases. Another example is that death rates from infectious diseases and respiratory diseases have declined over time – especially from the mid-20th century onwards, with the rise of antibiotics, vaccines, and public healthcare.4 ![](cause-of-death-age-and-period-mortality-rate-France.png) ### Death rates from communicable and non-communicable disease vary widely around the world Some causes of death are far more common in some parts of the world than others. In poorer countries in Africa and Asia – where [clean water](https://ourworldindata.org/water-access), [sanitation](https://ourworldindata.org/sanitation), and access to healthcare are lacking – people are much more likely to die from infectious diseases, maternal, neonatal, and nutritional causes. However, in wealthier countries, people are much more likely to die from 'non-communicable diseases' instead, which include heart diseases and cancers. This is because of two related points. First, infectious diseases are much more common in poorer countries, and treatment is often lacking. Second, deaths from infectious diseases were much more common in wealthier countries in the past. As these causes of death are reduced or eliminated, people tend to live longer and die from other causes instead.5 Therefore, the data needs to be ‘[age-standardized](https://ourworldindata.org/age-standardization)’ to see how causes of death vary between countries, among people of the same age. In the chart, you can see an age-standardized comparison of these causes of death. As you can see, countries with higher death rates from communicable diseases _also_ tend to have higher death rates from non-communicable diseases. This results from poorer healthcare, income, and living standards, which affect the chances of surviving many kinds of diseases. You can also move the slider to see how they have [shifted over time](https://ourworldindata.org/grapher/death-rate-from-communicable-vs-non-communicable-diseases?time=1990..latest). Annual death rates have been reduced over time for both categories – but they have dropped faster for communicable diseases. ### Underlying data on causes of death is limited in many countries The most common way to track and understand causes of death is to rely on data from death certificates – where doctors describe the chain of events that led to each person’s death and the disease or injury that caused it. These are registered in Vital Registration systems and shared with the World Health Organization annually. However, in many countries, this data is lacking. You can see this in the chart. This is because of several factors – a lack of doctors, nurses, medical records, and hospitals, and a poorly functioning Vital Registration system. Instead, our understanding of causes of death in poor countries often comes from other studies, such as ‘verbal autopsies’, which are not conducted regularly. To improve our understanding of causes of death worldwide, we need better-functioning Vital Registration systems, medical records, and training for doctors and nurses to collect data where it’s lacking. In this article, we cover this in more detail: ### How are causes of death registered around the world? When people die, the cause of their death is usually officially registered in their country’s national system. How is the cause determined? https://ourworldindata.org/how-are-causes-of-death-registered-around-the-world ### A range of risk factors affect the chances of death In the data we present on causes of death, we show each death as caused by a _single_ disease, event, or injury. However, people have often been exposed to various risk factors earlier in life, affecting their chances of premature death. These risk factors can include behaviors – such as [smoking](https://ourworldindata.org/smoking) and low exercise – and environmental factors, such as [air pollution](https://ourworldindata.org/air-pollution), [unsafe water](https://ourworldindata.org/water-access), and pathogens. Researchers try to estimate the number of deaths caused by each risk factor, using available data. For example, researchers estimate the number of deaths that could be prevented if no one smoked, by using estimates of the risk caused by smoking, and the levels of smoking in the population.6 In the chart, you can see the estimated number of deaths caused by a range of risk factors.7 The chart shows that risk factors such as high blood pressure, smoking, and air pollution are each estimated to cause millions of deaths yearly. These risk factors are not exclusive: people can be exposed to multiple risk factors, and some are related to each other. Therefore, the numbers do not sum up to the total number of deaths. In this article, we cover this in more detail: ### How do researchers estimate the death toll caused by each risk factor, whether it’s smoking, obesity or air pollution? Risk factors are important to understand because they can help us identify how to save lives. How do researchers estimate their impact? https://ourworldindata.org/how-do-researchers-estimate-the-death-toll-caused-by-each-risk-factor-whether-its-smoking-obesity-or-air-pollution ## Related research and writing * https://ourworldindata.org/causes-of-death-treemap ,* https://ourworldindata.org/how-are-causes-of-death-registered-around-the-world ,* https://ourworldindata.org/influenza-deaths ,* https://ourworldindata.org/malaria-introduction ,* https://ourworldindata.org/childhood-diarrheal-diseases ,* https://ourworldindata.org/oral-rehydration-therapy ,* https://ourworldindata.org/rotavirus-vaccine ,* https://ourworldindata.org/child-deaths-from-pneumonia ,* https://ourworldindata.org/art-lives-saved ,* https://ourworldindata.org/smallpox-is-the-only-human-disease-to-be-eradicated-heres-how-the-world-achieved-it ,* https://ourworldindata.org/spanish-flu-largest-influenza-pandemic-in-history ,* https://ourworldindata.org/microbes-battle-science-vaccines ,* https://ourworldindata.org/data-review-air-pollution-deaths ,* https://ourworldindata.org/why-do-far-fewer-people-die-in-famines-today ,* https://ourworldindata.org/century-disaster-deaths ,* https://ourworldindata.org/how-many-deaths-make-a-natural-disaster-newsworthy ,* https://ourworldindata.org/how-many-people-in-the-world-die-from-cancer ,* https://ourworldindata.org/risk-ratios-odds-ratios-risk-differences-how-do-researchers-calculate-the-risk-from-a-risk-factor ,* https://ourworldindata.org/how-do-researchers-estimate-the-death-toll-caused-by-each-risk-factor-whether-its-smoking-obesity-or-air-pollution ,* https://ourworldindata.org/why-isnt-it-possible-to-sum-up-the-deaths-from-different-risk-factors Although communicable and non-communicable diseases are shown separately, it is now understood that infectious diseases contribute to several non-communicable diseases. This includes Helicobacter pylori and stomach cancer, human papillomavirus and cervical cancer, hepatitis C virus and liver cancer, Chlamydia pneumoniae and atherosclerosis, Streptococcus pneumoniae and chronic respiratory diseases, and others. In addition, infectious diseases can increase the risk of dying from non-communicable diseases. For example, several respiratory pathogens, such as the influenza virus, increase the risk of heart attacks and strokes. Mercer, A. J. (2018). Updating the epidemiological transition model. Epidemiology and Infection, 146(6), 680–687. [https://doi.org/10.1017/S0950268818000572](https://doi.org/10.1017/S0950268818000572) Behrouzi, B., Bhatt, D. L., Cannon, C. P., Vardeny, O., Lee, D. S., Solomon, S. D., & Udell, J. A. (2022). Association of Influenza Vaccination With Cardiovascular Risk: A Meta-analysis. JAMA Network Open, 5(4), e228873. [https://doi.org/10.1001/jamanetworkopen.2022.8873](https://doi.org/10.1001/jamanetworkopen.2022.8873) Armstrong, G. L. (1999). Trends in Infectious Disease Mortality in the United States During the 20th Century. JAMA, 281(1), 61. [https://doi.org/10.1001/jama.281.1.61](https://doi.org/10.1001/jama.281.1.61) Wise, D. A. (Ed.). (2004). Perspectives on the economics of aging. University of Chicago Press. Chapter 9. Cutler, D., & Meara, E. (2001). Changes in the Age Distribution of Mortality Over the 20th Century (No. w8556; p. w8556). National Bureau of Economic Research. [https://doi.org/10.3386/w8556](https://doi.org/10.3386/w8556) Drevenstedt, G. L., Crimmins, E. M., Vasunilashorn, S., & Finch, C. E. (2008). The rise and fall of excess male infant mortality. Proceedings of the National Academy of Sciences, 105(13), 5016–5021. [https://doi.org/10.1073/pnas.0800221105](https://doi.org/10.1073/pnas.0800221105) Jayachandran, S., Lleras-Muney, A., & Smith, K. V. (2010). Modern Medicine and the Twentieth Century Decline in Mortality: Evidence on the Impact of Sulfa Drugs. American Economic Journal: Applied Economics, 2(2), 118–146. [https://doi.org/10.1257/app.2.2.118](https://doi.org/10.1257/app.2.2.118) This chart was inspired by related figures in the paper: Schöley, J., & Willekens, F. (2017). Visualizing compositional data on the Lexis surface. Demographic Research, 36, 627–658. [https://doi.org/10.4054/DemRes.2017.36.21](https://doi.org/10.4054/DemRes.2017.36.21) Murray, C. J. L., Aravkin, A. Y., Zheng, P., Abbafati, C., Abbas, K. M., Abbasi-Kangevari, M., Abd-Allah, F., Abdelalim, A., Abdollahi, M., Abdollahpour, I., Abegaz, K. H., Abolhassani, H., Aboyans, V., Abreu, L. G., Abrigo, M. R. M., Abualhasan, A., Abu-Raddad, L. J., Abushouk, A. I., Adabi, M., … Lim, S. S. (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258), 1223–1249. [https://doi.org/10.1016/S0140-6736(20)30752-2](https://doi.org/10.1016/S0140-6736(20)30752-2) Out of these, death rates from falls have an especially strong age gradient. Rockett, I. R. H., Regier, M. D., Kapusta, N. D., Coben, J. H., Miller, T. R., Hanzlick, R. L., Todd, K. H., Sattin, R. W., Kennedy, L. W., Kleinig, J., & Smith, G. S. (2012). Leading Causes of Unintentional and Intentional Injury Mortality: United States, 2000–2009. American Journal of Public Health, 102(11), e84–e92. [https://doi.org/10.2105/AJPH.2012.300960](https://doi.org/10.2105/AJPH.2012.300960) Remund, A., Camarda, C. G., & Riffe, T. (2018). A Cause-of-Death Decomposition of Young Adult Excess Mortality. Demography, 55(3), 957–978. [https://doi.org/10.1007/s13524-018-0680-9](https://doi.org/10.1007/s13524-018-0680-9) Rockett, I. R., Regier, M. D., Kapusta, N. D., Coben, J. H., Miller, T. R., Hanzlick, R. L., Todd, K. H., Sattin, R. W., Kennedy, L. W., Kleinig, J., & others. (2012). Leading causes of unintentional and intentional injury mortality: United States, 2000–2009. American Journal of Public Health, 102(11), e84–e92. Percentage of Deaths from External Causes,* by Age Group† - United States, 2017. (2019). MMWR. Morbidity and Mortality Weekly Report, 68(32), 710. [https://doi.org/10.15585/mmwr.mm6832a7](https://doi.org/10.15585/mmwr.mm6832a7) Mercer, A. J. (2018). Updating the epidemiological transition model. Epidemiology and Infection, 146(6), 680–687. [https://doi.org/10.1017/S0950268818000572](https://doi.org/10.1017/S0950268818000572) Vaupel, J. W. (2010). Biodemography of human ageing. Nature, 464(7288), 536–542. [https://doi.org/10.1038/nature08984](https://doi.org/10.1038/nature08984) Vaupel, J. W., & Yashin, A. I. (1985). Heterogeneity’s Ruses: Some Surprising Effects of Selection on Population Dynamics. The American Statistician, 39(3), 176–185. [https://doi.org/10.1080/00031305.1985.10479424](https://doi.org/10.1080/00031305.1985.10479424) Schöley, J., & Willekens, F. (2017). Visualizing compositional data on the Lexis surface. Demographic Research, 36, 627–658. [https://doi.org/10.4054/DemRes.2017.36.21](https://doi.org/10.4054/DemRes.2017.36.21) For other risk factors, like obesity, they estimate the number of deaths that could be prevented if the risk factor was reduced to an ‘optimal level’, such as a BMI range.",Causes of Death 1scFKpuRQ13hmruDYgL3f-nMKNVbjW8YtM-u6mxqLa9k,trade-and-globalization,linear-topic-page,"{""toc"": [{""slug"": ""trade-has-changed-the-world-economy"", ""text"": ""Trade has changed the world economy"", ""title"": ""Trade has changed the world economy"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""trade-has-grown-remarkably-over-the-last-century"", ""text"": ""Trade has grown remarkably over the last century"", ""title"": ""Trade has grown remarkably over the last century"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""the-increase-in-trade-has-even-outpaced-economic-growth"", ""text"": ""The increase in trade has even outpaced economic growth"", ""title"": ""The increase in trade has even outpaced economic growth"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""trade-expanded-in-two-waves"", ""text"": ""Trade expanded in two waves"", ""title"": ""Trade expanded in two waves"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""trade-and-trade-partners-by-country"", ""text"": ""Trade and trade partners by country"", ""title"": ""Trade and trade partners by country"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""trade-around-the-world-today"", ""text"": ""Trade around the world today"", ""title"": ""Trade around the world today"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-much-do-countries-trade"", ""text"": ""How much do countries trade?"", ""title"": ""How much do countries trade?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""what-do-countries-trade"", ""text"": ""What do countries trade?"", ""title"": ""What do countries trade?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""how-are-trade-partnerships-changing"", ""text"": ""How are trade partnerships changing?"", ""title"": ""How are trade partnerships changing?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""trade-generates-efficiency-gains"", ""text"": ""Trade generates efficiency gains"", ""title"": ""Trade generates efficiency gains"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""the-raw-correlation-between-trade-and-growth"", ""text"": ""The raw correlation between trade and growth"", ""title"": ""The raw correlation between trade and growth"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""evidence-from-cross-country-differences-in-trade-growth-and-productivity"", ""text"": ""Evidence from cross-country differences in trade, growth, and productivity"", ""title"": ""Evidence from cross-country differences in trade, growth, and productivity"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""evidence-from-changes-in-labor-productivity-at-the-firm-level"", ""text"": ""Evidence from changes in labor productivity at the firm level"", ""title"": ""Evidence from changes in labor productivity at the firm level"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""trade-does-not-only-increase-efficiency-gains"", ""text"": ""Trade does not only increase efficiency gains"", ""title"": ""Trade does not only increase efficiency gains"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""trade-has-distributional-consequences"", ""text"": ""Trade has distributional consequences"", ""title"": ""Trade has distributional consequences"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""the-conceptual-link-between-trade-and-household-welfare"", ""text"": ""The conceptual link between trade and household welfare"", ""title"": ""The conceptual link between trade and household welfare"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""the-link-between-trade-jobs-and-wages"", ""text"": ""The link between trade, jobs and wages"", ""title"": ""The link between trade, jobs and wages"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""the-link-between-trade-and-the-cost-of-living"", ""text"": ""The link between trade and the cost of living"", ""title"": ""The link between trade and the cost of living"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""implications-of-trade-s-distributional-effects"", ""text"": ""Implications of trade’s distributional effects"", ""title"": ""Implications of trade’s distributional effects"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""explaining-trade-patterns-theory-and-evidence"", ""text"": ""Explaining trade patterns: Theory and Evidence"", ""title"": ""Explaining trade patterns: Theory and Evidence"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""comparative-advantage"", ""text"": ""Comparative advantage"", ""title"": ""Comparative advantage"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""trade-diminishes-with-distance"", ""text"": ""Trade diminishes with distance"", ""title"": ""Trade diminishes with distance"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""institutions"", ""text"": ""Institutions"", ""title"": ""Institutions"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""increasing-returns-to-scale"", ""text"": ""Increasing returns to scale"", ""title"": ""Increasing returns to scale"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""measurement-and-data-quality"", ""text"": ""Measurement and data quality"", ""title"": ""Measurement and data quality"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-data-is-available"", ""text"": ""What data is available?"", ""title"": ""What data is available?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""how-large-are-the-discrepancies-between-sources"", ""text"": ""How large are the discrepancies between sources?"", ""title"": ""How large are the discrepancies between sources?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""why-doesn-t-the-data-add-up"", ""text"": ""Why doesn't the data add up?"", ""title"": ""Why doesn't the data add up?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""a-checklist-for-comparing-sources"", ""text"": ""A checklist for comparing sources"", ""title"": ""A checklist for comparing sources"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""all-charts"", ""text"": ""Interactive charts on Trade and Globalization"", ""title"": ""Interactive charts on Trade and Globalization"", ""isSubheading"": false}, {""slug"": ""article-endnotes"", ""text"": ""Endnotes"", ""title"": ""Endnotes"", ""isSubheading"": false}, {""slug"": ""article-citation"", ""text"": ""Citation"", ""title"": ""Citation"", ""isSubheading"": false}, {""slug"": ""article-licence"", ""text"": ""Licence"", ""title"": ""Licence"", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""On this topic page, you can find data, visualizations, and research on historical and current patterns of international trade, as well as discussions of their origins and effects."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Other research and writing on trade and globalization on Our World in Data:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/is-globalization-an-engine-of-economic-development"", ""children"": [{""text"": ""Is globalization an engine of economic development?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/trade-and-income-inequality"", ""children"": [{""text"": ""Is trade a major driver of income inequality?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Related topics"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1gVSV2gqzPSTMI80gmHEgbaKeWalj6daqe519RtZaJ7I/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1yzOrFd6uWvrAl2oFB3S67oOSbhgL1ffPHxjAqS7i-4w/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/migration"", ""type"": ""prominent-link"", ""title"": ""Migration"", ""description"": ""See all our data, visualizations, and writing on migration."", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""url"": ""#all-charts"", ""children"": [{""text"": ""See all interactive charts on Trade and Globalization ↓"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""text"": [{""text"": ""Trade has changed the world economy"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Trade has grown remarkably over the last century"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One of the most important developments of the last century has been the integration of national economies into a global economic system. This process of integration, often called globalization, has resulted in a remarkable growth in trade between countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart here shows the growth of world exports over more than the last two centuries. These estimates are in constant prices (i.e. have been adjusted to account for inflation) and are indexed at 1913 values."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows an extraordinary growth in international trade over the last couple of centuries: Exports today are more than 40 times larger than in 1913."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can switch to a logarithmic scale under ‘Settings’. This will help you see that, over the long run, growth has roughly followed an exponential path."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/world-trade-exports-constant-prices"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The increase in trade has even outpaced economic growth"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart above shows how much more trade we have today relative to a century ago. But what about trade relative to total economic output?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Over the last couple of centuries the world economy has experienced "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/world-gdp-over-the-last-two-millennia"", ""children"": [{""text"": ""sustained positive economic growth"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", so looking at changes in trade relative to GDP offers another interesting perspective."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The next chart plots the value of traded goods relative to GDP (i.e. the value of merchandise trade as a share of global economic output)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Up to 1870, the sum of worldwide exports accounted for less than 10% of global output. Today, the value of exported goods around the world is around 25%. This shows that over the last hundred years, the growth in trade has even outpaced rapid economic growth."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/merchandise-exports-gdp-cepii?country=OWID_WRL"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Trade expanded in two waves"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""The first \""wave of globalization\"" started in the 19th century, the second one after WW2"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization presents a compilation of available trade estimates, showing the evolution of world exports and imports as a share of "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/gdp-data/"", ""children"": [{""text"": ""global economic output"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This metric (the ratio of total trade, exports plus imports, to global GDP) is known as the “openness index”. The higher the index, the higher the influence of trade transactions on global economic activity."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we can see, until 1800 there was a long period characterized by persistently low international trade – globally the index never exceeded 10% before 1800. This then changed over the course of the 19th century, when technological advances triggered a period of marked growth in world trade – the so-called “first wave of globalization”."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This first wave came to an end with the beginning of World War I, when the decline of liberalism and the rise of nationalism led to a slump in international trade. In the chart we see a large drop in the interwar period."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""After World War II trade started growing again. This new – and ongoing – wave of globalization has seen international trade grow faster than ever before. Today the sum of exports and imports across nations amounts to more than 50% of the value of total global output."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/globalization-over-5-centuries"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Before the first wave of globalization, trade was driven mostly by colonialism"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Over the early modern period, transoceanic flows of goods between empires and colonies accounted for an important part of international trade. The following visualizations provide a comparison of intercontinental trade, in per capita terms, for different countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we can see, intercontinental trade was very dynamic, with volumes varying considerably across time and from empire to empire."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Leonor Freire Costa, Nuno Palma, and Jaime Reis, who compiled and published the original data shown here, argue that trade, also in this period, had a substantial positive impact on the economy."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/intercontinental-trade-per-capita-1500-1800"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The first wave of globalization was marked by the rise and collapse of intra-European trade"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization shows a detailed overview of Western European exports by destination. Figures correspond to export-to-GDP ratios (i.e. the sum of the value of exports from all Western European countries, divided by the total GDP in this region). You can use “Settings” to switch to a relative view and see the proportional contribution of each region to total Western European exports."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart shows that growth in Western European trade throughout the 19th century was largely driven by trade within the region: In the period 1830-1900 intra-European exports went from 1% of GDP to 10% of GDP, and this meant that the relative weight of intra-European exports doubled over the period. However, this process of European integration then collapsed sharply in the interwar period."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""After the Second World War trade within Europe rebounded, and from the 1990s onwards exceeded the highest levels of the first wave of globalization. In addition, Western Europe then started to increasingly trade with Asia, the Americas, and to a smaller extent Africa and Oceania."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/merchandise-exports-by-continent?facet=none&country=~Western+Europe"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The next graph, using data from Broadberry and O'Rourke (2010)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "", shows another perspective on the integration of the global economy and plots the evolution of three indicators measuring integration across different markets – specifically goods, labor, and capital markets."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The indicators in this chart are indexed, so they show changes relative to the levels of integration observed in 1900. This gives us another perspective on how quickly global integration collapsed with the two World Wars."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""alt"": ""Migration, Financial integration, and Trade openness from 1880–1996"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Migration, financial integration and trade openness, World, 1880-1996 (indexed to 1900 = 100) – Cambridge Economic History Vol. 2"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""broadberry-migration.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""The second wave of globalization was enabled by technology"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The worldwide expansion of trade after the Second World War was largely possible because of reductions in transaction costs stemming from technological advances, such as the development of commercial civil aviation, the improvement of productivity in the merchant marines, and the democratization of the telephone as the main mode of communication. The visualization shows how, at the global level, costs across these three variables have been going down since 1930."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/real-transport-and-communication-costs"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Reductions in transaction costs impacted not only the volumes of trade but also the types of exchanges that were possible and profitable."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The first wave of globalization was characterized by inter-industry trade. This means that countries exported goods that were very different from what they imported – England exchanged machines for Australian wool and Indian tea. As transaction costs went down, this changed. In the second wave of globalization, we are seeing a rise in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""intra"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""-industry trade (i.e. the exchange of broadly similar goods and services is becoming more and more common). France, for example, now both imports and exports machines to and from Germany."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization, from the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://documents1.worldbank.org/curated/en/730971468139804495/pdf/437380REVISED01BLIC1097808213760720.pdf"", ""children"": [{""text"": ""UN World Development Report (2009)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", plots the fraction of total world trade that is accounted for by intra-industry trade, by type of goods. As we can see, intra-industry trade has been going up for primary, intermediate, and final goods."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This pattern of trade is important because the scope for specialization increases if countries are able to exchange intermediate goods (e.g. auto parts) for related final goods (e.g. cars)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": ""GrubelLloyd_WDR09"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Share of intraindustry trade by type of goods – Figure 6.1 in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://documents1.worldbank.org/curated/en/730971468139804495/pdf/437380REVISED01BLIC1097808213760720.pdf"", ""children"": [{""text"": ""UN World Development Report (2009)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""filename"": ""intraindustry-trade-unwde.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""Trade and trade partners by country"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Above, we examined the broad global trends over the last two centuries. Let's now examine country-level trends over this long and dynamic period."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart plots estimates of the value of trade in goods, relative to total economic activity (i.e. export-to-GDP ratios)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These historical estimates obviously come with a large margin of error (in the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/trade-and-globalization#measurement-and-data-quality"", ""children"": [{""text"": ""measurement section below"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" we discuss the data limitations); yet they offer an interesting perspective."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can edit the countries and regions selected. Each country tells a different story."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/merchandise-exports-gdp-cepii"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the next chart we plot, country by country, the regional breakdown of exports. India is shown by default, but you can edit the countries and regions shown."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When switching to displaying relative values under ‘Settings’, we see the proportional contribution of purchases from each region. For example, we see that more than a third of Indian exports went to Asian countries in recent decades."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This gives us an interesting perspective on the changing nature of trade partnerships. In India, we see the rising importance of trade with Africa—a pattern that we "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/trade-and-globalization#south-south-trade-is-becoming-increasingly-important"", ""children"": [{""text"": ""discuss in more detail below"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/merchandise-exports-by-continent?facet=none&country=~IND"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Trade around the world today"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""How much do countries trade?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Trade openness around the world"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The metric trade as a share of GDP gives us an idea of global integration by capturing all incoming and outgoing transactions of a country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The charts shows that countries differ a lot in the extent to which they engage in trade. Trade, for example, is much less important to the US economy than for other rich countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you press the play button on the map, you can see changes over time. This reveals that, despite the great variation between countries, there is a common trend: over the last couple of decades trade openness has gone up in most countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/trade-as-share-of-gdp"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Exports and imports in real dollars"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Expressing the value of trade as a share of GDP tells us the importance of trade in relation to the size of economic activity. Let's now take a look at trade in monetary terms – this tells us the importance of trade in absolute, rather than relative terms."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows the value of exports (goods plus services) in dollars, country by country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The main takeaway here is that the trend towards more trade is more pronounced than in the charts showing shares of GDP. This is not surprising: most countries today "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/app/uploads/2013/05/Scatter-1960-vs-2014-GDP.png"", ""children"": [{""text"": ""produce more than a couple of decades ago"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and at the same time they trade more of what they produce."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/exports-of-goods-and-services-constant-2010-us"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""What do countries trade?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Trade in goods vs. trade in services"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Trade transactions include goods (tangible products that are physically shipped across borders by road, rail, water, or air) and services (intangible commodities, such as tourism, financial services, and legal advice)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Many traded services make merchandise trade easier or cheaper—for example, shipping services, or insurance and financial services."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Trade in goods has "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.caitlingreen.org/2017/03/a-very-long-way-from-home.html"", ""children"": [{""text"": ""been happening for millennia"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", while trade in services is a relatively recent phenomenon."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In some countries services are today an important driver of trade: in the UK services account for around half of all exports; and in the Bahamas, almost all exports are services."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In other countries, such as Nigeria and Venezuela, services account for a small share of total exports."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Globally, trade in goods accounts for the majority of trade transactions. But as this chart shows, the share of services in total global exports has slightly increased in recent decades."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-services-in-total-exports?country=BMU+BRA+NGA+GBR+USA+VEN+OWID_WRL+BHS"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""How are trade partnerships changing?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Bilateral trade is becoming increasingly common"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we consider all pairs of countries that engage in trade around the world, we find that in the majority of cases, there is a bilateral relationship today: most countries that export goods to a country also import goods from the same country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The interactive visualization shows this."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In the chart, all possible country pairs are partitioned into three categories: the top portion represents the fraction of country pairs that do not trade with one another; the middle portion represents those that trade in both directions (they export to one another); and the bottom portion represents those that trade in one direction only (one country imports from, but does not export to, the other country)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we can see, bilateral trade is becoming increasingly common (the middle portion has grown substantially). However, many countries still do not trade with each other at all."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/distribution-of-bilateral-and-unilateral-trade-partnerships"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""South-South trade is becoming increasingly important"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The next visualization here shows the share of world merchandise trade that corresponds to exchanges between today's rich countries and the rest of the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The 'rich countries' in this chart are: Australia, Austria, Belgium, Canada, Cyprus, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom and the United States. 'Non-rich countries' are all the other countries in the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we can see, up until the Second World War, the majority of trade transactions involved exchanges between this small group of rich countries. But this has changed quickly over the last couple of decades, and today, trade between non-rich countries is just as important as trade between rich countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the past two decades, China has been a key driver of this dynamic: the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://hdr.undp.org/content/human-development-report-2013"", ""children"": [{""text"": ""UN Human Development Report (2013)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" estimates that between 1992 and 2011, China's trade with Sub-Saharan Africa rose from $1 billion to more than $140 billion."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-world-merchandise-trade-by-type-of-trade-percent"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The majority of preferential trade agreements are between emerging economies"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The last few decades have not only seen an increase in the volume of international trade, but also an increase in the number of preferential trade agreements through which exchanges take place. A preferential trade agreement is a trade pact that reduces tariffs between the participating countries for certain products."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The visualization here shows the evolution of the cumulative number of preferential trade agreements in force worldwide, according to the World Trade Organization (WTO). These numbers include notified and non-notified preferential agreements (the source reports that only about two-thirds of the agreements currently in force have been notified to the WTO) and are disaggregated by country groups."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This figure shows the increasingly important role of trade between developing countries (South-South trade), vis-a-vis trade between developed and developing countries (North-South trade). In the late 1970s, North-South agreements accounted for more than half of all agreements – in 2010, they accounted for about one-quarter. Today, the majority of preferential trade agreements are between developing economies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Number of preferential trade agreements in force by country group, 1950-2010 – Figure B1 in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.wto.org/english/res_e/booksp_e/anrep_e/world_trade_report11_e.pdf"", ""children"": [{""text"": ""WTO Trade Report (2011)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""filename"": ""PTAs_WTO2011.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""Trading patterns have been changing quickly in middle-income countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""An important change in the composition of exported goods in these countries has accompanied the increase in trade among emerging economies over the last half century."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The next visualization plots the share of food exports in each country's total exported merchandise. These figures, produced by the World Bank, correspond to the Standard International Trade Classification, in which 'food' includes, among other goods, live animals, beverages, tobacco, coffee, oils, and fats."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Two points stand out. First, the relative importance of food exports has substantially decreased in most countries since the 1960s (although globally, it has gone up slightly more recently). Second, this decrease has been largest in middle-income countries, particularly in Latin America."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Regarding levels, as one would expect, in high-income countries, food still accounts for a much smaller share of merchandise exports than in most low- and middle-income-countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-food-exports?country=COL~BRA~RUS~ITA~FRA~OWID_WRL"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Trade generates efficiency gains"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""The raw correlation between trade and growth"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Over the last couple of centuries, the world economy has experienced "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/world-gdp-over-the-last-two-millennia"", ""children"": [{""text"": ""sustained positive economic growth"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and over the same period, this process of economic growth has been accompanied by "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/merchandise-exports-gdp-cepii?country=~OWID_WRL"", ""children"": [{""text"": ""even faster growth in global trade"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In a similar way, if we look at country-level data from the last half century we find that there is also a correlation between economic growth and trade: countries with higher rates of GDP growth also tend to have higher rates of growth in trade as a share of output. This basic correlation is shown in the chart here, where we plot the average annual change in real GDP per capita, against growth in trade (average annual change in value of exports as a share of GDP)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/growth-of-income-and-trade"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Is this statistical association between economic output and trade causal?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Among the potential growth-enhancing factors that may come from greater global economic integration are: competition (firms that fail to adopt new technologies and cut costs are more likely to fail and be replaced by more dynamic firms); economies of scale (firms that can export to the world face larger demand, and under the right conditions, they can operate at larger scales where the price per unit of product is lower); learning and innovation (firms that trade gain more experience and exposure to develop and adopt technologies and industry standards from foreign competitors)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Are these mechanisms supported by the data? Let's take a look at the available empirical evidence."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Evidence from cross-country differences in trade, growth, and productivity"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When it comes to academic studies estimating the impact of trade on GDP growth, the most cited paper is Frankel and Romer (1999)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this study, Frankel and Romer used geography as a proxy for trade to estimate the impact of trade on growth. This is a classic example of the so-called "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/Instrumental_variables_estimation"", ""children"": [{""text"": ""instrumental variables approach"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The idea is that a country's geography is fixed, and mainly affects national income through trade. So if we observe that a country's distance from other countries is a powerful predictor of economic growth (after accounting for other characteristics), then the conclusion is drawn that it must be because "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""trade"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" has an effect on economic growth. Following this logic, Frankel and Romer find evidence of a strong impact of trade on economic growth."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Other papers have applied the same approach to richer cross-country data, and they have found similar results. A key example is Alcalá and Ciccone (2004)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This body of evidence suggests trade is indeed one of the factors driving national average incomes (GDP per capita) and macroeconomic productivity (GDP per worker) over the long run."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-15"", ""children"": [{""children"": [{""text"": ""15"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""Evidence from changes in labor productivity at the firm level"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If trade is causally linked to economic growth, we would expect that trade liberalization episodes also lead to firms becoming more productive in the medium and even short run. There is evidence suggesting this is often the case."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Pavcnik (2002) examined the effects of liberalized trade on plant productivity in the case of Chile, during the late 1970s and early 1980s. She found a positive impact on firm productivity in the import-competing sector. She also found evidence of aggregate productivity improvements from the reshuffling of resources and output from less to more efficient producers."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-16"", ""children"": [{""children"": [{""text"": ""16"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Bloom, Draca, and Van Reenen (2016) examined the impact of rising Chinese import competition on European firms over the period 1996-2007 and obtained similar results. They found that innovation increased more in those firms most affected by Chinese imports. They also found evidence of efficiency gains through two related channels: innovation increased and new existing technologies were adopted within firms, and aggregate productivity also increased because employment was reallocated towards more technologically advanced firms."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-17"", ""children"": [{""children"": [{""text"": ""17"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""Trade does not only increase efficiency gains"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Overall, the available evidence suggests that trade liberalization does improve economic efficiency. This evidence comes from different political and economic contexts and includes both micro and macro measures of efficiency."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This result is important because it shows that there are gains from trade. But of course, efficiency is not the only relevant consideration here. As we discuss in a "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/trade-and-income-inequality"", ""children"": [{""text"": ""companion article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", the efficiency gains from trade are not generally equally shared by everyone. The evidence from the impact of trade on firm productivity confirms this: \""reshuffling workers from less to more efficient producers\"" means closing down some jobs in some places. Because distributional concerns are real it is important to promote public policies – such as unemployment benefits and other safety-net programs – that help redistribute the gains from trade."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Trade has distributional consequences"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""The conceptual link between trade and household welfare"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When a country opens up to trade, the demand and supply of goods and services in the economy shift. As a consequence, local markets respond, and prices change. This has an impact on households, both as consumers and as wage earners."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The implication is that trade has an impact on everyone. It's not the case that the effects are restricted to workers from industries in the trade sector; or to consumers who buy imported goods. The effects of trade extend to everyone because markets are interlinked, so imports and exports have knock-on effects on all prices in the economy, including those in non-traded sectors."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Economists usually distinguish between \""general equilibrium consumption effects\"" (i.e. changes in consumption that arise from the fact that trade affects the prices of non-traded goods relative to traded goods) and \""general equilibrium income effects\"" (i.e. changes in wages that arise from the fact that trade has an impact on the demand for specific types of workers, who could be employed in both the traded and non-traded sectors)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Considering all these complex interrelations, it's not surprising that economic theories predict that not everyone will benefit from international trade in the same way. The distribution of the gains from trade depends on what different groups of people consume, and which types of jobs they have, or could have."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-18"", ""children"": [{""children"": [{""text"": ""18"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""The link between trade, jobs and wages"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Evidence from Chinese imports and their impact on factory workers in the US"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The most famous study looking at this question is Autor, Dorn and Hanson (2013): \""The China syndrome: Local labor market effects of import competition in the United States\""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-19"", ""children"": [{""children"": [{""text"": ""19"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this paper, Autor and coauthors examined how local labor markets changed in the parts of the country most exposed to Chinese competition. They found that rising exposure increased unemployment, lowered labor force participation, and reduced wages. Additionally, they found that claims for unemployment and healthcare benefits also increased in more trade-exposed labor markets."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The visualization here is one of the key charts from their paper. It's a scatter plot of cross-regional exposure to rising imports, against changes in employment. Each dot is a small region (a 'commuting zone' to be precise). The vertical position of the dots represents the percent change in manufacturing employment for the working-age population, and the horizontal position represents the predicted exposure to rising imports (exposure varies across regions depending on the local weight of different industries)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The trend line in this chart shows a negative relationship: more exposure goes along with less employment. There are large deviations from the trend (there are some low-exposure regions with big negative changes in employment); but the paper provides more sophisticated regressions and robustness checks, and finds that this relationship is statistically significant."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Exposure to rising Chinese imports and changes in employment across local labor markets in the US (1999-2007) – Autor, Dorn, and Hanson (2013)"", ""spanType"": ""span-simple-text""}], ""filename"": ""Autor-et-al-Fig-2b-01.png"", ""hasOutline"": false, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This result is important because it shows that the labor market adjustments were large. Many workers and communities were affected over a long period of time."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-20"", ""children"": [{""children"": [{""text"": ""20"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But it's also important to keep in mind that Autor and colleagues are only giving us a partial perspective on the total effect of trade on employment. In particular, comparing changes in employment at the regional level misses the fact that firms operate in multiple regions and industries at the same time. Indeed, Ildikó Magyari found evidence suggesting the Chinese trade shock provided incentives for US firms to diversify and reorganize production."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-21"", ""children"": [{""children"": [{""text"": ""21"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""So companies that outsourced jobs to China often ended up closing some lines of business, but at the same time expanded other lines elsewhere in the US. This means that job losses in some regions subsidized new jobs in other parts of the country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On the whole, Magyari finds that although Chinese imports may have reduced employment within some establishments, these losses were more than offset by gains in employment within the same firms in other places. This is no consolation to people who lost their jobs. But it is necessary to add this perspective to the simplistic story of \""trade with China is bad for US workers\""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Evidence from the expansion of trade in India and the impact on poverty reductions"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another important paper in this field is Topalova (2010): \""Factor immobility and regional impacts of trade liberalization: Evidence on poverty from India\""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-22"", ""children"": [{""children"": [{""text"": ""22"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this paper, Topalova examines the impact of trade liberalization on poverty across different regions in India, using the sudden and extensive change in India's trade policy in 1991. She finds that rural regions that were more exposed to liberalization experienced a slower decline in poverty and lower consumption growth."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Analyzing the mechanisms underlying this effect, Topalova finds that liberalization had a stronger negative impact among the least geographically mobile at the bottom of the income distribution and in places where labor laws deterred workers from reallocating across sectors."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The evidence from India shows that (i) discussions that only look at \""winners\"" in poor countries and \""losers\"" in rich countries miss the point that the gains from trade are unequally distributed within both sets of countries; and (ii) context-specific factors, like worker mobility across sectors and geographic regions, are crucial to understand the impact of trade on incomes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Evidence from other studies"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""Donaldson (2018) uses archival data from colonial India to estimate the impact of India’s vast railroad network. He finds railroads increased trade, and in doing so they increased real incomes (and reduced income volatility)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-23"", ""children"": [{""children"": [{""text"": ""23"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Porto (2006) looks at the distributional effects of "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/Mercosur"", ""children"": [{""text"": ""Mercosur"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" on Argentine families, and finds this regional trade agreement led to benefits across the entire income distribution. He finds the effect was progressive: poor households gained more than middle-income households because prior to the reform, trade protection benefitted the rich disproportionately."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-24"", ""children"": [{""children"": [{""text"": ""24"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Trefler (2004) looks at the Canada-US Free Trade Agreement and finds there was a group who bore \""adjustment costs\"" (displaced workers and struggling plants) and a group who enjoyed \""long-run gains\"" (consumers and efficient plants). "", ""spanType"": ""span-simple-text""}, {""url"": ""#note-25"", ""children"": [{""children"": [{""text"": ""25"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""The link between trade and the cost of living"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The fact that trade negatively affects labor market opportunities for specific groups of people does not necessarily imply that trade has a negative aggregate effect on household welfare. This is because, while trade affects wages and employment, it also affects the prices of consumption goods. So households are affected both as consumers and as wage earners."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most studies focus on the earnings channel and try to approximate the impact of trade on welfare by looking at how much wages can buy, using as a reference the changing prices of a fixed basket of goods."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This approach is problematic because it fails to consider welfare gains from increased product variety, and obscures complicated distributional issues such as the fact that poor and rich individuals consume different baskets so they benefit differently from changes in relative prices."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-26"", ""children"": [{""children"": [{""text"": ""26"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Ideally, studies looking at the impact of trade on household welfare should rely on fine-grained data on prices, consumption, and earnings. This is the approach followed in Atkin, Faber, and Gonzalez-Navarro (2018): \""Retail globalization and household welfare: Evidence from Mexico\""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-27"", ""children"": [{""children"": [{""text"": ""27"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Atkin and coauthors use a uniquely rich dataset from Mexico, and find that the arrival of global retail chains led to reductions in the incomes of traditional retail sector workers, but had little impact on average municipality-level incomes or employment; and led to lower costs of living for both rich and poor households."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart here shows the estimated distribution of total welfare gains across the household income distribution (the light-gray lines correspond to confidence intervals). These are proportional gains expressed as a percent of initial household income."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we can see, there is a net positive welfare effect across all income groups; but these improvements in welfare are regressive, in the sense that richer households gain proportionally more (about 7.5 percent gain compared to 5 percent)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-28"", ""children"": [{""children"": [{""text"": ""28"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Evidence from other countries confirms this is not an isolated case – the expenditure channel really seems to be an important and understudied source of household welfare. Giuseppe Berlingieri, Holger Breinlich, Swati Dhingra, for example, investigated the consumer benefits from trade agreements implemented by the EU between 1993 and 2013; and they found that these trade agreements increased the quality of available products, which translated into a cumulative reduction in consumer prices equivalent to savings of €24 billion per year for EU consumers."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-29"", ""children"": [{""children"": [{""text"": ""29"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Distribution of total household welfare gains from the arrival of foreign retail chains in Mexico – Atkin, Faber, and Gonzalez-Navarro (2018)"", ""spanType"": ""span-simple-text""}], ""filename"": ""Atkin-et-al-2018-lower-opacity.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""Implications of trade’s distributional effects"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The available evidence shows that, for some groups of people, trade has a negative effect on wages and employment opportunities; at the same time, it has a large positive effect via lower consumer prices and increased product availability."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Two points are worth emphasizing."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For some households, the net effect is positive. But for some households that's not the case. In particular, workers who lose their jobs can be affected for extended periods of time, so the positive effect via lower prices is not enough to compensate them for the reduction in earnings."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On the whole, if we aggregate changes in welfare across households, the net effect is usually positive. But this is hardly a consolation for the worse off."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This highlights a complex reality: There are "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/trade-and-econ-growth"", ""children"": [{""text"": ""aggregate gains from trade"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", but there are also real distributional concerns. Even if "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/trade-and-income-inequality"", ""children"": [{""text"": ""trade is not a major driver of income inequalities"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", it's important to keep in mind that public policies, such as unemployment benefits and other safety-net programs, can and should help redistribute the gains from trade."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Explaining trade patterns: Theory and Evidence"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Comparative advantage"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Theory: What is 'comparative advantage' and why does it matter to understand trade?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In economic theory, the 'economic cost' – or the 'opportunity cost' – of producing a good is the value of everything you need to give up in order to produce that good."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Economic costs include physical inputs (the value of the stuff you use to produce the good), plus forgone opportunities (when you allocate scarce resources to a task, you give up alternative uses of those resources)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A country or a person is said to have a 'comparative advantage' if it can produce something at a lower opportunity cost than its trade partners."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The forgone opportunities of production are key to understanding this concept. It is precisely this that distinguishes absolute advantage from comparative advantage."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To see the difference between comparative and absolute advantage, consider a commercial aviation pilot and a baker. Suppose the pilot is an excellent chef, and she can bake just as well, or even better than the baker. In this case, the pilot has an absolute advantage in both tasks. Yet the baker probably has a comparative advantage in baking, because the opportunity cost of baking is much higher for the pilot."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The freely available economics textbook "", ""spanType"": ""span-simple-text""}, {""url"": ""https://core-econ.org/the-economy/book/text/18.html"", ""children"": [{""text"": ""The Economy: Economics for a Changing World"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" explains this as follows: "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""\""A person or country has comparative advantage in the production of a particular good, if the cost of producing an additional unit of that good relative to the cost of producing another good is lower than another person or country’s cost to produce the same two goods.\"""", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At the individual level, comparative advantage explains why you might want to delegate tasks to someone else, even if you can do those tasks better and faster than them. This may sound counterintuitive, but it is not: If you are good at many things, it means that investing time in one task has a high opportunity cost, because you are not doing the other amazing things you could be doing with your time and resources. So, at least from an efficiency point of view, you should specialize on what you are best at, and delegate the rest."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The same logic applies to countries. Broadly speaking, the principle of comparative advantage postulates that all nations can gain from trade if each specializes in producing what they are relatively more efficient at producing, and imports the rest: “do what you do best, import the rest”."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-30"", ""children"": [{""children"": [{""text"": ""30"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In countries with a relative abundance of certain factors of production, the theory of comparative advantage predicts that they will export goods that rely heavily upon those factors: a country typically has a comparative advantage in those goods that use its abundant resources. Colombia exports bananas to Europe because it has comparatively abundant tropical weather."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Is there empirical support for comparative-advantage theories of trade?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The empirical evidence suggests that the principle of comparative advantage does help explain trade patterns. Bernhofen and Brown (2004)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-31"", ""children"": [{""children"": [{""text"": ""31"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "", for instance, provide evidence using the experience of Japan. Specifically, they exploit Japan’s dramatic nineteenth-century move from a state of near complete isolation to wide trade openness."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The graph here shows the price changes of the key tradable goods after the opening up to trade. It presents a scatter diagram of the net exports in 1869 graphed in relation to the change in prices from 1851–53 to 1869. As we can see, this is consistent with the theory: after opening to trade, the relative prices of major exports such as silk increased (Japan exported what was cheap for them to produce and which was valuable abroad), while the relative price of imports such as sugar declined (they imported what was relatively more difficult for them to produce, but was cheap abroad)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Net exports and price changes for 1869, Japan – Figure 4 in Bernhofen and Brown (2014)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-31"", ""children"": [{""children"": [{""text"": ""31"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Japan_BernhoffenBrown2014.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""Trade diminishes with distance"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The resistance that geography imposes on trade has long been studied in the empirical economics literature – and the main conclusion is that trade intensity is strongly linked to geographic distance."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The visualization, from Eaton and Kortum (2002), graphs 'normalized import shares' against distance."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-32"", ""children"": [{""children"": [{""text"": ""32"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Each dot represents a country pair from a set of 19 OECD countries, and both the vertical and horizontal axes are expressed on logarithmic scales."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The 'normalized import shares' in the vertical axis provide a measure of how much each country imports from different partners (see the paper for details on how this is calculated and normalized), while the distance in the horizontal axis corresponds to the distance between central cities in each country (see the paper and references therein for details on the list of cities). As we can see, there is a strong negative relationship. Trade diminishes with distance. Through econometric modeling, the paper shows that this relationship is not just a correlation driven by other factors: their findings suggest that distance imposes a significant barrier to trade."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Import share and distance between country pairs, OECD, 1990 – Figure 1 in Eaton and Kortum (2002)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-32"", ""children"": [{""children"": [{""text"": ""32"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""eaton-kortum-distance-and-geography.png"", ""hasOutline"": false, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The fact that trade diminishes with distance is also corroborated by data on trade intensity within countries. The visualization here shows, through a series of maps, the geographic distribution of French firms that export to France's neighboring countries. The colors reflect the percentage of firms that export to each specific country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we can see, the share of firms exporting to each of the corresponding neighbors is the largest close to the border. The authors also show in the paper that this pattern holds for the value of individual-firm exports – trade value decreases with distance to the border."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Percentage of firms which export in France, by importing country, 1992 – Figure 2 in Crozet and Koenig (2010)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-33"", ""children"": [{""children"": [{""text"": ""33"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""France_Trade_Borders_2010.jpg"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""Institutions"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Conducting international trade requires both financial and non-financial institutions to support transactions. Some of these institutions are fairly obvious (e.g. law enforcement); but some are less obvious. For example, the evidence shows that producers in exporting countries often need credit in order to engage in trade."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The scatter plot, from Manova (2013), shows the correlation between levels in private credit (specifically exporters’ private credit as a share of GDP) and exports (average log bilateral exports across destinations and sectors)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-34"", ""children"": [{""children"": [{""text"": ""34"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" As can be seen, financially developed economies – those with more dynamic private credit markets – typically outperform exporters with less evolved financial institutions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Other studies have shown that country-specific institutions, like the knowledge of foreign languages, for instance, are also important to promote foreign relative to domestic trade."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-35"", ""children"": [{""children"": [{""text"": ""35"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Cross-country correlation between private credit and exports – Figure 2 in Manova (2013)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-34"", ""children"": [{""children"": [{""text"": ""34"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""manova-bilateral-exports-final.png"", ""hasOutline"": false, ""parseErrors"": []}, {""text"": [{""text"": ""Increasing returns to scale"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The concept of comparative advantage predicts that if all countries had identical endowments and institutions, there would be little incentive for specialization because the opportunity cost of producing any good would be the same in every country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""So you may wonder: why is it then the case that in the last few years, we have seen such rapid growth in intra-industry trade between rich countries?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The increase in intra-industry between rich countries seems paradoxical under the light of comparative advantage because in recent decades we have seen convergence in key factors, such as "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/global-rise-of-education"", ""children"": [{""text"": ""human capital"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", across these countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The solution to the paradox is actually not very complicated: Comparative advantage is one, but not the only force driving incentives to specialization and trade."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Several economists, most notably Paul Krugman, have developed theories of trade in which trade is not due to differences between countries, but instead due to \""increasing returns to scale\"" – an economic term used to denote a technology in which producing extra units of a good becomes cheaper if you operate at a larger scale."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The idea is that specialization allows countries to reap greater economies of scale (i.e. to reduce production costs by focusing on producing large quantities of specific products), so trade can be a good idea even if the countries do not differ in endowments, including culture and institutions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These models of trade, often referred to as “New Trade Theory”, are helpful in explaining why in the last few years we have seen such rapid growth in two-way exchanges of goods within industries between developed nations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In a much-cited paper, Evenett and Keller (2002) show that both factor endowments and increasing returns help explain production and trade patterns around the world."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-36"", ""children"": [{""children"": [{""text"": ""36"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can learn more about New Trade Theory, and the empirical support behind it, in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.nobelprize.org/uploads/2018/06/krugman_lecture.pdf"", ""children"": [{""text"": ""Paul Krugman's Nobel lecture"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Measurement and data quality"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are dozens of official sources of data on international trade, and if you compare these different sources, you will find that they do not agree with one another. Even if you focus on what seems to be the same indicator for the same year in the same country, discrepancies are large."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Such differences between sources can also be found in rich countries where statistical agencies tend to follow international reporting guidelines more closely."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are also large bilateral discrepancies within sources: the value of goods that country A exports to country B can be more than the value of goods that country B imports from country A."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Here we explain how international trade data is collected and processed, and why there are such large discrepancies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What data is available?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The data hubs from several large international organizations publish and maintain extensive cross-country datasets on international trade. Here's a list of the most important ones:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""url"": ""https://data.worldbank.org/topic/trade"", ""children"": [{""text"": ""World Bank Open Data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""http://data.imf.org/?sk=9D6028D4-F14A-464C-A2F2-59B2CD424B85&sId=1515614720959"", ""children"": [{""text"": ""IMF Data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://stats.wto.org/"", ""children"": [{""text"": ""WTO Statistics"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://comtrade.un.org"", ""children"": [{""text"": ""UN Comtrade"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://wits.worldbank.org/Default.aspx?lang=en"", ""children"": [{""text"": ""UNCTAD World Integrated Trade Solutions"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ec.europa.eu/eurostat/web/international-trade-in-goods/information-data#Aggregated%20data"", ""children"": [{""text"": ""Eurostat"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://stats.oecd.org/Index.aspx?DataSetCode=BIMTS_CPA"", ""children"": [{""text"": ""OECD.Stat"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In addition to these sources, there are also many other academic projects that publish data on international trade. These projects tend to rely on data from one or more of the sources above, and they typically process and merge series in order to improve coverage and consistency. Three important sources are:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""url"": ""http://correlatesofwar.org"", ""children"": [{""text"": ""Correlates of War Project"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-37"", ""children"": [{""children"": [{""text"": ""37"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.princeton.edu/~reddings/data/NBER/NBER-world-trade.html"", ""children"": [{""text"": ""NBER-United Nations Trade Dataset Project"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.cepii.fr/cepii/en/bdd_modele/presentation.asp?id=32"", ""children"": [{""text"": ""CEPII Bilateral Trade and Gravity Data Project"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-38"", ""children"": [{""children"": [{""text"": ""38"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""How large are the discrepancies between sources?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the visualization here, we compare the data published by several of the sources listed above, country by country, from 1955 to today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For each country, we exclude trade in services, and we focus only on estimates of the total value of exported goods, expressed as shares of GDP."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-39"", ""children"": [{""children"": [{""text"": ""39"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As this chart clearly shows, different data sources often tell very different stories. If you change the country or region shown you will see that this is true, to varying degrees, across all countries and years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/various-sources-of-the-total-value-of-exports-as-a-share-of-gdp?country=CHN"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Constructing this chart was demanding. It required downloading trade data from many different sources, collecting the relevant series, and then standardizing them so that the units of measure and the geographical territories were consistent."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All series, except the two long-run series from CEPII and NBER-UN, were produced from data published by the sources in current US dollars and then converted to GDP shares using a unique source (World Bank)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""So, if all series are in the same units (share of national GDP) and they measure the same thing (value of goods exported from one country to the rest of the world), what explains the differences?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Let's dig deeper to understand what's going on."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Why doesn't the data add up?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Differences in guidelines used by countries to record and report trade data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Broadly speaking, there are two main approaches used to estimate international merchandise trade:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""The first approach relies on estimating trade from "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""customs records"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", often complementing or correcting figures with data from enterprise surveys and administrative records associated with taxation. The main manual providing guidelines for this approach is the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://unstats.un.org/unsd/trade/eg-imts/IMTS%202010%20(English).pdf"", ""children"": [{""text"": ""International Merchandise Trade Statistics Manual"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (IMTS)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The second approach relies on estimating trade from "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""macroeconomic data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", typically "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""National Accounts"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". The main manual providing guidelines for this approach is the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.imf.org/external/pubs/ft/bop/2007/bopman6.htm"", ""children"": [{""text"": ""Balance of Payments and International Investment Position Manual"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (BPM6), which was drafted in parallel with the 2008 "", ""spanType"": ""span-simple-text""}, {""url"": ""https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf"", ""children"": [{""text"": ""System of National Accounts of the United Nations"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (SNA 2008). The idea behind this approach is to record changes in economic ownership."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-40"", ""children"": [{""children"": [{""text"": ""40"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Under these two approaches, it is common to distinguish between 'traded merchandise' and 'traded goods'. The distinction is often made because goods simply being transported through a country (i.e., goods in transit) are not considered to change a country's stock of material resources and are hence often excluded from the more narrow concept of 'merchandise trade'."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Also, adding to the complexity, countries often rely on measurement protocols developed alongside approaches and concepts that are not perfectly compatible to begin with. In Europe, for example, countries use the "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-02-17-333"", ""children"": [{""text"": ""'Compilers guide on European statistics on international trade in goods'."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""text"": [{""text"": ""Measurement error and other inconsistencies"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Even when two sources rely on the same broad accounting approach, discrepancies arise because countries fail to adhere perfectly to the protocols."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In theory, for example, the exports of country A to country B should mirror the imports of country B from country A. But in practice this is rarely the case because of differences in valuation. According to the BPM6, imports, and exports should be recorded in the balance of payments accounts on a '"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""free on board"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" (FOB) basis', which means using prices that include all charges up to placing the goods on board a ship at the port of departure. Yet many countries stick to FOB values only for exports, and use CIF values for imports (CIF stands for 'Cost, Insurance and Freight', and includes the costs of transportation)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-41"", ""children"": [{""children"": [{""text"": ""41"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart here gives you an idea of how large import-export asymmetries are. Shown are the differences between the value of goods that each country reports exporting to the US, and the value of goods that the US reports importing from the same countries. For example, for China, the figure in the chart corresponds to the “Value of merchandise imports in the US from China” minus the “Value of merchandise exports from China to the US”."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The differences in the chart here, which are both positive and negative, suggest that there is more going on than differences in FOB vs. CIF values. If all asymmetries were coming from FOB-CIF differences, then we should only see positive values in the chart (recall that, unlike FOB values, CIF values include the cost of transportation, so CIF values are larger)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/difference-in-the-value-of-goods-exported-to-and-imported-by-the-us"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What else may be going on here?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another common source of measurement error relates to the inconsistent attribution of trade partners. An example is failure to follow the guidelines on how to treat goods passing through intermediary countries for processing or merchanting purposes. As global production chains become more complex, countries find it increasingly difficult to unambiguously establish the origin and final destination of merchandise, even when rules are established in the manuals."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-42"", ""children"": [{""children"": [{""text"": ""42"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And there are still more potential sources of discrepancies. For example differences in customs and tax regimes, and differences between \""general\"" and \""special\"" trade systems (i.e. differences between statistical territories and actual country borders, which do not often coincide because of things like 'custom free zones')."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-43"", ""children"": [{""children"": [{""text"": ""43"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Even when two sources have identical trade estimates, inconsistencies in published data can arise from differences in exchange rates. If a dataset reports cross-country trade data in US dollars, estimates will vary depending on the exchange rates used. Different exchange rates will lead to conflicting estimates, even if figures in local currency units are consistent."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""A checklist for comparing sources"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Asymmetries in international trade statistics are large and arise for a variety of reasons. These include conceptual inconsistencies across measurement standards and inconsistencies in the way countries apply agreed-upon protocols. Here's a checklist of issues to keep in mind when comparing sources."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""Differences in underlying records: is trade measured from National Accounts data rather than directly from custom or tax records?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Differences in import and export valuations: are transactions valued at FOB or CIF prices?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Inconsistent attribution of trade partners: how is the origin and final destination of merchandise established?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Difference between 'goods' and 'merchandise': how are re-importing, re-exporting, and intermediary merchanting transactions recorded?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Exchange rates: how are values converted from local currency units to the currency that allows international comparisons (most often the US-$)?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Differences between 'general' and 'special' trade system: how is trade recorded for custom-free zones?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Other issues: Time of recording, confidentiality policies, product classification, deliberate mis-invoicing for illicit purposes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Many organizations producing trade data have long recognized these factors. Indeed, international organizations often incorporate corrections in an attempt to improve data quality."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The OECD's "", ""spanType"": ""span-simple-text""}, {""url"": ""https://stats.oecd.org/Index.aspx?DataSetCode=BIMTS_CPA"", ""children"": [{""text"": ""Balanced International Merchandise Trade Statistics"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", for example, uses its own approach to correct and reconcile international merchandise trade statistics."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-44"", ""children"": [{""children"": [{""text"": ""44"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The corrections applied in the OECD's 'balanced' series make this the best source for cross-country comparisons. However, this dataset has low coverage across countries, and it only goes back to 2011. This is an important obstacle since the complex adjustments introduced by the OECD imply we can't easily improve coverage by appending data from other sources. At Our World in Data we have chosen to rely on CEPII as the main source for exploring long-run changes in international trade, but we also rely on World Bank and OECD data for up-to-date cross-country comparisons."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are two key lessons from all of this. The first lesson is that, for most users of trade data out there, there is no obvious way of choosing between sources. And the second lesson is that, because of statistical glitches, researchers and policymakers should always take analyses of trade data with a pinch of salt. For example, in a recent "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.gfintegrity.org/report/illicit-financial-flows-from-developing-countries-2004-2013/"", ""children"": [{""text"": ""high-profile report"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", researchers attributed mismatches in bilateral trade data to illicit financial flows through trade mis-invoicing (or trade-based money laundering). As we show here, this interpretation of the data is not appropriate, since mismatches in the data can, and often do arise from measurement inconsistencies rather than malfeasance."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-45"", ""children"": [{""children"": [{""text"": ""45"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Hopefully, the discussion and checklist above can help researchers better interpret and choose between conflicting data sources."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""top"": [], ""type"": ""all-charts"", ""heading"": ""Interactive charts on Trade and Globalization"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""0c6f0c95b79f4a74d3c1f4d57eeda2d057273093"": {""id"": ""0c6f0c95b79f4a74d3c1f4d57eeda2d057273093"", ""index"": 11, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The textbook "", ""spanType"": ""span-simple-text""}, {""url"": ""https://core-econ.org/the-economy/book/text/18.html"", ""children"": [{""text"": ""The Economy: Economics for a Changing World"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" explains this in more detail."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""1ac0445bad4eaf58109902761d70e9b26a066020"": {""id"": ""1ac0445bad4eaf58109902761d70e9b26a066020"", ""index"": 42, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For more details about general and special trade see the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:General_and_special_trade_systems"", ""children"": [{""text"": ""Eurostat glossary"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""1d21eeb1db3efcc7df1568d44aaa90a7fdd4de2c"": {""id"": ""1d21eeb1db3efcc7df1568d44aaa90a7fdd4de2c"", ""index"": 13, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Alcalá, F., & Ciccone, A. (2004). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.jstor.org/stable/pdf/25098695.pdf"", ""children"": [{""text"": ""Trade and productivity"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The Quarterly Journal of Economics, 119(2), 613-646."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""1e0a4ad38b370016865ff79cc05866ce78b82775"": {""id"": ""1e0a4ad38b370016865ff79cc05866ce78b82775"", ""index"": 27, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""In the paper, Atkin and coauthors explore the reasons for this and find that the regressive nature of the distribution is mainly due to richer households placing higher weight on the product variety and shopping amenities on offer at these new foreign stores."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""1eb35a0fa6466cb6bb88e502ef19502fb290b32f"": {""id"": ""1eb35a0fa6466cb6bb88e502ef19502fb290b32f"", ""index"": 22, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Donaldson, D. (2018). "", ""spanType"": ""span-simple-text""}, {""url"": ""http://eprints.lse.ac.uk/38368/1/ARCWP41-Donaldson.pdf"", ""children"": [{""text"": ""Railroads of the Raj: Estimating the impact of transportation infrastructure"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". American Economic Review, 108(4-5), 899-934."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""2892e54eefcc7797729b5f7d3c3d079a64639764"": {""id"": ""2892e54eefcc7797729b5f7d3c3d079a64639764"", ""index"": 33, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Manova, Kalina. \""Credit constraints, heterogeneous firms, and international trade.\"" The Review of Economic Studies 80.2 (2013): 711-744."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""2f3312dd92bfc82142ba7ebe3bdf80a2b06016e5"": {""id"": ""2f3312dd92bfc82142ba7ebe3bdf80a2b06016e5"", ""index"": 24, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Trefler, D. (2004). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.aeaweb.org/articles?id=10.1257/0002828042002633"", ""children"": [{""text"": ""The long and short of the Canada-US free trade agreement"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". American Economic Review, 94(4), 870-895."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3c542c5da06ad021fa8dfa4aaaa623d6835cba35"": {""id"": ""3c542c5da06ad021fa8dfa4aaaa623d6835cba35"", ""index"": 9, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""We also have the same data, but as "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-world-merchandise-trade-by-type-of-trade"", ""children"": [{""text"": ""a stacked-area chart"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3db283d0effc6f9098c63f7f39bbcb9bf18bc9a0"": {""id"": ""3db283d0effc6f9098c63f7f39bbcb9bf18bc9a0"", ""index"": 26, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Atkin, David, Benjamin Faber, and Marco Gonzalez-Navarro. \""Retail globalization and household welfare: Evidence from Mexico.\"" Journal of Political Economy 126.1 (2018): 1-73."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""4007ed6c67d1fd2298015dbcce3bb80343930bf6"": {""id"": ""4007ed6c67d1fd2298015dbcce3bb80343930bf6"", ""index"": 12, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Frankel, J. A., & Romer, D. H. (1999). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.aeaweb.org/articles?id=10.1257/aer.89.3.379"", ""children"": [{""text"": ""Does trade cause growth?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" American Economic Review, 89(3), 379-399."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""47e712ad82926594085de7b1fdfed0aaa9e91340"": {""id"": ""47e712ad82926594085de7b1fdfed0aaa9e91340"", ""index"": 23, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Porto, G (2006). Using Survey Data to Assess the Distributional Effects of Trade Policy. Journal of International Economics 70 (2006) 140–160."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""4bf8a075397711359aacd767771bbf8cdf4bbc61"": {""id"": ""4bf8a075397711359aacd767771bbf8cdf4bbc61"", ""index"": 41, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Precisely because of the difficulty that arises when trying to establish the origin and final destination of merchandise, some sources distinguish between national and dyadic (i.e. 'directed') trade estimates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""52d90237271cc6f46ee1abbb2e948e59d0772af4"": {""id"": ""52d90237271cc6f46ee1abbb2e948e59d0772af4"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""We also have the"", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/merchandise-imports-gdp-cepii"", ""children"": [{""text"": "" same chart but showing imports"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""65c4f99814a493fae8c5d96dc0570f4c0929f1c9"": {""id"": ""65c4f99814a493fae8c5d96dc0570f4c0929f1c9"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/grapher/service-exports-and-imports-gdp"", ""children"": [{""text"": ""This interactive chart"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" shows trade in services as a share of GDP across countries and regions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""69393bb344b8170cefafe286c6fe2915068bc5f5"": {""id"": ""69393bb344b8170cefafe286c6fe2915068bc5f5"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Leonor Freire Costa, Nuno Palma, and Jaime Reis (2015) – The great escape? The contribution of the empire to Portugal's economic growth, 1500–1800 Leonor Freire Costa Nuno Palma Jaime Reis European Review of Economic History, Volume 19, Issue 1, 1 February 2015, Pages 1–22, https://doi.org/10.1093/ereh/heu019"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""75aaa3b827fde7e760780df5e4a6a8c321a5c90f"": {""id"": ""75aaa3b827fde7e760780df5e4a6a8c321a5c90f"", ""index"": 31, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Eaton, J., & Kortum, S. (2002). Technology, geography, and trade. Econometrica, 70(5), 1741-1779."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""76600fbd7e564654381befaa7b6ef4afe38c6194"": {""id"": ""76600fbd7e564654381befaa7b6ef4afe38c6194"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The openness index, when calculated for the world as a whole, includes double-counting of transactions: When country A sells goods to country B, this shows up in the data both as an import (B imports from A) and as an export (A sells to B)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Indeed, if you compare the chart showing the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/globalization-over-5-centuries-km"", ""children"": [{""text"": ""global trade openness index"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and the chart showing "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/merchandise-exports-gdp-cepii?country=OWID_WRL"", ""children"": [{""text"": ""global merchandise exports as a share of GDP"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", you find that the former is almost twice as large as the latter."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Why is the global openness index not exactly twice the value reported in the chart plotting global merchandise exports? There a three reasons."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""First, the global openness index uses different sources. Second, the global openness index includes trade in goods and services, while merchandise exports include goods but not services. And third, the amount that country A reports exporting to country B does not usually match the amount that B reports importing from A."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We explore this in more detail in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/trade-and-globalization#measurement-and-data-quality"", ""children"": [{""text"": ""our measurement section"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7ddd057c6b9a182806350b70f1d48e492e66024f"": {""id"": ""7ddd057c6b9a182806350b70f1d48e492e66024f"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Broadberry and O'Rourke (2010) - The Cambridge Economic History of Modern Europe: Volume 2, 1870 to the Present. Cambridge University Press."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""8077162ab76808d15fe834a871cf1b79a251bd64"": {""id"": ""8077162ab76808d15fe834a871cf1b79a251bd64"", ""index"": 37, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Further information on CEPII's methodology can be found in "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.cepii.fr/PDF_PUB/wp/2016/wp2016-14.pdf"", ""children"": [{""text"": ""their working paper"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""914132290c63413dd642c4d362cfd9aa5d988858"": {""id"": ""914132290c63413dd642c4d362cfd9aa5d988858"", ""index"": 29, ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Nobel laureate Paul Samuelson (1969) was once challenged by the mathematician Stanislaw Ulam: \""Name me one proposition in all of the social sciences which is both true and non-trivial.\"" It was several years later than he thought of the correct response: comparative advantage. \""That it is logically true need not be argued before a mathematician; that is is not trivial is attested by the thousands of important and intelligent men who have never been able to grasp the doctrine for themselves or to believe it after it was explained to them.\"""", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""(NB. This is an excerpt from https://www.wto.org/english/res_e/reser_e/cadv_e.htm)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""96e4ab4b13e7e45dd280185718a11410119f00f5"": {""id"": ""96e4ab4b13e7e45dd280185718a11410119f00f5"", ""index"": 32, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Crozet, M., & Koenig, P. (2010). Structural Gravity Equations with Intensive and Extensive Margins. The Canadian Journal of Economics / Revue Canadienne D'Economique, 43(1), 41-62. Retrieved from http://www.jstor.org/stable/40389555"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9bffeda9d923bb029127f74cdfd4ea487ff58832"": {""id"": ""9bffeda9d923bb029127f74cdfd4ea487ff58832"", ""index"": 19, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""It's important to mention here that the economist Jonathan Rothwell wrote a "", ""spanType"": ""span-simple-text""}, {""url"": ""https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2920188"", ""children"": [{""text"": ""paper"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" suggesting these findings are the result of a statistical illusion. Rothwell's critique received some "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.wsj.com/articles/the-truth-about-the-china-trade-shock-1491168339"", ""children"": [{""text"": ""attention from the media"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", but Autor and coauthors provided a "", ""spanType"": ""span-simple-text""}, {""url"": ""https://chinashock.info/wp-content/uploads/2016/06/ADH-Response-to-Rothwell.pdf"", ""children"": [{""text"": ""reply"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", which I think successfully refutes this claim."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9cc41b6cf6e4ca45b28877557c8e1af1caff5c2b"": {""id"": ""9cc41b6cf6e4ca45b28877557c8e1af1caff5c2b"", ""index"": 15, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Pavcnik, N. (2002). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.jstor.org/stable/pdf/2695960.pdf"", ""children"": [{""text"": ""Trade liberalization, exit, and productivity improvements: Evidence from Chilean plants"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The Review of Economic Studies, 69(1), 245-276."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9d7525bf752d0b59404230e0ba68895525c634c7"": {""id"": ""9d7525bf752d0b59404230e0ba68895525c634c7"", ""index"": 14, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""There are many papers that try to answer this specific question with macro data. For an overview of papers and methods see: Durlauf, S. N., Johnson, P. A., & Temple, J. R. (2005). Growth econometrics. Handbook of economic growth, 1, 555-677."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9f1841e1fd93ed993bcfc7d23755d26f352f6dae"": {""id"": ""9f1841e1fd93ed993bcfc7d23755d26f352f6dae"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""We also have the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/imports-of-goods-and-services-constant-2010-us"", ""children"": [{""text"": ""same chart, but showing imports"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b543c38f2ae38cf5226bb346f6e5c974d41230c5"": {""id"": ""b543c38f2ae38cf5226bb346f6e5c974d41230c5"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Broadberry and O'Rourke (2010) - The Cambridge Economic History of Modern Europe: Volume 2, 1870 to the Present. Cambridge University Press. The graph depicts the “evolution of three indicators measuring integration in commodity, labor, and capital markets over the long run. Commodity market integration is measured by computing the ratio of exports to GDP. Labor market integration is measured by dividing the migratory turnover by population. Financial integration is measured using Feldstein–Horioka estimators of current account disconnectedness.”"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c150ba7c7a005562f9e9dc70a86d273eb844d703"": {""id"": ""c150ba7c7a005562f9e9dc70a86d273eb844d703"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Integration in the goods markets is measured here through the 'trade openness index', which is defined by the sum of exports and imports as a share of GDP. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/trade-openness-in-europe?country=FRA+DEU+ITA+ESP+SWE+GBR"", ""children"": [{""text"": ""In our interactive chart"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" you can explore trends in trade openness over this period for a selection of European countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c4030df18a7e84507ace48be9fc816f8ccbc62ee"": {""id"": ""c4030df18a7e84507ace48be9fc816f8ccbc62ee"", ""index"": 35, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Evenett, S. J., & Keller, W. (2002). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.researchgate.net/publication/4729534_On_Theories_Explaining_the_Success_of_the_Gravity_Equation"", ""children"": [{""text"": ""On theories explaining the success of the gravity equation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Journal of Political Economy, 110(2), 281-316."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d24089251472716a3f50de372989e0ec009ac73e"": {""id"": ""d24089251472716a3f50de372989e0ec009ac73e"", ""index"": 21, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Topalova, P. (2010). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.aeaweb.org/articles?id=10.1257/app.2.4.1"", ""children"": [{""text"": ""Factor immobility and regional impacts of trade liberalization: Evidence on poverty from India"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". American Economic Journal: Applied Economics, 2(4), 1-41."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d3fa0bb68da20d625a567567171202ad6e12c512"": {""id"": ""d3fa0bb68da20d625a567567171202ad6e12c512"", ""index"": 10, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""There are different ways of capturing this correlation. I focus here on all countries with data over the period 1945-2014. You can find a similar chart using different data sources and time periods in Ventura, J. (2005). A global view of economic growth. Handbook of economic growth, 1, 1419-1497. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://repositori.upf.edu/bitstream/handle/10230/1248/849.pdf?sequence=1"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d48543993e3185cf5ae81bc81779c339e96b7d2d"": {""id"": ""d48543993e3185cf5ae81bc81779c339e96b7d2d"", ""index"": 40, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This issue is actually also a source of disagreement between National Accounts data and customs data. You can read more about it in this report: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20221115005121/https://www.imf.org/external/pubs/ft/bop/2012/12-30.pdf"", ""children"": [{""text"": ""Harrison, Anne (2013) FOB/CIF Issue in Merchandise Trade/Transport of Goods in BPM6 and the 2008 SNA, Twenty-Fifth Meeting of the IMF Committee on Balance of Payments Statistics, Washington, D.C"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d85bdbbaa3b805a965d3eac160b7cc09fcb76d30"": {""id"": ""d85bdbbaa3b805a965d3eac160b7cc09fcb76d30"", ""index"": 17, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""You can read more about these economic concepts, and the related predictions from economic theory, in Chapter 18 of the textbook "", ""spanType"": ""span-simple-text""}, {""url"": ""https://core-econ.org/the-economy/book/text/18.html"", ""children"": [{""text"": ""The Economy: Economics for a Changing World"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""db335036a3a254ba8dd38b184cf33c1942e72818"": {""id"": ""db335036a3a254ba8dd38b184cf33c1942e72818"", ""index"": 44, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For more details on this see Forstater, M. (2018) "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.cgdev.org/publication/illicit-financial-flows-trade-misinvoicing-and-multinational-tax-avoidance"", ""children"": [{""text"": ""Illicit Financial Flows, Trade Misinvoicing, and Multinational Tax Avoidance: The Same or Different?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", CGD Policy Paper 123."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ddf308fc7f17ee0efa0913851917c536c5916d96"": {""id"": ""ddf308fc7f17ee0efa0913851917c536c5916d96"", ""index"": 28, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Berlingieri, G., Breinlich, H., & Dhingra, S. (2018). The Impact of Trade Agreements on Consumer Welfare—Evidence from the EU Common External Trade Policy. Journal of the European Economic Association."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e5fca460d06d7eedf009d77e28bcc0e64ec3bfa2"": {""id"": ""e5fca460d06d7eedf009d77e28bcc0e64ec3bfa2"", ""index"": 38, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The chart includes series labeled by the sources as 'merchandise trade' and 'goods trade'. As we explain below, part of the asymmetries in trade data comes from the fact that, although 'merchandise' and 'goods' are equivalent in the dictionary, these two terms often measure related but different things."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e6f9fb6ec6ee7a0cd970fb183a7ad2e2dee2c053"": {""id"": ""e6f9fb6ec6ee7a0cd970fb183a7ad2e2dee2c053"", ""index"": 18, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""David, H., Dorn, D., & Hanson, G. H. (2013). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.aeaweb.org/articles?id=10.1257/aer.103.6.2121"", ""children"": [{""text"": ""The China syndrome: Local labor market effects of import competition in the United States"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". American Economic Review, 103(6), 2121-68."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e709bfa6f51748845d0b836492415ce924397143"": {""id"": ""e709bfa6f51748845d0b836492415ce924397143"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This chart was inspired by a chart from Helpman, E., Melitz, M., & Rubinstein, Y. (2007). Estimating trade flows: Trading partners and trading volumes (No. w12927). National Bureau of Economic Research."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e834b0b3fdd4ac3bcff756498cfc2af694d6cec8"": {""id"": ""e834b0b3fdd4ac3bcff756498cfc2af694d6cec8"", ""index"": 34, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Melitz, J. (2008). Language and foreign trade. European Economic Review, 52(4), 667-699."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f12637956bbf72f83a9c7e0f7c016023dae58367"": {""id"": ""f12637956bbf72f83a9c7e0f7c016023dae58367"", ""index"": 43, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The OECD approach consists of four steps, which they describe as follows: \""First, data are collected and organized, and imports are converted to FOB prices to match the valuation of exports. Secondly, data are adjusted for several specific large problems known to drive asymmetries. Presently these include “modular” adjustments for unallocated and confidential trade; for exports by Hong Kong, China; for Swiss non-monetary gold; and for clear-cut cases of product misclassifications. The list of modules is expected to grow over time. In the third step, adjusted data are balanced using a “Symmetry Index” that weights exports and imports. As the final step, the data are also converted to Classification of Products by Activity (CPA) products to better align with National Accounts statistics, such as in national Supply-Use tables.\"" You can read more about it "", ""spanType"": ""span-simple-text""}, {""url"": ""https://oecdecoscope.blog/2017/12/22/statistical-insights-merchandise-trade-statistics-without-asymmetries/"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". In addition to the OECD, other sources also use corrections. The IMF's DOTS dataset, for example, uses a 6 percent rule for converting import valuations (in CIF) into export values (in FOB). More information can be found in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20220208150732/https://www.elibrary.imf.org/view/journals/001/2018/016/001.2018.issue-016-en.xml"", ""children"": [{""text"": ""the IMF's (2018) working paper on 'New Estimates for Direction of Trade Statistics'."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f22d0dbbf5f0fcd77e6d6919396e4c18cc92c5cb"": {""id"": ""f22d0dbbf5f0fcd77e6d6919396e4c18cc92c5cb"", ""index"": 16, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Bloom, N., Draca, M., & Van Reenen, J. (2016). Trade induced technical change? The impact of Chinese imports on innovation, IT and productivity. The Review of Economic Studies, 83(1), 87-117. Available online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20170810215533/http://bfi.uchicago.edu/sites/default/files/research/Van%20Reenen_Trade%20Induced%20Technical%20Change.pdf"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f6de5b7ea6a9825366c680b2d71fe2eda84ac38e"": {""id"": ""f6de5b7ea6a9825366c680b2d71fe2eda84ac38e"", ""index"": 30, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Bernhofen, D., & Brown, J. (2004). A Direct Test of the Theory of Comparative Advantage: The Case of Japan. Journal of Political Economy, 112(1), 48-67. doi:1. Retrieved from http://www.jstor.org/stable/10.1086/379944 doi:1"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f7e7b5ce0ac7f6995afb3455efe8d6a4c617ef74"": {""id"": ""f7e7b5ce0ac7f6995afb3455efe8d6a4c617ef74"", ""index"": 25, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See: (i) Feenstra, R. C., & Weinstein, D. E. (2017). "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.nber.org/papers/w15749"", ""children"": [{""text"": ""Globalization, markups, and US welfare"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Journal of Political Economy, 125(4), 1040-1074. (ii) Fajgelbaum, P. D., & Khandelwal, A. K. (2016). "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.econ.ucla.edu/pfajgelbaum/mugft_qje.pdf"", ""children"": [{""text"": ""Measuring the unequal gains from trade"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The Quarterly Journal of Economics, 131(3), 1113-1180."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""fb048779f0a2100d1fdf2f5ff6f3f6db29231851"": {""id"": ""fb048779f0a2100d1fdf2f5ff6f3f6db29231851"", ""index"": 36, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For more information on how the COW trade datasets were constructed see: (i) Barbieri, Katherine, and Omar M. G. Omar Keshk. 2016. Correlates of War Project Trade Data Set Codebook, Version 4.0. Available at "", ""spanType"": ""span-simple-text""}, {""url"": ""http://correlatesofwar.org"", ""children"": [{""text"": ""http://correlatesofwar.org"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and (ii) Barbieri, Katherine, Omar M. G. Keshk, and Brian Pollins. 2009. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.researchgate.net/publication/49518195_Trading_Data_Evaluating_Our_Assumptions_and_Coding_Rules"", ""children"": [{""text"": ""TRADING DATA: Evaluating our Assumptions and Coding Rules."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" Conflict Management and Peace Science, 26(5): 471–491."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""fc4388eef523c3aaef74a8f8be1156ea2ae80dbd"": {""id"": ""fc4388eef523c3aaef74a8f8be1156ea2ae80dbd"", ""index"": 20, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Magyari, I. (2017). "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.columbia.edu/~im2348/JMP_Magyari.pdf"", ""children"": [{""text"": ""Firm Reorganization, Chinese Imports, and US Manufacturing Employment"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". US Census Bureau, Center for Economic Studies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""fd71cbbf3f749a164b6cc1d144efbf4ab5b89b2e"": {""id"": ""fd71cbbf3f749a164b6cc1d144efbf4ab5b89b2e"", ""index"": 39, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For example, if there is no change in ownership (e.g. a firm exports goods to its factory in another country for processing, and then re-imports the processed goods) the manual says that statistical agencies should only record the net difference in value. You can find more details about this in an "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.oecd.org/sdd/na/new-standards-for-compiling-national-accounts-SNA2008-OECDSB20.pdf"", ""children"": [{""text"": ""OECD Statistics Briefing"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""linear-topic-page"", ""title"": ""Trade and Globalization"", ""authors"": [""Esteban Ortiz-Ospina"", ""Diana Beltekian"", ""Max Roser""], ""excerpt"": ""How did international trade and globalization change over time? What do they look like today? And what are their impacts?"", ""dateline"": ""This page was first published in 2014 and last revised in April 2024."", ""subtitle"": ""How did international trade and globalization change over time? What is the structure today? And what is its impact?"", ""sidebar-toc"": true, ""featured-image"": ""trade-and-globalization-thumbnail.png""}",1,2023-11-10 14:14:22,2018-10-01 13:16:00,2023-12-28 16:31:11,unlisted,ALBJ4LsGp4SIu0H1hMBgOXPX9ibpXTIO6RJp8E5ZFfYpN7ifBNqm98E52KjnwFAV0OBHLo7XBIo0k4ypPchYBw,,"In this topic page we analyze available data and research on international trade patterns, including the determinants and consequences of globalization over the last couple of decades. Here is an overview of the main points we cover below. **Related topics:** * [Is trade a major driver of income inequality?](https://ourworldindata.org/trade-and-income-inequality) – a brief discussion of the link between globalization and income inequality. * [Is globalization an engine of economic development?](https://ourworldindata.org/is-globalization-an-engine-of-economic-development) – an overview of the main arguments linking globalization and economic development. **[See all interactive charts on Trade and Globalization ↓](#all-charts)** --- # Trade has changed the world economy --- ## Trade has grown remarkably over the last century The integration of national economies into a global economic system has been one of the most important developments of the last century. This process of integration, often called Globalization, has materialized in a remarkable growth in trade between countries. The chart here shows the value of world exports over the period 1800-2014. These estimates are in constant prices (i.e. have been adjusted to account for inflation) and are indexed at 1913 values. This chart shows an extraordinary growth in international trade over the last couple of centuries: Exports today are more than 40 times larger than in 1913. You can click on the option marked 'Linear', on top of the vertical axis, to change into a logarithmic scale. This will help you see that, over the long run, growth has roughly followed an exponential path. ### Trade has grown more than proportionately with GDP The chart above shows how much more trade we have today relative to a century ago. But what about trade relative to total economic output? Over the last couple of centuries the world economy has experienced [sustained positive economic growth](https://ourworldindata.org/grapher/world-gdp-over-the-last-two-millennia), so looking at changes in trade relative to GDP offers another interesting perspective. The next chart plots the value of trade in goods relative to GDP (i.e. the value of merchandise trade as a share of global economic output). Up to 1870, the sum of worldwide exports accounted for less than 10% of global output. Today, the value of exported goods around the world is close to 25%. This shows that over the last hundred years of economic growth, there has been more than proportional growth in global trade. _(NB. In this chart you can add countries by choosing the option on the bottom left; or you can compare countries around the world by clicking on 'Map' on the chart.)_ ### Today trade is a fundamental part of economic activity everywhere In today's global economic system, countries exchange not only final products, but also intermediate inputs. This creates an intricate network of economic interactions that cover the whole world. The interactive data visualization, created by the [London-based data visualisation studio Kiln](https://www.kiln.digital/) and the [UCL Energy Institute](http://www.bartlett.ucl.ac.uk/energy), gives us an insight into the complex nature of trade. It plots the position of cargo ships across the oceans. --- # Trade generates efficiency gains --- ## The raw correlation between trade and growth Over the last couple of centuries the world economy has experienced [sustained positive economic growth](https://ourworldindata.org/grapher/world-gdp-over-the-last-two-millennia), and over the same period, this process of economic growth has been accompanied by [even faster growth in global trade](https://ourworldindata.org/grapher/merchandise-exports-gdp-cepii?country=~OWID_WRL). In a similar way, if we look at country-level data from the last half century we find that there is also a correlation between economic growth and trade: countries with higher rates of GDP growth also tend to have higher rates of growth in trade as a share of output. This basic correlation is shown in the chart here, where we plot average annual change in real GDP per capita, against growth in trade (average annual change in value of exports as a share of GDP).1 Is this statistical association between economic output and trade causal? Among the potential growth-enhancing factors that may come from greater global economic integration are: Competition (firms that fail to adopt new technologies and cut costs are more likely to fail and to be replaced by more dynamic firms); Economies of scale (firms that can export to the world face larger demand, and under the right conditions, they can operate at larger scales where the price per unit of product is lower); Learning and innovation (firms that trade gain more experience and exposure to develop and adopt technologies and industry standards from foreign competitors).2 Are these mechanisms supported by the data? Let's take a look at the available empirical evidence. ## Causality: Evidence from cross-country differences in trade, growth and productivity When it comes to academic studies estimating the impact of trade on GDP growth, the most cited paper is Frankel and Romer (1999).3 In this study, Frankel and Romer used geography as a proxy for trade, in order to estimate the impact of trade on growth. This is a classic example of the so-called [instrumental variable approach](https://en.wikipedia.org/wiki/Instrumental_variables_estimation). The idea is that a country's geography is fixed, and mainly affects national income through trade. So if we observe that a country's distance from other countries is a powerful predictor of economic growth (after accounting for other characteristics), then the conclusion is drawn that it must be because _trade_ has an effect on economic growth. Following this logic, Frankel and Romer find evidence of a strong impact of trade on economic growth. Other papers have applied the same approach to richer cross-country data, and they have found similar results. A key example is Alcalá and Ciccone (2004).4 This body of evidence suggests trade is indeed one of the factors driving national average incomes (GDP per capita) and macroeconomic productivity (GDP per worker) over the long run.5 ## Causality: Evidence from changes in labor productivity at the firm level If trade is causally linked to economic growth, we would expect that trade liberalization episodes also lead to firms becoming more productive in the medium, and even short run. There is evidence suggesting this is often the case. Pavcnik (2002) examined the effects of liberalized trade on plant productivity in the case of Chile, during the late 1970s and early 1980s. She found a positive impact on firm productivity in the import-competing sector. And she also found evidence of aggregate productivity improvements from the reshuffling of resources and output from less to more efficient producers. 6 Bloom, Draca and Van Reenen (2016) examined the impact of rising Chinese import competition on European firms over the period 1996-2007, and obtained similar results. They found that innovation increased more in those firms most affected by Chinese imports. And they found evidence of efficiency gains through two related channels: innovation increased and new existing technologies were adopted within firms; and aggregate productivity also increased because employment was reallocated towards more technologically advanced firms.7 ## Wrapping up: Trade does generate efficiency gains On the whole, the available evidence suggests trade liberalization does improve economic efficiency. This evidence comes from different political and economic contexts, and includes both micro and macro measures of efficiency. This result is important, because it shows that there are gains from trade. But of course efficiency is not the only relevant consideration here. As we discuss in a [companion blog post](https://ourworldindata.org/trade-and-income-inequality), the efficiency gains from trade are not generally equally shared by everyone. The evidence from the impact of trade on firm productivity confirms this: ""reshuffling workers from less to more efficient producers"" means closing down some jobs in some places. Because distributional concerns are real it is important to promote public policies – such as unemployment benefits and other safety-net programs – that help redistribute the gains from trade. --- # Trade has distributional consequences --- ## The conceptual link between trade and household welfare When a country opens up to trade, the demand and supply of goods and services in the economy shift. As a consequence, local markets respond, and prices change. This has an impact on households, both as consumers and as wage earners. The implication is that trade has an impact on everyone. It's not the case that the effects are restricted to workers from industries in the trade sector; or to consumers who buy imported goods. The effect of trade extends to everyone because markets are interlinked, so imports and exports have knock-on effects on all prices in the economy, including those in non-traded sectors. Economists usually distinguish between ""general equilibrium consumption effects"" (i.e. changes in consumption that arise from the fact that trade affects the prices of non-traded goods relative to traded goods) and ""general equilibrium income effects"" (i.e. changes in wages that arise from the fact that trade has an impact on the demand for specific types of workers, who could be employed in both the traded and non-traded sectors). Considering all these complex interrelations, it's not surprising that economic theories predict that not everyone will benefit from international trade in the same way. The distribution of the gains from trade depends on what different groups of people consume, and which types of jobs they have, or could have. _(NB. You can read more about these economic concepts, and the related predictions from economic theory, in Chapter 18 of the textbook __[The Economy: Economics for a Changing World](https://core-econ.org/the-economy/book/text/18.html)__.)_ ## The link between trade, jobs and wages ### Evidence from Chinese imports and their impact on factory workers in the US The most famous study looking at this question is Autor, Dorn and Hanson (2013): ""The China syndrome: Local labor market effects of import competition in the United States"".8 In this paper, Autor and coauthors looked at how local labor markets changed in the parts of the country most exposed to Chinese competition, and they found that rising exposure increased unemployment, lowered labor force participation, and reduced wages. Additionally, they found that claims for unemployment and healthcare benefits also increased in more trade-exposed labor markets. The visualization here is one of the key charts from their paper. It's a scatter plot of cross-regional exposure to rising imports, against changes in employment. Each dot is a small region (a 'commuting zone' to be precise). The vertical position of the dots represents the percent change in manufacturing employment for working age population; and the horizontal position represents the predicted exposure to rising imports (exposure varies across regions depending on the local weight of different industries). The trend line in this chart shows a negative relationship: more exposure goes together with less employment. There are large deviations from the trend (there are some low-exposure regions with big negative changes in employment); but the paper provides more sophisticated regressions and robustness checks, and finds that this relationship is statistically significant. This result is important because it shows that the labor market adjustments were large. Many workers and communities were affected over a long period of time.9 But it's also important to keep in mind that Autor and colleagues are only giving us a partial perspective on the total effect of trade on employment. In particular, comparing changes in employment at the regional level misses the fact that firms operate in multiple regions and industries at the same time. Indeed, [Ildikó Magyari recently found evidence](http://www.columbia.edu/~im2348/JMP_Magyari.pdf) suggesting the Chinese trade shock provided incentives for US firms to diversify and reorganize production.10 So companies that outsourced jobs to China often ended up closing some lines of business, but at the same time expanded other lines elsewhere in the US. This means that job losses in some regions subsidized new jobs in other parts of the country. On the whole, Magyari finds that although Chinese imports may have reduced employment within some establishments, these losses were more than offset by gains in employment within the same firms in other places. This is no consolation to people who lost their job. But it is necessary to add this perspective to the simplistic story of ""trade with China is bad for US workers"". ### Evidence from the expansion of trade in India and the impact on poverty reductions Another important paper in this field is Topalova (2010): ""Factor immobility and regional impacts of trade liberalization: Evidence on poverty from India"".11 In this paper Topalova looks at the impact of trade liberalization on poverty across different regions in India, using the sudden and extensive [change](https://ourworldindata.org/grapher/trade-as-share-of-gdp?country=IND) in India's trade policy in 1991. She finds that rural regions that were more exposed to liberalization, experienced a slower decline in poverty, and had lower consumption growth. In the analysis of the mechanisms underlying this effect, Topalova finds that liberalization had a stronger negative impact among the least geographically mobile at the bottom of the income distribution, and in places where labor laws deterred workers from reallocating across sectors. The evidence from India shows that (i) discussions that only look at ""winners"" in poor countries and ""losers"" in rich countries miss the point that the gains from trade are unequally distributed within both sets of countries; and (ii) context-specific factors, like worker mobility across sectors and geographic regions, are crucial to understand the impact of trade on incomes. ### Evidence from other studies * Donaldson (2018) uses archival data from colonial India to estimate the impact of India’s vast railroad network. He finds railroads increased trade, and in doing so they increased real incomes (and reduced income volatility).12 * Porto (2006) looks at the distributional effects of [Mercosur](https://en.wikipedia.org/wiki/Mercosur) on Argentine families, and finds this regional trade agreement led to benefits across the entire income distribution. He finds the effect was progressive: poor households gained more than middle-income households, because prior to the reform, trade protection benefitted the rich disproportionately.13 * Trefler (2004) looks at the Canada-US Free Trade Agreement and finds there was a group who bore ""adjustment costs"" (displaced workers and struggling plants) and a group who enjoyed ""long-run gains"" (consumers and efficient plants). 14 ## The link between trade and the cost of living The fact that trade negatively affects labor market opportunities for specific groups of people does not necessarily imply that trade has a negative aggregate effect on household welfare. This is because, while trade affects wages and employment, it also affects the prices of consumption goods. So households are affected both as consumers and as wage earners. Most studies focus on the earnings channel, and try to approximate the impact of trade on welfare by looking at how much wages can buy, using as reference the changing prices of a fixed basket of goods. This approach is problematic because it fails to consider [welfare gains from increased product variety](http://www.nber.org/papers/w15749), and obscures complicated distributional issues such as the fact that poor and rich individuals consume different baskets so [they benefit differently from changes in relative prices](http://www.econ.ucla.edu/pfajgelbaum/mugft_qje.pdf).15 Ideally, studies looking at the impact of trade on household welfare should rely on fine-grained data on prices, consumption and earnings. This is the approach followed in Atkin, Faber, and Gonzalez-Navarro (2018): ""Retail globalization and household welfare: Evidence from Mexico"".16 Atkin and coauthors use a uniquely rich dataset from Mexico, and find that the arrival of global retail chains led to reductions in the incomes of traditional retail sector workers, but had little impact on average municipality-level incomes or employment; and led to lower costs of living for both rich and poor households. The chart here shows the estimated distribution of total welfare gains across the household income distribution (the light-gray lines correspond to confidence intervals). These are proportional gains, and are expressed as percent of initial household income. As we can see, there is a net positive welfare effect across all income groups; but these improvements in welfare are regressive, in the sense that richer households gain proportionally more (about 7.5 percent gain compared to 5 percent).17 Evidence from other countries confirms this is not an isolated case – the expenditure channel really seems to be an important and understudied source of household welfare. Giuseppe Berlingieri, Holger Breinlich, Swati Dhingra, for example, investigate the consumer benefits from trade agreements implemented by the EU between 1993 and 2013; and they find that these trade agreements increased the quality of available products, which translated into a cumulative reduction in consumer prices equivalent to savings of €24 billion per year for EU consumers.18 ## Wrapping up: Net welfare effects and implications The available evidence shows that, for some groups of people, trade has a negative effect on wages and employment opportunities; and at the same time it has a large positive effect via lower consumer prices and increased availability of products. Two points are worth emphasising. For some households, the net effect is positive. But for some households that's not the case. In particular, workers who lose their job can be affected for extended periods of time, so the positive effect via lower prices is not enough to compensate them for the reduction in earnings. On the whole, if we aggregate changes in welfare across households, the net effect is usually positive. But this is hardly a consolation for those who are worse off. This highlights a complex reality: There are [aggregate gains from trade](https://ourworldindata.org/trade-and-econ-growth), but there are also real distributional concerns. Even if [trade is not a major driver of income inequalities](https://ourworldindata.org/trade-and-income-inequality), it's important to keep in mind that public policies, such as unemployment benefits and other safety-net programs, can and should help redistribute the gains from trade. --- # Trade from a historical perspective --- ## The ""two waves of globalization"" ### The first ""wave of globalization"" started in the 19th century, the second one after WW2 The following visualization presents a compilation of available trade estimates, showing the evolution of world exports and imports as a share of [global economic output](https://ourworldindata.org/gdp-data/). This metric (the ratio of total trade, exports plus imports, to global GDP) is known as the 'openness index'. The higher the index, the higher the influence of trade transactions on global economic activity.19 As we can see, until 1800 there was a long period characterized by persistently low international trade – globally the index never exceeded 10% before 1800. This then changed over the course of the 19th century, when technological advances triggered a period of marked growth in world trade – the so-called 'first wave of globalization'. The first wave of globalization came to an end with the beginning of the First World War, when the decline of liberalism and the rise of nationalism led to a slump in international trade. In the chart we see a large drop in the interwar period. After the Second World War trade started growing again. This new – and ongoing – wave of globalization has seen international trade grow faster than ever before. Today the sum of exports and imports across nations amounts to more than 50% of the value of total global output. _(NB. Klasing and Milionis (2014), which is one of the sources in the chart, published an additional set of estimates under an alternative specification. Similarly, for the period 1960-2015, the World Bank's World Development Indicators published an alternative set of estimates, which are similar but not identical to those included from the Penn World Tables (9.1). You find all these alternative overlapping sources in __[this comparison chart](http://ourworldindata.org/grapher/globalization-over-5-centuries)__.)_ ### Before the first wave of globalization, trade was driven mostly by colonialism Over the early modern period, transoceanic flows of goods between empires and colonies accounted for an important part of international trade. The following visualizations provides a comparison of intercontinental trade, in per capita terms, for different countries. As we can see, intercontinental trade was very dynamic, with volumes varying considerably across time and from empire to empire. Leonor Freire Costa, Nuno Palma, and Jaime Reis, who compiled and published the original data shown here, argue that trade, also in this period, had a substantial positive impact on the economy.20 ### The first wave of globalization was marked by the rise and collapse of intra-European trade The following visualization shows a detailed overview of Western European exports by destination. Figures correspond to export-to-GDP ratios (i.e. the sum of the value of exports from all Western European countries, divided by total GDP in this region). Using the option labeled 'relative', at the bottom of the chart, you can see the proportional contribution of each region to total Western European exports. This chart shows that growth in Western European trade throughout the 19th century was largely driven by trade within the region: In the period 1830-1900 intra-European exports went from 1% of GDP to 10% of GDP; and this meant that the relative weight of intra-European exports doubled over the period (in the 'relative' view you can see the changing composition of exports by destination, and you can check that the weight of intra-European trade went from about one third to about two thirds over the period). But this process of European integration then collapsed sharply in the interwar period. After the Second World War trade within Europe rebounded, and from the 1990s onwards exceeded the highest levels of the first wave of globalization. In addition Western Europe then started to increasingly trade with Asia, the Americas, and to a smaller extent Africa and Oceania. The next graph, from Broadberry and O'Rourke (2010)21, shows another perspective on the integration of the global economy and plots the evolution of three indicators measuring integration across different markets – specifically goods, labor, and capital markets. The indicators in this chart are indexed, so they show changes relative to the levels of integration observed in 1900. This gives us another viewpoint to understand how quickly global integration collapsed with the two World Wars. _(NB. Integration in the goods markets is measured here through the 'trade openness index', which is defined by the sum of exports and imports as share of GDP. __[In this interactive chart](https://ourworldindata.org/grapher/trade-openness-in-europe?country=FRA+DEU+ITA+ESP+SWE+GBR)__ you can explore trends in trade openness over this period for a selection of European countries.)_ ### The second wave of globalization was enabled by technology The world-wide expansion of trade after the Second World War was largely possible because of reductions in transaction costs stemming from technological advances, such as the development of commercial civil aviation, the improvement of productivity in the merchant marines, and the democratization of the telephone as the main mode of communication. The visualization shows how, at the global level, costs across these three variables have been going down since 1930. The reductions in transaction costs had an impact, not only on the volumes of trade, but also on the types of exchanges that were possible and profitable. The first wave of globalization was characterized by inter-industry trade. This means that countries exported goods that were very different to what they imported – England exchanged machines for Australian wool and Indian tea. As transaction costs went down, this changed. In the second wave of globalization we are seeing a rise in _intra_-industry trade (i.e. the exchange of broadly similar goods and services is becoming more and more common). France, for example, now both imports and exports machines to and from Germany. The following visualization, from the [UN World Development Report (2009)](http://siteresources.worldbank.org/INTWDRS/Resources/477365-1327525347307/8392086-1327528510568/WDR09_12_Ch06web.pdf), plots the fraction of total world trade that is accounted for by intra-industry trade, by type of goods. As we can see, intra-industry trade has been going up for primary, intermediate and final goods. This pattern of trade is important because the scope for specialization increases if countries are able to exchange intermediate goods (e.g. auto parts) for related final goods (e.g. cars). ## Two centuries of trade, country by country Above we took a look at the broad global trends over the last two centuries. Let's now zoom in on country-level trends over this long and dynamic period. This chart plots estimates of the value of trade in goods, relative to total economic activity (i.e. export-to-GDP ratios). These historical estimates obviously come with a large margin of error (in the [measurement section below](https://ourworldindata.org/trade-and-globalization#measurement-and-data-quality) we discuss the data limitations); yet they offer an interesting perspective. You can add more series by clicking on the option 'Add country'. Each country tells a different story. If you add the Netherlands, for example, you will see how important the [Dutch Golden Age](https://en.wikipedia.org/wiki/Dutch_Golden_Age) was. _(NB. __[Here is the same chart but showing imports](https://ourworldindata.org/grapher/merchandise-imports-gdp-cepii)__, rather than exports.)_ ### Changing trade partners In the next chart we plot, country by country, the regional breakdown of exports. India is shown by default, but you can switch country using the option 'Change entity'. Using the option 'relative', at the bottom of the chart, you can see the proportional contribution of purchases from each region. For example: We see that 48% of the total value of Indian exports in 2014 went to Asian countries. This gives us an interesting perspective on the changing nature of trade partnerships. In India, we see the rising importance of trade with Africa – this is a pattern that we [discuss in more detail below](https://ourworldindata.org/trade-and-globalization#south-south-trade-is-becoming-increasingly-important). --- # Trade around the world today --- ## How much do countries trade? ### Trade openness around the world The so-called trade openness index is an economic metric calculated as the ratio of country's total trade (the sum of exports plus imports) to the country's gross domestic product. This metric gives us an idea of integration, because it captures all incoming and outgoing transactions. The higher the index the larger the influence of trade on domestic economic activities. The visualization presents a world map showing the trade openness index country by country. You can explore country-specific time series by clicking on a country, or by using the 'Chart' tab. For any given year, we see that there is a lot of variation across countries. The weight of trade in the US economy, for example, is much lower than in other rich countries. If you press the play button in the map, you can see changes over time. This reveals that, despite the great variation between countries, there is a common trend: Over the last couple of decades trade openness has gone up in most countries. ### Exports and imports in real dollars Expressing trade values as a share of GDP tells us the importance of trade in relation to the size of economic activity. Let's now take a look at trade in monetary terms – this tells us the importance of trade in absolute, rather than relative terms. The chart shows the value of exports (goods plus services) in dollars, country by country. All estimates are expressed in constant 2010 dollars (i.e. all values have been adjusted to correct for inflation). The main takeaway here are the country-specific trends, which are positive and more pronounced than in the charts showing shares of GDP. This is not surprising: most countries today [produce more than a couple of decades ago](https://ourworldindata.org/app/uploads/2013/05/Scatter-1960-vs-2014-GDP.png); and at the same time they trade more of what they produce. You can plot trends by region using the option 'Add country'. _(NB. __[Here is the same chart, but showing imports](https://ourworldindata.org/grapher/imports-of-goods-and-services-constant-2010-us)__ rather than exports.)_ ## What do countries trade? ### Trade in goods vs Trade in services Trade transactions include goods (tangible products that are physically shipped across borders by road, rail, water, or air) and services (intangible commodities, such as tourism, financial services, and legal advice). Many traded services make merchandise trade easier or cheaper—for example, shipping services, or insurance and financial services. Trade in goods has [been happening for millenia](https://www.caitlingreen.org/2017/03/a-very-long-way-from-home.html); while trade in services is a relatively recent phenomenon. In some countries services are today an important driver of trade: In the UK services account for about 45% of all exports; and in the Bahamas almost all exports are services (about 87% in 2016). In other countries the opposite is true: In Nigeria and Venezuela services accounted for around 2% and 3% of exports, respectively, in 2014. Globally, trade in goods accounts for the majority of trade transactions. But as this chart shows, the share of services in total global exports has increased, from 17% in 1979 to 24% in 2017. _(NB. __[This interactive chart](https://ourworldindata.org/grapher/service-exports-and-imports-gdp)__ shows trade in services as share of GDP across countries and regions.)_ ### Domestic vs Foreign value added in exports Firms around the world import goods and services, in order to use them as inputs to produce goods and services that are later exported. The imported goods and services incorporated in a country’s exports are a key indicator of economic integration – they tell us something about 'global value chains', where the different stages of the production process are located across different countries. The chart, from UNCTAD's [World Investment Report 2018 - Investment and New Industrial Policies](https://unctad.org/en/PublicationsLibrary/wir2018_en.pdf), shows trends of gross exports, broken down into domestic and foreign value added. That is, the share of the value of exports that comes from foreign inputs. Today, about 30% of the value of global exports comes from foreign inputs. In 1990, the share was about 25%. Foreign value added in trade peaked in 2010–2012 after two decades of continuous increase. This is consistent with the fact that, after the global financial crisis, there has been a [slowdown in the rate of growth of trade in goods and services, relative to global GDP](https://ourworldindata.org/grapher/trade-as-share-of-gdp?tab=chart&country=OWID_WRL). This is a sign that global integration stalled after the financial crisis. _(NB. The integration of global value chains is a common source of measurement error in trade data, because it makes it hard to correctly attribute the origin and destination of goods and services. We discuss this in more detail __[below](https://ourworldindata.org/trade-and-globalization#measurement-and-data-quality)__.)_ ## How are trade partnerships changing? ### Bilateral trade is becoming increasingly common If we consider all pairs of countries that engage in trade around the world, we find that in the majority of cases, there is a bilateral relationship today: Most countries that export goods to a country, also import goods from the same country. The interactive visualization shows this.23 In this chart, all possible country pairs are partitioned into three categories: the top portion represents the fraction of country pairs that do not trade with one-another; the middle portion represents those that trade in both directions (they export to one-another); and the bottom portion represents those that trade in one direction only (one country imports from, but does not export to, the other country). As we can see, bilateral trade is becoming increasingly common (the middle portion has grown substantially). But it remains true that many countries still do not trade with each other at all (in 2014 about 25% of all country-pairs recorded no trade). ### South-South trade is becoming increasingly important The visualization here shows the share of world merchandise trade that corresponds to exchanges between today's rich countries and the rest of the world. The 'rich countries' in this chart are: Australia, Austria, Belgium, Canada, Cyprus, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom and the United States. 'Non-rich countries' are all the other countries in the world. As we can see, up until the Second World War the majority of trade transactions involved exchanges between this small group of rich countries. But this has been changing quickly over the last couple of decades, and today trade between non-rich countries is just as important as trade between rich countries. In the past two decades China has been a key driver of this dynamic: the [UN Human Development Report (2013)](http://hdr.undp.org/sites/default/files/reports/14/hdr2013_en_complete.pdf) estimates that between 1992 and 2011, China's trade with Sub-Saharan Africa rose from $1 billion to more than $140 billion. _(NB. __[Here is a stacked area chart showing the total composition of exports by partnership](https://ourworldindata.org/grapher/share-of-world-merchandise-trade-by-type-of-trade)__. It's the same data, but plotted with stacked series.)_ ### The majority of preferential trade agreements are between emerging economies The last few decades have not only seen an increase in the volume of international trade, but also an increase in the number of preferential trade agreements through which exchanges take place. A preferential trade agreement is a trade pact that reduces tariffs between the participating countries for certain products. The visualization here shows the evolution of the cumulative number of preferential trade agreements that are in force across the world, according to the World Trade Organization (WTO). These numbers include notified and non-notified preferential agreements (the source reports that only about two-thirds of the agreements currently in force have been notified to the WTO), and are disaggregated by country groups. This figure shows the increasingly important role of trade between developing countries (South-South trade), vis-a-vis trade between developed and developing countries (North-South trade). In the late 1970s, North-South agreements accounted for more than half of all agreements – in 2010, they accounted for about one quarter. Today, the majority of preferential trade agreements are between developing economies. ### Trading patterns have been changing quickly in middle income countries The increase in trade among emerging economies over the last half century has been accompanied by an important change in the composition of exported goods in these countries. The next visualization plots the share of food exports in each country's total exported merchandise. These figures, produced by the World Bank, correspond to the Standard International Trade Classification, in which 'food' includes, among other goods, live animals, beverages, tobacco, coffee, oils, and fats. Two points stand out. First, there has been a substantial decrease in the relative importance of food exports since 1960s in most countries (although globally in the last decade it has gone up slightly). And second, this decrease has been largest in middle income countries, particularly in Latin America. Colombia is a notable case in point: food went from 77% of merchandise exports in 1962, to 15.9% in 2015. Regarding levels, as one would expect, in high income countries food still accounts for a much smaller share of merchandise exports than in most low- and middle-income-countries. --- # Explaining trade patterns: Theory and Evidence --- ## Comparative advantage ### Theory: What is 'comparative advantage' and why does it matter to understand trade? In economic theory, the 'economic cost' – or the 'opportunity cost' – of producing a good is the value of everything you need to give up in order to produce that good. Economic costs include physical inputs (the value of the stuff you use to produce the good), plus forgone opportunities (when you allocate scarce resources to a task, you give up alternative uses of those resources). A country or a person is said to have a 'comparative advantage' if they have the ability to produce something at a lower opportunity cost than their trade partners. The forgone opportunities of production are key to understand this concept. It is precisely this that distinguishes absolute advantage from comparative advantage. To see the difference between comparative and absolute advantage, consider a commercial aviation pilot and a baker. Suppose the pilot is an excellent chef, and she can bake just as well, or even better than the baker. In this case, the pilot has an absolute advantage in both tasks. Yet the baker probably has a comparative advantage in baking, because the opportunity cost of baking is much higher for the pilot. The freely available economics textbook [The Economy: Economics for a Changing World](https://core-econ.org/the-economy/book/text/18.html) explains this as follows: _""A person or country has comparative advantage in the production of a particular good, if the cost of producing an additional unit of that good relative to the cost of producing another good is lower than another person or country’s cost to produce the same two goods.""_ At the individual level, comparative advantage explains why you might want to delegate tasks to someone else, even if you can do those tasks better and faster than them. This may sound counterintuitive, but it is not: If you are good at many things, it means that investing time in one task has a high opportunity cost, because you are not doing the other amazing things you could be doing with your time and resources. So, at least from an efficiency point of view, you should specialize on what you are best at, and delegate the rest. The same logic applies to countries. Broadly speaking, the principle of comparative advantage postulates that all nations can gain from trade if each specializes in producing what they are relatively more efficient at producing, and import the rest: “do what you do best, import the rest”.24 In countries with relative abundance of certain factors of production, the theory of comparative advantage predicts that they will export goods that rely heavily in those factors: a country typically has a comparative advantage in those goods that use more intensively its abundant resources. Colombia exports bananas to Europe because it has comparatively abundant tropical weather. Under autarky, Colombia would find it cheap to produce bananas relative to e.g. apples. ### Evidence: Is there empirical support for comparative-advantage theories of trade? The empirical evidence suggests that the principle of comparative advantage does help explain trade patterns. Bernhofen and Brown (2004)25, for instance, provide evidence using the experience of Japan. Specifically, they exploit Japan’s dramatic nineteenth-century move from a state of near complete isolation to wide trade openness. The graph here shows the price changes of the key tradable goods after the opening up to trade. It presents a scatter diagram of the net exports in 1869 graphed in relation to the change in prices from 1851–53 to 1869. As we can see, this is consistent with the theory: after opening to trade, the relative prices of major exports such as silk increased (Japan exported what was cheap for them to produce and which was valuable abroad), while the relative price of imports such as sugar declined (they imported what was relatively more difficult for them to produce, but was cheap abroad). ## Trade diminishes with distance The resistance that geography imposes on trade has long been studied in the empirical economics literature – and the main conclusion is that trade intensity is strongly linked to geographic distance. The visualization, from Eaton and Kortum (2002)26, graphs 'normalized import shares' against distance. Each dot represents a country-pair from a set of 19 OECD countries, and both the vertical and horizontal axis are expressed on logarithmic scales. The 'normalized import shares' in the vertical axis provide a measure of how much each country imports from different partners (see the paper for details on how this is calculated and normalised), while distance in the horizontal axis corresponds to the distance between central cities in each country (see the paper and references therein for details on the list of cities). As we can see, there is a strong negative relationship. Trade diminishes with distance. Through econometric modeling, the paper shows that this relationship is not just a correlation driven by other factors: their findings suggest that distance imposes a significant barrier to trade. The fact that trade diminishes with distance is also corroborated by data of trade intensity within countries. The visualization here shows, through a series of maps, the geographic distribution of French firms that export to France's neighboring countries. The colors reflect the percentage of firms which export to each specific country. As we can see, the share of firms exporting to each of the corresponding neighbors is largest close to the border. The authors also show in the paper that this pattern holds for the value of individual-firm exports – trade value decreases with distance to the border. ## Institutions Conducting international trade requires both financial and non-financial institutions to support transactions. Some of these institutions are fairly obvious (e.g. law enforcement); but some are less obvious. For example, the evidence shows that producers in exporting countries often need credit in order to engage in trade. The scatter plot, from Manova (2013)28, shows the correlation between levels in private credit (specifically exporters’ private credit as a share of GDP) and exports (average log bilateral exports across destinations and sectors). As can be seen, financially developed economies – those with more dynamic private credit markets – typically outperform exporters with less evolved financial institutions. Other studies have shown that country-specific institutions, like the knowledge of foreign languages, for instance, are also important to promote foreign relative to domestic trade (see Melitz 200829). ## Increasing returns to scale The concept of comparative advantage predicts that if all countries had identical endowments and institutions, then there would be little incentives for specialization, because the opportunity cost of producing any good would be the same in every country. So you may wonder: why is it then the case that in the last few years we have seen such rapid growth in intra-industry trade between rich countries? The increase in intra-industry between rich countries seems paradoxical under the light of comparative advantage, because in recent decades we have seen convergence in key factors, such as [human capital](https://ourworldindata.org/global-rise-of-education), across these countries. The solution to the paradox is actually not very complicated: Comparative advantage is one, but not the only force driving incentives to specialization and trade. Several economists, most notably Paul Krugman, have developed theories of trade in which trade is not due to differences between countries, but instead due to ""increasing returns to scale"" – an economic term used to denote a technology in which producing extra units of a good becomes cheaper if you operate at a larger scale. The idea is that specialization allows countries to reap greater economies of scale (i.e. to reduce production costs by focusing on producing large quantities of specific products), so trade can be a good idea even if the countries do not differ in endowments, including culture and institutions. These models of trade, often referred to as ‘New Trade Theory’, are helpful to explain why in the last few years we have seen such rapid growth in two-way exchanges of goods within industries between developed nations. In a much cited paper, Evenett and Keller (2002)30 show that both factor endowments and increasing returns help explain production and trade patterns around the world. You can learn more about New Trade Theory, and the empirical support behind it, in [Krugman's Nobel lecture](https://www.nobelprize.org/uploads/2018/06/krugman_lecture.pdf). --- # Measurement and data quality --- There are dozens of official sources of data on international trade, and if you compare these different sources, you will find that they do not agree with one another. Even if you focus on what seems to be the same indicator for the same year in the same country, discrepancies are large. For example, for China in 2010, the estimated total value of goods exports was $1.48 trillion according to [World Bank Data](https://data.worldbank.org/indicator/BX.GSR.MRCH.CD?locations=CN), but it was $1.58 trillion according to [WTO Data.](http://data.imf.org/regular.aspx?key=61545870) That's a difference of about 7%, or a hundred billion US dollars. Such differences between sources can also be found for rich countries where statistical agencies tend to follow international reporting guidelines more closely. In Italy, for example, Eurostat figures of the value of exported goods in 2015 are 10% higher than the merchandise trade figures published by the OECD. And there are also large bilateral discrepancies within sources. According to IMF data, for example, the value of goods that Canada reports exporting to the US is almost $20 billion more that the value of goods that the US reports importing from Canada. Here we explain how international trade data is collected and processed, and why there are such large discrepancies. ## What data is available? The data hubs from several large international organizations publish and maintain extensive cross-country datasets on international trade. Here's a list of the most important ones: * [World Bank Open Data](https://data.worldbank.org/topic/trade) * [IMF Data](http://data.imf.org/?sk=9D6028D4-F14A-464C-A2F2-59B2CD424B85&sId=1515614720959) * [WTO Statistics](https://data.wto.org/en) * [UN Comtrade](https://comtrade.un.org) * [UNCTAD World Integrated Trade Solutions](https://wits.worldbank.org/Default.aspx?lang=en) * [Eurostat](http://ec.europa.eu/eurostat/web/international-trade-in-goods/data/database) * [OECD.Stat](https://stats.oecd.org/Index.aspx?DataSetCode=BIMTS_CPA) In addition to these sources, there are also many other academic projects that publish data on international trade. These projects tend to rely on data from one or more of the sources above; and they typically process and merge series in order to improve coverage and consistency. Three important sources are: * The [Correlates of War Project](http://correlatesofwar.org).31 * The [NBER-United Nations Trade Dataset Project](http://cid.econ.ucdavis.edu/wix.html).32 * The [CEPII Bilateral Trade and Gravity Data Project](http://www.cepii.fr/cepii/en/bdd_modele/presentation.asp?id=32).33 ## How large are discrepancies between sources? In the visualization here we provide a comparison of the data published by several of the sources listed above, country by country, since 1955 up until today. For each country, we exclude trade in services, and we focus only on estimates of the total value of exported goods, expressed as shares of GDP.34 As we can clearly see in this chart, different data sources tell often very different stories. And this is true, to varying degrees, across all countries and years. You can use the option labeled 'change country', at the bottom of the chart, to focus on any country. Constructing this chart was demanding. It required downloading trade data from many different sources, collecting the relevant series, and then standardising them so that the units of measure and the geographical territories were consistent. All series, except the two long-run series from CEPII and NBER-UN, were produced from data published by the sources in current US dollars, and then converted to GDP shares using a unique source (World Bank).35 So, if all series are in the same units (share of national GDP), and they all measure the same thing (value of goods exported from one country to the rest of the world), what explains the differences? Let's dig deeper to understand what's going on. ## Why doesn't the data add up? ### Differences in guidelines used by countries to record and report trade data Broadly speaking, there are two main approaches used to estimate international merchandise trade: * The first approach relies on estimating trade from _customs records_, often complementing or correcting figures with data from enterprise surveys and administrative records associated with taxation. The main manual providing guidelines for this approach is the [International Merchandise Trade Statistics Manual](https://unstats.un.org/unsd/tradekb/Knowledgebase/50154/IMTS-Publication-List) (IMTS). * The second approach relies on estimating trade from _macroeconomic data_, typically _National Accounts_. The main manual providing guidelines for this approach is the [Balance of Payments and International Investment Position Manual](https://www.imf.org/external/pubs/ft/bop/2007/bopman6.htm) (BPM6), which was drafted in parallel with the 2008 [System of National Accounts of the United Nations](https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf) (SNA 2008). The idea behind this approach is recording changes in economic ownership.36 Under these two approaches, it is common to distinguish between 'traded merchandise' and 'traded goods'. The distinction is often made because goods simply being transported through a country (i.e. goods in transit) are not considered to change the stock of material resources of a country, and are hence often excluded from the more narrow concept of 'merchandise trade'. Also, adding to the complexity, countries often rely on measurement protocols that are developed alongside these approaches and concepts that are not perfectly compatible to begin with. In Europe, for example, countries use the ['Compilers guide on European statistics on international trade in goods'.](http://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-02-17-333) ### Measurement error and other inconsistencies Even when two sources rely on the same broad accounting approach, discrepancies arise because countries fail to adhere perfectly to the protocols. In theory, for example, the exports of country A to country B should mirror the imports of country B from country A. But in practice this is rarely the case because of differences in valuation. According to the BPM6, imports and exports should be recorded in the balance of payments accounts on a '_free on board_ (FOB) basis', which means using prices that include all charges up to placing the goods on board a ship at the port of departure. Yet many countries stick to FOB values only for exports, and use CIF values for imports (CIF stands for 'Cost, Insurance and Freight', and includes the costs of transportation).37 The chart here gives you an idea of how large import-export asymmetries are. Shown are the differences between the value of goods that each country reports exporting to the US, and the value of goods that the US reports importing from the same countries. For example, for China, the figure in the chart corresponds to the “Value of merchandise imports in the US from China” minus “Value of merchandise exports from China to the US”. The differences in the chart here, which are both positive and negative, suggest that there is more going on than differences in FOB vs CIF values. If all asymmetries were coming from CIF-FOB differences, then we should only see positive values in the chart (recall that, unlike FOB values, CIF values include the cost of transportation, so CIF values are larger). What else is going on here? Another common source of measurement error relates to the inconsistent attribution of trade partners. An example is failure to follow the guidelines on how to treat goods passing through intermediary countries for processing or merchanting purposes. As global production chains become more complex, countries find it increasingly difficult to unambiguously establish the origin and final destination of merchandise, even when rules are established in the manuals. 38 And there are still more potential sources of discrepancies. For example differences in customs and tax regimes, and differences between ""general"" and ""special"" trade systems (i.e. differences between statistical territories and actual country borders, which do not often coincide because of things like 'custom free zones').39 Even when two sources have identical trade estimates, inconsistencies in published data can arise from differences in exchange rates. If a dataset reports cross-country trade data in US dollars, estimates will vary depending on the exchange rates used. Different exchange rates will lead to conflicting estimates, even if figures in local currency units are consistent. ## Wrapping up Asymmetries in international trade statistics are large and they arise for a variety of reasons. These include conceptual inconsistencies across measurement standards, as well as inconsistencies in the way countries apply agreed protocols. Here's a checklist of issues to keep in mind when comparing sources. * Differences in underlying records: is trade measured from National Accounts data rather than directly from custom or tax records? * Differences in import and export valuations: are transactions valued at FOB or CIF prices? * Inconsistent attribution of trade partners: how is the origin and final destination of merchandise established? * Difference between 'goods' and 'merchandise': how are re-importing, re-exporting, and intermediary merchanting transactions recorded? * Exchange rates: how are values converted from local currency units to the currency that allows international comparisons (most often the US-$)? * Differences between 'general' and 'special' trade system: how is trade recorded for custom-free zones? * Other issues: Time of recording, confidentiality policies, product classification, deliberate misinvoicing for illicit purposes. These factors have long been recognized by many organizations producing trade data. Indeed, international organizations often incorporate corrections, in an attempt to improve data quality along these lines. The OECD's [Balanced International Merchandise Trade Statistics](https://stats.oecd.org/Index.aspx?DataSetCode=BIMTS_CPA), for example, uses its own approach to correct and reconcile international merchandise trade statistics.40 The corrections applied in the OECD's 'balanced' series make this the best source for cross-country comparisons. However, this dataset has low coverage across countries, and it only goes back to 2011. This is an important obstacle, since the complex adjustments introduced by the OECD imply we can't easily improve coverage by appending data from other sources. At Our World in Data we have chosen to rely on CEPII as the main source for exploring long-run changes in international trade; but we also rely on World Bank and OECD data for up-to-date cross-country comparisons. There are two key lessons from all of this. The first lesson is that, for most users of trade data out there, there is no obvious way of choosing between sources. And the second lesson is that, because of statistical glitches, researchers and policymakers should always take analysis of trade data with a pinch of salt. For example, in a recent [high-profile report](http://www.gfintegrity.org/report/illicit-financial-flows-from-developing-countries-2004-2013/), researchers attributed mismatches in bilateral trade data to illicit financial flows through trade misinvoicing (or trade-based money laundering). As we show here, this interpretation of the data is not appropriate, since mismatches in the data can, and often do arise from measurement inconsistencies rather than malfeasance.41 Hopefully the discussion and checklist above can help researchers better interpret and choose between conflicting data sources. --- # Data Sources --- ### International Historical Statistics (by Brian Mitchell) * **Data:** Aggregate trade (current value), bilateral trade with main trading partners (current value), and major commodity exports by main exporting countries. No data on trade as share of GDP is readily available. * **Geographical coverage:** Countries around the world * **Time span:** Long time series with annual observations – from 19th century up to today (2010) * **Available at:** The books are published in three volumes covering more than 5000 pages.42 At some universities you can access the online version of the books where data tables can be downloaded as ePDFs and Excel files. The online access is [here](https://www.palgrave.com/gp/search?query=International+Historical+Statistics). ### Penn World Tables * **Data:** Real and PPP-adjusted GDP in US millions of dollars, national accounts (household consumption, investment, government consumption, exports and imports), exchange rates and population figures. * **Geographical coverage:** Countries around the world * **Time span:** from 1950-2017 (version 9.1) * **Available at:** Online [here](http://www.rug.nl/research/ggdc/data/pwt/) * _Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), ""The Next Generation of the Penn World Table"" forthcoming American Economic Review, available for download at __[www.ggdc.net/pwt](http://www.ggdc.net/pwt)_ ### Correlates of War Bilateral Trade * **Data:** Total national trade and bilateral trade flows between states. Total imports and exports of each country in current US millions of dollars and bilateral flows in current US millions of dollars * **Geographical coverage:** Single countries around the world * **Time span:** from 1870-2009 * **Available at:** Online at [www.correlatesofwar.org](http://www.correlatesofwar.org) * _This data set is hosted by Katherine Barbieri, University of South Carolina, and Omar Keshk, Ohio State University. Authors note in their 'COW Trade Data Set Codebook': ""We advise against using the dyadic data file to produce any national or global totals, based on aggregations of the partner trade.""_ ### World Bank - World Development Indicators * **Data:** Trade (% of GDP) and many more specific series: trade in merchandise, trade in services, trade in high-technology, trade in ICT goods, trade in ICT services – always exports and imports separately. Also export and import value index and volume index. * **Geographical coverage:** Countries and world regions * **Time span:** Annual since 1960 * **Available at:** Online at [http://data.worldbank.org](http://data.worldbank.org) ### UN Comtrade * **Data:** Bilateral trade flows by commodity * **Geographical coverage:** Countries around the world * **Time span:** 1962-2013 * **Available at:** Online [here](http://comtrade.un.org/db/default.aspx) * _Bilateral trade flows can be sorted by goods or services, monthly or annually, with choice of classification (including HS codes, SITC, and BEC). Data is likely to be very time consuming to collate as there is no bulk data download unless a user has a premium site license._ ### UNCTADstat * **Data:** Many different measures, including trade by volumes and value * **Geographical coverage:** Countries around the world * **Time span:** For some series, data is available since 1948 - mostly annual, sometimes quarterly. * **Available at:** Online [here](http://unctadstat.unctad.org/wds/ReportFolders/reportFolders.aspx) * _ UNCTADstat reports export and import data between 1995 and 2016 but primarily to different regional groupings than any one country, so it's probably not best suited to comparing country-to-country bilateral flows._ ### Eurostat - COMEXT * **Data:** Trade flows (also by commodity) * **Geographical coverage:** Europe (EU and EFTA) * **Time span:** Mostly since 1988 * **Available at:** Online [here](http://epp.eurostat.ec.europa.eu/newxtweb/mainxtnet.do) * _Also, the Eurostat website 'Statistics Explained' publishes up-to-date statistical information on international trade in __[goods](http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/International_trade_in_goods)__ and __[services](http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/International_trade_in_services)__. _ ### World Trade Organization - WTO * **Data:** Many series on tariffs and trade flows * **Geographical coverage:** Countries around the world * **Time span:** Since 1948 for some series * **Available at:** Online [here](http://www.wto.org/english/res_e/statis_e/statis_e.htm) * _The WTO offers a bulk download of trade datasets which can be found __[here](https://www.wto.org/english/res_e/statis_e/trade_datasets_e.htm)__. Amongst these are annual WTO merchandise trade values and WTO-UNCTAD-ITC annual trade in services datasets. The former is available from 1948 - 2017, workable, with very little additional formatting needed. However, observations are country groups, such as the EU28, the BRICS etc. rather than country-by-country values. Otherwise, the __[WTO's Statistics Database (SDB)](https://data.wto.org/en)__ has extensive time series on international trade, by country with their trading partners. Again, trading partners are primarily restricted to country groupings rather than individual nations._ ### Fouquin and Hugot (CEPII 2016) - TRADHIST dataset * **Data:** Many different data sets related to international trade, including trade flows by commodity geographical variables, and variables to estimate gravity models * **Geographical coverage:** Countries around the world * **Available at:** Online [here](http://www.cepii.fr/CEPII/en/bdd_modele/bdd.asp) * _ TRADHIST __[Bilateral Trade Historical Series: New Dataset 1827-2014](http://www.cepii.fr/cepii/en/bdd_modele/presentation.asp?id=32)__ provides extensive dyadic trade data, with 97 percent of the observations from 1948 to today drawing on the IMF's Direction of Trade Statistics (DOTS) dataset._ ### NBER-United Nations Trade Data, 1962-2000 * **Data:** Export and import values and volumes by commodity * **Geographical coverage:** Single countries * **Time span:** 1962-2000 * **Available at:** Online [here](http://cid.econ.ucdavis.edu/wixd.html) * _ This data is also available from the __[Center for International Data](http://cid.econ.ucdavis.edu/)__. Bilateral trade data value estimates are very close to that of the World Bank's imports of goods and services time series. _ ### Federico-Tena World Trade Historical Database * **Data:** This website contains annual series of trade by polity from 1800 to 1938 which sum as series for continent and world. * **Geographical coverage:** Countries around the world * **Time span:** 1800-1938 * **Available at:** Federico, G., Tena Junguito, A. (2016). World trade, 1800-1938: a new data-set. EHES Working Papers in Economic History, n. 93. Online [here](https://www.uc3m.es/ss/Satellite/UC3MInstitucional/es/TextoMixta/1371246237481/Federico-Tena_World_Trade_Historical_Database) ### Other historical trade data sets * Data on **UK bilateral trade** for the time 1870-1913 was collected by David S. Jacks. It is downloadable in excel format [here](http://www.sfu.ca/~djacks/data/publications/UK%20trade,%2017%20countries,%201870-1913.xls). * For the time **1870-1913** 21,000 bilateral trade observations can be found in Mitchener and Weidenmier (2008) – Trade and empire, available in the [Economic Journal here](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C44&q=Kris+James+Mitchener+Marc+Weidenmier+2008+Trade+and+Empire&btnG=). * Data on **UK, Germany, France, and US** between mid-19th to 20th Century can be found [here](http://www.nber.org/databases/macrohistory/contents/chapter07.html). * Data on **Developing Country Export** - in 1840, 1860, 1880 and 1900 - by John Hanson is available [here](http://eh.net/databases). * Data on **trade between England and Africa** during the period 1699-1808 is available on the [Dutch Data Archiving and Networked Services](https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:39576). It was compiled by Marion Johnson. For more details on this see Forstater, M. (2018) Illicit Financial Flows, Trade Misinvoicing, and Multinational Tax Avoidance: The Same or Different?, CGD Policy Paper 123, available online at: https://www.cgdev.org/publication/illicit-financial-flows-trade-misinvoicing-and-multinational-tax-avoidance It's important to mention here that the economist Jonathan Rothwell recently wrote a [paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2920188) suggesting these findings are the result of a statistical illusion. Rothwell's critique received some [attention from the media](https://www.wsj.com/articles/the-truth-about-the-china-trade-shock-1491168339), but Autor and coauthors provided a [reply](http://economics.mit.edu/files/12729), which I think successfully refutes this claim. Magyari, I. (2017). Firm Reorganization, Chinese Imports, and US Manufacturing Employment. US Census Bureau, Center for Economic Studies. Available online [here](http://www.columbia.edu/~im2348/JMP_Magyari.pdf). Topalova, P. (2010). Factor immobility and regional impacts of trade liberalization: Evidence on poverty from India. American Economic Journal: Applied Economics, 2(4), 1-41. Available online [here](http://dl.kli.re.kr/dl_image/IMG/03/000000012162/SERVICE/000000012162_01.PDF). Manova, Kalina. ""Credit constraints, heterogeneous firms, and international trade."" The Review of Economic Studies 80.2 (2013): 711-744. Alcalá, F., & Ciccone, A. (2004). Trade and productivity. The Quarterly Journal of Economics, 119(2), 613-646. Online [here](https://www.jstor.org/stable/pdf/25098695.pdf). Broadberry and O'Rourke (2010) - The Cambridge Economic History of Modern Europe: Volume 2, 1870 to the Present. Cambridge University Press. The graph depicts the 'evolution of three indicators measuring integration in commodity, labor, and capital markets over the long run. Commodity market integration is measured by computing the ratio of exports to GDP. Labor market integration is measured by dividing the migratory turnover by population. Financial integration is measured using Feldstein–Horioka estimators of current account disconnectedness.' This data is taken from: Bayoumi 1990; Flandreau and Rivière 1999; Bordo and Flandreau 2003; Obstfeld and Taylor 2003. The textbook [The Economy: Economics for a Changing World](https://core-econ.org/the-economy/book/text/18.html) explains this in more detail here: [https://core-econ.org/the-economy/book/text/18.html#1810-trade-and-growth](https://core-econ.org/the-economy/book/text/18.html#1810-trade-and-growth) Porto, G (2006). Using Survey Data to Assess the Distributional Effects of Trade Policy. Journal of International Economics 70 (2006) 140–160. Precisely because of the difficulty that arises when trying to establish the origin and final destination of merchandise, some sources distinguish between national and dyadic (i.e. 'directed') trade estimates. For more details about general and special trade see: [http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:General_and_special_trade_systems](http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:General_and_special_trade_systems) Atkin, David, Benjamin Faber, and Marco Gonzalez-Navarro. ""Retail globalization and household welfare: Evidence from mexico."" Journal of Political Economy 126.1 (2018): 1-73. Leonor Freire Costa, Nuno Palma, and Jaime Reis (2015) – The great escape? The contribution of the empire to Portugal's economic growth, 1500–1800 Leonor Freire Costa Nuno Palma Jaime Reis European Review of Economic History, Volume 19, Issue 1, 1 February 2015, Pages 1–22, https://doi.org/10.1093/ereh/heu019 The chart includes series labeled by the sources as 'merchandise trade' and 'goods trade'. As we explain below, part of the asymmetries in trade data come from the fact that, although 'merchandise' and 'goods' are equivalent in the dictionary, these two terms often measure related but different things. Eaton, J., & Kortum, S. (2002). Technology, geography, and trade. Econometrica, 70(5), 1741-1779. Pavcnik, N. (2002). Trade liberalization, exit, and productivity improvements: Evidence from Chilean plants. The Review of Economic Studies, 69(1), 245-276. Online [here](https://www.jstor.org/stable/pdf/2695960.pdf). The NBER-UN trade data and documentation is available at [http://cid.econ.ucdavis.edu/data/undata/undata.html](http://cid.econ.ucdavis.edu/data/undata/undata.html) There are many papers that try to answer this specific question with macro data. For an overview of papers and methods see: Durlauf, S. N., Johnson, P. A., & Temple, J. R. (2005). Growth econometrics. Handbook of economic growth, 1, 555-677. Online [here](https://digitalwindow.vassar.edu/cgi/viewcontent.cgi?article=1037&context=faculty_research_reports). Frankel, J. A., & Romer, D. H. (1999). Does trade cause growth?. American economic review, 89(3), 379-399. Online [here](https://www.jstor.org/stable/pdf/117025.pd). Broadberry and O'Rourke (2010) - The Cambridge Economic History of Modern Europe: Volume 2, 1870 to the Present. Cambridge University Press. Evenett, S. J., & Keller, W. (2002). On theories explaining the success of the gravity equation. Journal of political economy, 110(2), 281-316. Available online [here](https://www.alexandria.unisg.ch/22207/1/evenettkeller.pdf). Trefler, D. (2004). The long and short of the Canada-US free trade agreement. American Economic Review, 94(4), 870-895. Available online [here](https://www.aeaweb.org/articles?id=10.1257/0002828042002633). _Nobel laureate Paul Samuelson (1969) was once challenged by the mathematician Stanislaw Ulam: ""Name me one proposition in all of the social sciences which is both true and non-trivial."" It was several years later than he thought of the correct response: comparative advantage. ""That it is logically true need not be argued before a mathematician; that is is not trivial is attested by the thousands of important and intelligent men who have never been able to grasp the doctrine for themselves or to believe it after it was explained to them.""_ (NB. This is an excerpt from https://www.wto.org/english/res_e/reser_e/cadv_e.htm) Crozet, M., & Koenig, P. (2010). Structural Gravity Equations with Intensive and Extensive Margins. The Canadian Journal of Economics / Revue Canadienne D'Economique, 43(1), 41-62. Retrieved from http://www.jstor.org/stable/40389555 The openness index, when calculated for the world as a whole, includes double-counting of transactions: When country A sells goods to country B, this shows up in the data both as an import (B imports from A) and as an export (A sells to B). Indeed, if you compare the chart showing the [global trade openness index](https://ourworldindata.org/grapher/globalization-over-5-centuries-km) and the chart showing [global merchandise exports as share of GDP](https://ourworldindata.org/grapher/merchandise-exports-gdp-cepii?country=OWID_WRL), you find that the former is almost twice as large as the latter. Why is the global openness index not exactly twice the value reported in the chart plotting global merchandise exports? There a three reasons. First, the global openness index uses different sources. Second, the global openness index includes trade in goods and services, while merchandise exports include goods but not services. And third, the amount that country A reports exporting to country B does not usually match the amount that B reports importing from A. We explore this in more detail in our blog post [Trade data: why doesn't it add up?](https://ourworldindata.org/trade-data-sources-discrepancies) See: (i) Feenstra, R. C., & Weinstein, D. E. (2017). Globalization, markups, and US welfare. Journal of Political Economy, 125(4), 1040-1074. (ii) Fajgelbaum, P. D., & Khandelwal, A. K. (2016). Measuring the unequal gains from trade. The Quarterly Journal of Economics, 131(3), 1113-1180. For example, if there is no change in ownership (e.g. a firm exports goods to it's factory in another country for processing, and then re-imports the processed goods) the manual says that statistical agencies should only record the net difference in value. You can find more details about this in [this](http://www.oecd.org/sdd/na/new-standards-for-compiling-national-accounts-SNA2008-OECDSB20.pdf) OECD Statistics Briefing. There are different ways of capturing this correlation. I focus here on all countries with data over the period 1945-2014. You can find a similar chart using different data sources and time periods in Ventura, J. (2005). A global view of economic growth. Handbook of economic growth, 1, 1419-1497. Online [here](https://repositori.upf.edu/bitstream/handle/10230/1248/849.pdf?sequence=1). This issue is actually also a source of disagreement between National Accounts data and customs data. You can read more about it in this report: [Harrison, Anne (2013) FOB/CIF Issue in Merchandise Trade/Transport of Goods in BPM6 and the 2008 SNA, Twenty-Fifth Meeting of the IMF Committee on Balance of Payments Statistics, Washington, D.C](https://web.archive.org/web/20221115005121/https://www.imf.org/external/pubs/ft/bop/2012/12-30.pdf). Berlingieri, G., Breinlich, H., & Dhingra, S. (2018). The Impact of Trade Agreements on Consumer Welfare—Evidence from the EU Common External Trade Policy. Journal of the European Economic Association. Donaldson, D. (2018). Railroads of the Raj: Estimating the impact of transportation infrastructure. American Economic Review, 108(4-5), 899-934. Available online [here](http://eprints.lse.ac.uk/38368/1/ARCWP41-Donaldson.pdf). In the paper, Atkin and coauthors explore the reasons for this, and find that the regressive nature of the distribution is mainly due to richer households placing higher weight on the product variety and shopping amenities on offer at these new foreign stores. Further information on CEPII's methodology can be found at [http://www.cepii.fr/PDF_PUB/wp/2016/wp2016-14.pdf](http://www.cepii.fr/PDF_PUB/wp/2016/wp2016-14.pdf) This chart was inspired by a chart from Helpman, E., Melitz, M., & Rubinstein, Y. (2007). Estimating trade flows: Trading partners and trading volumes (No. w12927). National Bureau of Economic Research. Melitz, J. (2008). Language and foreign trade. European Economic Review, 52(4), 667-699. In the 'Sources' tab in the chart you find a full explanation of how we constructed all series, as well as links to the original raw data. The printed version is published in 3 volumes: Africa, Asia, Oceania – The Americas – Europe. The volume set is described at the publisher's website [here](https://www.palgrave.com/gp/search?query=International+Historical+Statistics). David, H., Dorn, D., & Hanson, G. H. (2013). The China syndrome: Local labor market effects of import competition in the United States. American Economic Review, 103(6), 2121-68. Available online here: [http://economics.mit.edu/files/7723](http://economics.mit.edu/files/7723) Bloom, N., Draca, M., & Van Reenen, J. (2016). Trade induced technical change? The impact of Chinese imports on innovation, IT and productivity. The Review of Economic Studies, 83(1), 87-117. Available online [here](https://web.archive.org/web/20170810215533/http://bfi.uchicago.edu/sites/default/files/research/Van%20Reenen_Trade%20Induced%20Technical%20Change.pdf). Bernhofen, D., & Brown, J. (2004). A Direct Test of the Theory of Comparative Advantage: The Case of Japan. Journal of Political Economy, 112(1), 48-67. doi:1. Retrieved from http://www.jstor.org/stable/10.1086/379944 doi:1 For more information on how the COW trade datasets were constructed see: (i) Barbieri, Katherine and Omar M. G. Omar Keshk. 2016. Correlates of War Project Trade Data Set Codebook, Version 4.0. Available at [http://correlatesofwar.org](http://correlatesofwar.org) and (ii) Barbieri, Katherine, Omar M. G. Keshk, and Brian Pollins. 2009. “TRADING DATA: Evaluating our Assumptions and Coding Rules.” Conflict Management and Peace Science, 26(5): 471–491. Available at: [https://www.researchgate.net/publication/49518195_Trading_Data_Evaluating_Our_Assumptions_and_Coding_Rules](https://www.researchgate.net/publication/49518195_Trading_Data_Evaluating_Our_Assumptions_and_Coding_Rules) The OECD approach consists of four steps, which they describe as follows: ""First, data are collected and organized, and imports are converted to FOB prices to match the valuation of exports. Secondly, data are adjusted for several specific large problems known to drive asymmetries. Presently these include “modular” adjustments for unallocated and confidential trade; for exports by Hong Kong, China; for Swiss non-monetary gold; and for clear-cut cases of product misclassifications. The list of modules is expected to grow over time. In the third step, adjusted data are balanced using a “Symmetry Index” that weights exports and imports. As the final step, the data are also converted to Classification of Products by Activity (CPA) products to better align with National Accounts statistics, such as in national Supply-Use tables."" You can read more about it here: [http://www.oecd.org/sdd/its/statistical-insights-merchandise-trade-statistics-without-asymmetries.htm](http://www.oecd.org/sdd/its/statistical-insights-merchandise-trade-statistics-without-asymmetries.htm) In addition to the OECD, other sources also use corrections. The IMF's DOTS dataset, for example, uses a 6 percent rule for converting import valuations (in CIF) into export values (in FOB). More information can be found at [the IMF's (2018) working paper on 'New Estimates for Direction of Trade Statistics'.](https://web.archive.org/web/20220208150732/https://www.elibrary.imf.org/view/journals/001/2018/016/001.2018.issue-016-en.xml)",Trade and Globalization 1sVMvK6raf99WJVRc1Wn9zlriiiITkTQ1megmNeQ5AHU,we-won-the-lovie-award,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Our World in Data is a winner of this year's Lovie Awards. The Lovie Award is the European internet award and just like its American counterpart, the Webby Awards, it is awarded by the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""International Academy of Digital Arts and Sciences"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Their description of why they award the prize to us captures really well why we are doing this work:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""Our World in Data will receive the 2019 Lovie Be Greater with Data Award in recognition of their outstanding use of data and the internet to supply the general public with understandable data-driven research – the kind necessary to invoke social, economic, and environmental change. As an antidote to the cynicism that much of the population feels in today’s world–from the war on climate change to poverty and disease – Our World in Data focuses on long-lasting solutions to these issues, with data as its backbone.The birds-eye view that Our World in Data provides gives people everywhere access to digestible, useful information – shared through beautiful data visualisations alongside great storytelling and clear reporting – and delivers the kind of much-needed evidence that our world is actually changing for the better."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""blockquote"", ""citation"": """", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://www.lovieawards.com/features/2019-winners-announced/"", ""children"": [{""text"": ""Here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" is the description of all winners of the 2019 Lovie Awards."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A huge honor to all of us! Thank you for your support to make this work possible."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""article"", ""title"": ""We won the Lovie Award!"", ""authors"": [""Max Roser""], ""excerpt"": ""The Lovie Award is the European internet award awarded by the International Academy of Digital Arts and Sciences."", ""dateline"": ""October 16, 2019"", ""subtitle"": """", ""featured-image"": ""Lovie_Logo.jpg""}",1,2023-12-02 02:39:23,2019-10-16 16:08:12,2024-01-30 12:57:57,listed,ALBJ4LtCUSTmzq1_Yl1zwye2w0K4ohmes9oScjy3SLJUAZ6b6n83ZgQcnx3HOLj0FCZWQTNp6H6vjhJWG0JdyA,,"Our World in Data is a winner of this year's Lovie Awards. The Lovie Award is the European internet award and just like its American counterpart, the Webby Awards, it is awarded by the _International Academy of Digital Arts and Sciences_. Their description of why they award the prize to us captures really well why we are doing this work: -- [Here](https://www.lovieawards.com/features/2019-winners-announced/) is the description of all winners of the 2019 Lovie Awards. A huge honor to all of us! Thank you for your support to make this work possible.",We won the Lovie Award! 1sTqavZ-oAtzW_p0O8U_JWb_lRCorV3gU-E570EsC9lc,africa-yields-problem,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""To grow food you need two things: some land and some of your time. These two – land and labor – are two of agriculture’s ‘inputs’. To build a food system that works for people and the planet, humanity needs to achieve high productivity in both of them."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To escape poverty, farmers need to increase "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""labor productivity "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""– to produce more food per hour worked. It is a deep societal problem when most of the population works in farming and gets little money in return. The farmers' families are unable to get a good education; improve healthcare; and to free up labor so that their children can become teachers or build new industries outside of agriculture."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To protect the world’s wildlife, we need high "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""land productivity "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""– to produce more food per unit of land area. Land productivity for crops is measured as ‘"", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/crop-yields"", ""children"": [{""text"": ""crop yields"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""’. If humanity wants to reduce deforestation and protect habitats rich in biodiversity then we need to use less land to grow food."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Across much of Sub-Saharan Africa, the productivity of both input factors is low. Agricultural productivity across the region needs to improve to reduce hunger, poverty, and the destruction of biodiversity. This is why I think that it is one of the most important problems to tackle this century."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Labor productivity is low across much of Sub-Saharan Africa"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Rates of extreme poverty across Sub-Saharan Africa are still very high. While it has made progress in recent decades, 40% of the population "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-population-in-extreme-poverty?tab=chart&country=~Sub-Saharan+Africa"", ""children"": [{""text"": ""still live below"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" the international poverty line of 1.90 international-dollars per day. This is a very low poverty line, used to identify those in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/extreme-poverty-in-brief"", ""children"": [{""text"": ""the deepest poverty"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Much of this is explained by the fact that "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-the-labor-force-employed-in-agriculture?tab=chart&time=1300..latest&country=~Sub-Saharan+Africa"", ""children"": [{""text"": ""more than half"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of the labor force work in agriculture, and labor productivity in the sector is poor. Most of the region’s poorest people are farmers: the majority (82%) of those in extreme poverty live in rural areas, and more than three-quarters (76%) of working adults in extreme poverty are employed in agriculture."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These farmers are both producers and consumers. Poverty means that there are not only barriers in food supply, but also in the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""demand"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" for agricultural products. Farmers need access to local markets where others can afford to buy from them. If that market does not exist, or farmers lack road infrastructure to get there – as has been the case in many African countries – there is less of an incentive for productivity to improve."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is also due to rich countries’ policies towards Africa. Trade policies in other regions have made this even more challenging for African farmers. The EU’s agricultural policies, in particular, have received criticism for their impact on global markets."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It developed strong trade agreements between EU countries, and at the same time limited export markets for other regions, including Africa."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see just how low the productivity is in the chart which compares the amount of agricultural ‘value added’ per person working in agriculture."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The amount of ‘value added’ per worker in Sub-Saharan Africa is less than half the global average, and more than 50-times lower than in the countries in which farmers are most productive."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Some countries within Sub-Saharan Africa generate as little as half of this regional average.This makes it impossible for families to escape poverty. Most are "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/farm-size"", ""children"": [{""text"": ""smallholder farms"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" They need family members to work and contribute. They also often cannot afford to invest in education or other opportunities that might allow them to move into industries with higher wages. Without increasing labor productivity, most of the population will have to continue working in agriculture."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/agriculture-value-added-per-worker-wdi?tab=chart&time=latest&country=OWID_WRL~CHN~USA~GBR~East+Asia+%26+Pacific~Sub-Saharan+Africa~Latin+America+%26+Caribbean~TZA~COG~DNK"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Land productivity: Crop yields in Sub-Saharan Africa are very low relative to other regions"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The other – strongly-related – problem is that most countries across the region have very low crop yields. We see this in the charts, which compare cereal yields across the world. The average across Africa is half that of India and one-fifth of the yields in the US."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Growing food in this way means using a lot of land; land that would otherwise be habitat for wildlife. Africa’s yields have lagged behind most of the world as the time-series chart shows. Most countries have achieved a significant rise since 1961. But across much of Africa, yields have stagnated."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" As a consequence the global inequality in yields has increased."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""left"": [{""url"": ""https://ourworldindata.org/explorers/global-food?tab=chart&time=latest&facet=none&hideControls=true&Food=Cereals&Metric=Yield&Per+Capita=false&country=OWID_WRL~IND~CHN~Africa~USA~GBR~BRA~TCD~NER~BEL~IRL~NLD"", ""type"": ""chart"", ""parseErrors"": []}], ""type"": ""side-by-side"", ""right"": [{""url"": ""https://ourworldindata.org/explorers/global-food?tab=chart&time=1961..latest&facet=none&hideControls=true&Food=Cereals&Metric=Yield&Per+Capita=false&country=OWID_WRL~IND~CHN~Africa~USA~GBR~BRA~TCD~NER~BEL~IRL~NLD"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Crop yields in Sub-Saharan Africa have lagged behind, at the cost of natural habitat"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There is an environmental cost to these low yields."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is because there are two ways to increase food production: one can either increase yields or one has to use more land."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Improvements in yields in other regions meant that they could grow much more food without taking over natural ecosystems. Because Africa did not increase yields, increased food production came at the cost of turning natural habitats into agricultural land."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see this if we compare what has happened to agriculture in South Asia versus Sub-Saharan Africa since 1980."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Both charts consist of two rectangles. The inner rectangle shows cereal production in 1980. The outer rectangle shows cereal production in 2018."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""height"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of each rectangle represents the cereal yield – how much was produced per hectare of land. The taller the rectangle, the higher the yield."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""width"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of the rectangle represents the amount of land used for cereal production – the wider the rectangle, the more land is used for crops."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As a consequence, the total area of the rectangle represents the total cereal production: it is the number of hectares used to grow cereal, multiplied by how much cereal is produced on each hectare."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Both regions produced a lot more cereal in 2018 than they did three decades earlier. In South Asia it increased by 133%; in Sub-Saharan Africa, it tripled."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Cereal-yields-vs-land-use-–-Marimekko.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But, "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""how "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""the regions achieved this increase was very different. In South Asia, "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""all"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of this growth came from higher yields: The rectangle got much taller, but did not get any wider. Land use did not change at all."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Almost the opposite is true in Sub-Saharan Africa. Yield improvements have been small. They increased just 30%. Nearly all of this growth in food production came from using more and more land. Land use more than doubled from 48 to 112 million hectares."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Both regions have increased food production a lot, but only in Sub-Saharan Africa did this come at the cost of the loss of natural habitat."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""We know that it is possible to increase agricultural productivity"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If Africa does not improve its agricultural productivity, what would happen?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Progress on poverty will be slow – much of its population will continue working in agriculture, and will earn very little in return."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Africa will also need to grow a lot more food. First, because "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/hunger-and-undernourishment"", ""children"": [{""text"": ""undernourishment rates"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" are high. That food gap needs to be filled to end hunger. Second, its "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/future-population-growth"", ""children"": [{""text"": ""population will grow a lot"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" over the coming decades. If yields do not increase, the continent will need to use more and more of its land for agriculture."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Studies have shown that if progress on crop yields does not improve, the continent "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/yields-habitat-loss"", ""children"": [{""text"": ""will lose large amounts"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of its natural habitat to farmland."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In many countries across Sub-Saharan Africa, researchers "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/habitat-loss?facet=none&hideControls=true&Projected+change+by+2050=Cropland+area&Species+Group=All+vertebrates&Scenario=Business-as-usual&country=OWID_WRL~Sub-Saharan+Africa~South+and+East+Asia~Europe~North+America~Latin+America~North+Africa~Oceania"", ""children"": [{""text"": ""estimate that cropland area"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" could almost triple by 2050. This will come at the cost of wildlife: in these "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/habitat-loss?facet=none&hideControls=true&Projected+change+by+2050=Species+habitat+loss&Species+Group=All+vertebrates&Scenario=Business-as-usual&country=OWID_WRL~Sub-Saharan+Africa~South+and+East+Asia~Europe~North+America~Latin+America~North+Africa~Oceania"", ""children"": [{""text"": ""same projections"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", 10% to 20% of animal habitats will be lost."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But, it doesn’t have to be that way. Things "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""can"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" change."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All countries were once in the position that many African countries are in today. Take England, France or Italy as examples. Until two centuries ago "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-the-labor-force-employed-in-agriculture?tab=chart&time=1300..latest&country=ITA~FRA~POL~England~NLD~GBR"", ""children"": [{""text"": ""more than half"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of the labor force worked in agriculture – similar to the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-the-labor-force-employed-in-agriculture?time=latest"", ""children"": [{""text"": ""African average today"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". During that period, agricultural output per worker was very low, and therefore "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/extreme-poverty-in-brief"", ""children"": [{""text"": ""most lived in extreme poverty"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""That has changed dramatically: less than "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-the-labor-force-employed-in-agriculture"", ""children"": [{""text"": ""four percent"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" now work in agriculture, and the amount generated per worker "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/agriculture-value-added-per-worker-wdi?tab=chart&time=latest&country=GBR~DNK~FRA~ITA~Sub-Saharan+Africa"", ""children"": [{""text"": ""is much higher"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – at least 30 times higher – than across Africa today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This was the result of a significant improvement in productivity. As we see in the chart here, the UK was also struggling with persistently low crop yields for most of its agricultural history. Average cereal yields were one to two tonnes per hectare – very similar to what many African countries currently achieve. Since then, yields have quadrupled as a result of improved seed varieties, fertilizers, and access to other inputs such as machinery and irrigation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see this "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/wheat-yields?tab=chart"", ""children"": [{""text"": ""same trend"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" across Europe."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are more recent success stories across other regions. Rapid improvements "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/agriculture-value-added-per-worker-wdi?tab=chart&time=1991..latest&country=~CHN"", ""children"": [{""text"": ""across China"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The same "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/agriculture-value-added-per-worker-wdi?tab=chart&time=1991..latest&country=~BRA"", ""children"": [{""text"": ""in Brazil"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". And also massive improvements in some countries in Sub-Saharan Africa itself. In South Africa and Nigeria, for example, the agricultural value-added per worker "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/agriculture-value-added-per-worker-wdi?tab=chart&time=1991..latest&country=ZAF~NGA"", ""children"": [{""text"": ""has roughly tripled"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" over the last few decades."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Crop yields might seem like an odd choice to pick as one of the world’s most pressing problems. But, if yields and labor productivity do not increase it will have far-reaching consequences for global poverty, and protection of the environment. For people and planet, it’s one of our most important problems to work on."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/cereal-yields-uk"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""I would like to thank Max Roser for valuable suggestions and feedback on this article."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Acknowledgements"", ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""Keep reading at Our World in Data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/yields-habitat-loss"", ""type"": ""prominent-link"", ""title"": ""To protect the world’s wildlife we must improve crop yields — especially across Africa"", ""description"": """", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/poverty-growth-needed"", ""type"": ""prominent-link"", ""title"": ""The economies that are home to the poorest billions of people need to grow if we want global poverty to decline substantially"", ""description"": """", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/explorers/crop-yields"", ""type"": ""prominent-link"", ""title"": ""Crop Yields Data Explorer"", ""description"": """", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""8aab4758b2f3453579ad086a457219e9c2cf1936"": {""id"": ""8aab4758b2f3453579ad086a457219e9c2cf1936"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Hollinger and Staatz (2015). 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These two – land and labor – are two of agriculture’s ‘inputs’. To build a food system that works for people and the planet, humanity needs to achieve high productivity in both of them. To escape poverty, farmers need to increase **labor productivity **– to produce more food per hour worked. It is a deep societal problem when most of the population works in farming and gets little money in return. The farmers' families are unable to get a good education; improve healthcare; and to free up labor so that their children can become teachers or build new industries outside of agriculture. To protect the world’s wildlife, we need high **land productivity **– to produce more food per unit of land area. Land productivity for crops is measured as ‘[crop yields](http://ourworldindata.org/crop-yields)’. If humanity wants to reduce deforestation and protect habitats rich in biodiversity then we need to use less land to grow food. Across much of Sub-Saharan Africa, the productivity of both input factors is low. Agricultural productivity across the region needs to improve to reduce hunger, poverty, and the destruction of biodiversity. This is why I think that it is one of the most important problems to tackle this century.1 # Labor productivity is low across much of Sub-Saharan Africa Rates of extreme poverty across Sub-Saharan Africa are still very high. While it has made progress in recent decades, 40% of the population [still live below](https://ourworldindata.org/grapher/share-of-population-in-extreme-poverty?tab=chart&country=~Sub-Saharan+Africa) the international poverty line of 1.90 international-dollars per day. This is a very low poverty line, used to identify those in [the deepest poverty](https://ourworldindata.org/extreme-poverty-in-brief). Much of this is explained by the fact that [more than half](https://ourworldindata.org/grapher/share-of-the-labor-force-employed-in-agriculture?tab=chart&time=1300..latest&country=~Sub-Saharan+Africa) of the labor force work in agriculture, and labor productivity in the sector is poor. Most of the region’s poorest people are farmers: the majority (82%) of those in extreme poverty live in rural areas, and more than three-quarters (76%) of working adults in extreme poverty are employed in agriculture.2 These farmers are both producers and consumers. Poverty means that there are not only barriers in food supply, but also in the _demand_ for agricultural products. Farmers need access to local markets where others can afford to buy from them. If that market does not exist, or farmers lack road infrastructure to get there – as has been the case in many African countries – there is less of an incentive for productivity to improve.3 This is also due to rich countries’ policies towards Africa. Trade policies in other regions have made this even more challenging for African farmers. The EU’s agricultural policies, in particular, have received criticism for their impact on global markets.4 It developed strong trade agreements between EU countries, and at the same time limited export markets for other regions, including Africa.5 We see just how low the productivity is in the chart which compares the amount of agricultural ‘value added’ per person working in agriculture. The amount of ‘value added’ per worker in Sub-Saharan Africa is less than half the global average, and more than 50-times lower than in the countries in which farmers are most productive. Some countries within Sub-Saharan Africa generate as little as half of this regional average.This makes it impossible for families to escape poverty. Most are [smallholder farms](http://ourworldindata.org/farm-size).6 They need family members to work and contribute. They also often cannot afford to invest in education or other opportunities that might allow them to move into industries with higher wages. Without increasing labor productivity, most of the population will have to continue working in agriculture. # Land productivity: Crop yields in Sub-Saharan Africa are very low relative to other regions The other – strongly-related – problem is that most countries across the region have very low crop yields. We see this in the charts, which compare cereal yields across the world. The average across Africa is half that of India and one-fifth of the yields in the US. Growing food in this way means using a lot of land; land that would otherwise be habitat for wildlife. Africa’s yields have lagged behind most of the world as the time-series chart shows. Most countries have achieved a significant rise since 1961. But across much of Africa, yields have stagnated.7 As a consequence the global inequality in yields has increased. # Crop yields in Sub-Saharan Africa have lagged behind, at the cost of natural habitat There is an environmental cost to these low yields. This is because there are two ways to increase food production: one can either increase yields or one has to use more land. Improvements in yields in other regions meant that they could grow much more food without taking over natural ecosystems. Because Africa did not increase yields, increased food production came at the cost of turning natural habitats into agricultural land. We see this if we compare what has happened to agriculture in South Asia versus Sub-Saharan Africa since 1980.8 Both charts consist of two rectangles. The inner rectangle shows cereal production in 1980. The outer rectangle shows cereal production in 2018. * The _height_ of each rectangle represents the cereal yield – how much was produced per hectare of land. The taller the rectangle, the higher the yield. * The _width_ of the rectangle represents the amount of land used for cereal production – the wider the rectangle, the more land is used for crops. * As a consequence, the total area of the rectangle represents the total cereal production: it is the number of hectares used to grow cereal, multiplied by how much cereal is produced on each hectare. Both regions produced a lot more cereal in 2018 than they did three decades earlier. In South Asia it increased by 133%; in Sub-Saharan Africa, it tripled. But, _how _the regions achieved this increase was very different. In South Asia, _all_ of this growth came from higher yields: The rectangle got much taller, but did not get any wider. Land use did not change at all. Almost the opposite is true in Sub-Saharan Africa. Yield improvements have been small. They increased just 30%. Nearly all of this growth in food production came from using more and more land. Land use more than doubled from 48 to 112 million hectares. Both regions have increased food production a lot, but only in Sub-Saharan Africa did this come at the cost of the loss of natural habitat. # We know that it is possible to increase agricultural productivity If Africa does not improve its agricultural productivity, what would happen? Progress on poverty will be slow – much of its population will continue working in agriculture, and will earn very little in return. Africa will also need to grow a lot more food. First, because [undernourishment rates](http://ourworldindata.org/hunger-and-undernourishment) are high. That food gap needs to be filled to end hunger. Second, its [population will grow a lot](http://ourworldindata.org/future-population-growth) over the coming decades. If yields do not increase, the continent will need to use more and more of its land for agriculture. Studies have shown that if progress on crop yields does not improve, the continent [will lose large amounts](https://ourworldindata.org/yields-habitat-loss) of its natural habitat to farmland.9 In many countries across Sub-Saharan Africa, researchers [estimate that cropland area](https://ourworldindata.org/explorers/habitat-loss?facet=none&hideControls=true&Projected+change+by+2050=Cropland+area&Species+Group=All+vertebrates&Scenario=Business-as-usual&country=OWID_WRL~Sub-Saharan+Africa~South+and+East+Asia~Europe~North+America~Latin+America~North+Africa~Oceania) could almost triple by 2050. This will come at the cost of wildlife: in these [same projections](https://ourworldindata.org/explorers/habitat-loss?facet=none&hideControls=true&Projected+change+by+2050=Species+habitat+loss&Species+Group=All+vertebrates&Scenario=Business-as-usual&country=OWID_WRL~Sub-Saharan+Africa~South+and+East+Asia~Europe~North+America~Latin+America~North+Africa~Oceania), 10% to 20% of animal habitats will be lost. But, it doesn’t have to be that way. Things _can_ change. All countries were once in the position that many African countries are in today. Take England, France or Italy as examples. Until two centuries ago [more than half](https://ourworldindata.org/grapher/share-of-the-labor-force-employed-in-agriculture?tab=chart&time=1300..latest&country=ITA~FRA~POL~England~NLD~GBR) of the labor force worked in agriculture – similar to the [African average today](https://ourworldindata.org/grapher/share-of-the-labor-force-employed-in-agriculture?time=latest). During that period, agricultural output per worker was very low, and therefore [most lived in extreme poverty](https://ourworldindata.org/extreme-poverty-in-brief). That has changed dramatically: less than [four percent](https://ourworldindata.org/grapher/share-of-the-labor-force-employed-in-agriculture) now work in agriculture, and the amount generated per worker [is much higher](https://ourworldindata.org/grapher/agriculture-value-added-per-worker-wdi?tab=chart&time=latest&country=GBR~DNK~FRA~ITA~Sub-Saharan+Africa) – at least 30 times higher – than across Africa today. This was the result of a significant improvement in productivity. As we see in the chart here, the UK was also struggling with persistently low crop yields for most of its agricultural history. Average cereal yields were one to two tonnes per hectare – very similar to what many African countries currently achieve. Since then, yields have quadrupled as a result of improved seed varieties, fertilizers, and access to other inputs such as machinery and irrigation. We see this [same trend](https://ourworldindata.org/grapher/wheat-yields?tab=chart) across Europe. There are more recent success stories across other regions. Rapid improvements [across China](https://ourworldindata.org/grapher/agriculture-value-added-per-worker-wdi?tab=chart&time=1991..latest&country=~CHN). The same [in Brazil](https://ourworldindata.org/grapher/agriculture-value-added-per-worker-wdi?tab=chart&time=1991..latest&country=~BRA). And also massive improvements in some countries in Sub-Saharan Africa itself. In South Africa and Nigeria, for example, the agricultural value-added per worker [has roughly tripled](https://ourworldindata.org/grapher/agriculture-value-added-per-worker-wdi?tab=chart&time=1991..latest&country=ZAF~NGA) over the last few decades. Crop yields might seem like an odd choice to pick as one of the world’s most pressing problems. But, if yields and labor productivity do not increase it will have far-reaching consequences for global poverty, and protection of the environment. For people and planet, it’s one of our most important problems to work on. --- --- # Keep reading at Our World in Data ### To protect the world’s wildlife we must improve crop yields — especially across Africa https://ourworldindata.org/yields-habitat-loss ### The economies that are home to the poorest billions of people need to grow if we want global poverty to decline substantially https://ourworldindata.org/poverty-growth-needed ### Crop Yields Data Explorer https://ourworldindata.org/explorers/crop-yields Jayne, T. S., & Sanchez, P. A. (2021). [Agricultural productivity must improve in sub-Saharan Africa](https://www.science.org/doi/10.1126/science.abf5413). _Science_, 372(6546), 1045-1047. Castaneda, R., Doan, D., Newhouse, D. L., Nguyen, M., Uematsu, H., & Azevedo, J. P. (2016). [Who are the poor in the developing world?](https://documents1.worldbank.org/curated/en/187011475416542282/pdf/WPS7844.pdf). World Bank Policy Research Working Paper, (7844). Hollinger and Staatz (2015). [Agricultural growth in West Africa: Market and Policy Drivers](https://www.fao.org/publications/card/en/c/866496a3-f459-4fee-8c13-cd66897c97cf/). Food and Agriculture Organization of the United Nations. Bold, T., Ghisolfi, S., Nsonzi, F., & Svensson, J. (2022). [Market Access and Quality Upgrading: Evidence from Three Field Experiments](https://www.aeaweb.org/articles?id=10.1257/aer.20210122). American Economic Review. Luke, Mevel and Desta (2020). EU-Africa Trade Arrangements at a Crossroads Securing Africa’s External Frontier. Europe is also a large food exporter and, until 2017, would offer export subsidies to reduce the price for international buyers. This was part of the EU’s [Common Agricultural Policy](https://ec.europa.eu/info/food-farming-fisheries/key-policies/common-agricultural-policy/cap-glance_en), which included export subsidies until 2017. This flooded other markets – including many across Africa – with cheap European goods that local farmers could not compete with. For example, it’s [estimated that](https://www.un.org/africarenewal/magazine/june-2021/african-farmers-could-benefit-more-friendly-eu-agriculture-policies) over 90% of chicken meat in supermarkets in Ghana are frozen poultry imported from the EU or the United States. Lowder, S. K., Skoet, J., & Raney, T. (2016). [The number, size, and distribution of farms, smallholder farms, and family farms worldwide](https://www.sciencedirect.com/science/article/pii/S0305750X15002703). World Development, 87, 16-29. Tian, X., & Yu, X. (2019). [Crop yield gap and yield convergence in African countries](https://link.springer.com/article/10.1007/s12571-019-00972-5). _Food Security_, 11(6), 1305-1319. The data for this chart is sourced from the Food and Agriculture Organization of the United Nations (UN FAO). Available at: [https://www.fao.org/faostat/en/#data](https://www.fao.org/faostat/en/#data). In its [World Development Indicators](https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators), the World Bank aggregates this data from the UN FAO to produce regional figures for Sub-Saharan Africa and South Asia. This is where these aggregates are sourced from. Williams, D. R., Clark, M., Buchanan, G. M., Ficetola, G. F., Rondinini, C., & Tilman, D. (2021). [Proactive conservation to prevent habitat losses to agricultural expansion](https://ourworldindata.org/yields-habitat-loss). Nature Sustainability, 4(4), 314-322.",Increasing agricultural productivity across Sub-Saharan Africa is one of the most important problems this century 1sC5hYCG5Tbnt02QzaxHFeGhliSWbwUuyttXKP8TS9xc,democracy,topic-page,"{""toc"": [], ""body"": [{""type"": ""topic-page-intro"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Democracy is broadly understood to mean ‘rule by the people’."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In practice, it is often defined as people choosing their leaders in free and fair elections."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Other definitions go beyond this. 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You can explore them in our "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/democracy?country=ARG~AUS~BWA~CHN~OWID_WRL&Dataset=Varieties+of+Democracy&Metric=Electoral+democracy&Sub-metric=Main+index"", ""children"": [{""text"": ""Democracy Data Explorer"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}]}, {""url"": ""https://ourworldindata.org/grapher/distribution-electoral-democracy-vdem"", ""type"": ""key-insight-slide"", ""title"": ""People around the world have gained democratic rights, but some have many more rights than others"", ""content"": [{""type"": ""text"", ""value"": [{""text"": ""There are large differences in the degree to which citizens enjoy political rights — between democracies and non-democracies, but also within each group. 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Based on V-Dem, this includes countries such as China, North Korea, and Saudi Arabia. There, citizens do not have the right to choose their political leaders in popular elections."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most countries, often in Africa and Asia, fall somewhere in the middle. Political leaders are elected and citizens have the right to vote there, but their rights to associate and express their opinions are limited, and elections are not entirely free and fair."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart looks at electoral democratic institutions. 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The chart shows that more countries have been autocratizing recently, based on the Episodes of Regime Transformation (ERT) data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The number of countries that are autocratizing has been increasing: for 2023, ERT identifies 42 that were autocratizing — close to an all-time high."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For a long time, the number of autocratizing countries was offset by democratizing ones. 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But since then it has fallen, and now looks more like the 2000s, the 1990s, or even the late 1980s, depending on which democracy measure we rely on."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We have seen similar democratic declines before, and past declines were reversed. People fought previous phases of autocratization in the 1930s and 1960/70s, turned the tide, and pushed democratic rights to unprecedented heights. We can do it again."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We have an article that provides more detail:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/14ThFloGyQv4uSGdEeZROWq_2yvHC9ggnAFij_9Oyz6g/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""text"": [{""text"": [{""text"": ""What you should know about this data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 5, ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""For data on which countries are becoming less or more democratic, we rely on data from the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.v-dem.net/ertds.html"", ""children"": [{""text"": ""Episodes of Regime Transformation project"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We use their data to identify which countries are autocratizing, democratizing, and which countries are not clearly moving in either direction."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""ERT seeks to strike a balance between large and small changes in how democratic countries are."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It captures smaller changes in the level of democracy that fall short of regime change. At the same time, it only codes a country as autocratizing when there is a substantial decrease in its democracy score. This is because very small decreases may be fleeting and not indicate broader shifts towards less democracy, or overstate changes altogether because the measurement is uncertain."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" ERT also allows for temporary stagnation because autocratization may not happen abruptly in one year, but slowly over several years."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We use the ERT data here, but there are several other leading approaches to measuring democracy, which sometimes classify or score countries differently. 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You can learn more about the approaches — "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""and which democracy measure may be best to answer your questions"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" — in our article explaining how researchers measure democracy:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1vMsru_zjboUD_W5aBXoMKMMxCxqyJ4oYVCfchYBzDQM/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}], ""parseErrors"": []}, {""more"": {""heading"": ""More Articles on Democracy"", ""articles"": [{""value"": {""url"": ""https://docs.google.com/document/d/10mpw95MM1OZO__0g75j1rHhEp2pjBlDnUcY08uyvn_E/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1LNnYyow4VGpM-z8R80D3GbzGHfodTv0EEvBceJxvgR8/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/15TD9DyPmgOUFC8pMzMczLJmNTIw_j-gBM32JpInPJIU/edit""}}]}, ""rows"": [{""heading"": ""Measuring Democracy"", ""articles"": [{""value"": {""url"": ""https://docs.google.com/document/d/1vMsru_zjboUD_W5aBXoMKMMxCxqyJ4oYVCfchYBzDQM/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/14zTYMg-mkPcMC68DqgQNb1eAe8VBwoD93yzyKGIeguw/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/1xO9wHbwM5LKlGFbHyet3CLmiqnI3ZOHS8V3P_81Itf4/edit""}}]}], ""type"": ""research-and-writing"", ""primary"": [{""value"": {""url"": ""https://docs.google.com/document/d/1ghyH3pzHeRNxnDNZ78FXfIqea6ULupeS3KyCMRoH80A/edit""}}, {""value"": {""url"": ""https://docs.google.com/document/d/14ThFloGyQv4uSGdEeZROWq_2yvHC9ggnAFij_9Oyz6g/edit""}}], ""secondary"": [], ""parseErrors"": [], ""hide-authors"": false}, {""top"": [{""url"": ""https://ourworldindata.org/grapher/political-regime""}, {""url"": ""https://ourworldindata.org/grapher/electoral-democracy-index""}, {""url"": ""https://ourworldindata.org/grapher/countries-democracies-autocracies-row""}, {""url"": ""https://ourworldindata.org/grapher/people-living-in-democracies-autocracies""}, {""url"": ""https://ourworldindata.org/grapher/number-electoral-democracies-age""}, {""url"": ""https://ourworldindata.org/grapher/distribution-electoral-democracy-vdem""}, {""url"": ""https://ourworldindata.org/grapher/countries-that-are-democratizing-and-autocratizing""}], ""type"": ""all-charts"", ""heading"": ""Interactive Charts on Democracy"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""1578f952fd255b7a3533f50b9409542012d1828b"": {""id"": ""1578f952fd255b7a3533f50b9409542012d1828b"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Examples are France and New Zealand."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""165e952b37d41e8b0a53da46c6ef226343520c00"": {""id"": ""165e952b37d41e8b0a53da46c6ef226343520c00"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Seraphine Maerz, Amanda Edgell, Matthew Wilson, Sebastian Hellmeier, Staffan Lindberg. 2021. 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University of Gothenburg."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""1c42d72626376ad02369af56c83dcc4007b8e73d"": {""id"": ""1c42d72626376ad02369af56c83dcc4007b8e73d"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Based on ERT, a country is autocratizing from when V-Dem’s electoral democracy index decreases by 0.01, until the score increases or remains unchanged for four years, and the total decrease between the start and end amounts to a decrease of at least 0.10. Democratizing countries are classified analogously. We exclude the few country years for which democratization and autocratization episodes happen to overlap. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/political-regime-ert"", ""children"": [{""text"": ""This"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" chart shows how each country has been classified at the end of each year since 1900."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""212154fd02a1ee28c9af6470e7665601652ce845"": {""id"": ""212154fd02a1ee28c9af6470e7665601652ce845"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Lührmann, Anna, Marcus Tannenberg, and Staffan Lindberg. 2018. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=L%C3%BChrmann%2C+Anna%2C+Marcus+Tannenberg%2C+and+Staffan+Lindberg.+2018.+Regimes+of+the+World+%28RoW%29%3A+Opening+New+Avenues+for+the+Comparative+Study+of+Political+Regimes.+Politics+and+Governance+6%281%29%3A+60-77.&btnG="", ""children"": [{""text"": ""Regimes of the World (RoW): Opening New Avenues for the Comparative Study of Political Regimes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Politics and Governance 6(1): 60-77."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""55dcd156965410b8fc36d5615a86465f292e20e6"": {""id"": ""55dcd156965410b8fc36d5615a86465f292e20e6"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""RoW’s reclassification is the result of recent changes in the V-Dem data, which identify declines in the autonomy of the election management body, the freedom and fairness of elections, and especially the freedom of expression, the media, and civil society. You can read more in V-Dem’s 2021 annual report"", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20220130230849/https://v-dem.net/static/website/files/dr/dr_2021.pdf"", ""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""url"": ""https://web.archive.org/web/20220130230849/https://v-dem.net/static/website/files/dr/dr_2021.pdf"", ""children"": [{""text"": ""Autocratization Turns Viral"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""800b5493512591b50cd35bca2514294b0f72cdb7"": {""id"": ""800b5493512591b50cd35bca2514294b0f72cdb7"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Examples are Australia, Belgium, and Switzerland."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""8dbb6fb517e1739387574fb0bb87c6fe5596a678"": {""id"": ""8dbb6fb517e1739387574fb0bb87c6fe5596a678"", ""index"": 9, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This means, however, that some countries are not classified as autocratizing even though their score visibly declines. One example is the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/democracy?tab=chart&time=2003..latest&facet=none&country=~USA&Dataset=Varieties+of+Democracy&Metric=Electoral+democracy&Sub-metric=Main+index"", ""children"": [{""text"": ""United States in the 2010s"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", whose decline between 2015 and 2020 fell just barely short of the ERT threshold."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b37d3d7d8b0377d795796e7f8755c95d35a1226a"": {""id"": ""b37d3d7d8b0377d795796e7f8755c95d35a1226a"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The United Kingdom and the United States were the only countries that could be classified as electoral autocracies, because its political leaders were chosen through elections, but citizens lacked additional freedoms to make those elections free and fair."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c0577669997216ab339643ad3c63983c803ea409"": {""id"": ""c0577669997216ab339643ad3c63983c803ea409"", ""index"": 10, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Bermeo, Nancy. 2016. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Bermeo%2C+Nancy.+2016.+On+democratic+backsliding.+Journal+of+Democracy+27%281%29%3A+5-19.&btnG="", ""children"": [{""text"": ""On democratic backsliding"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Journal of Democracy 27(1): 5-19."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d17ed74ce329417b95da4a87c9128e7be418f923"": {""id"": ""d17ed74ce329417b95da4a87c9128e7be418f923"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Edgell, Amanda B., Seraphine F. Maerz, Laura Maxwell, Richard Morgan, Juraj Medzi- horsky, Matthew C. Wilson, Vanessa A. Boese, Sebastian Hellmeier, Jean Lachapelle, Patrik Lindenfors, Anna Lührmann, and Staffan I. Lindberg. (2024). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Edgell%2C+Amanda+B.%2C+Seraphine+F.+Maerz%2C+Laura+Maxwell%2C+Richard+Morgan%2C+Juraj+Medzi-+horsky%2C+Matthew+C.+Wilson%2C+Vanessa+A.+Boese%2C+Sebastian+Hellmeier%2C+Jean+Lachapelle%2C+Patrik+Lindenfors%2C+Anna+L%C3%BChrmann%2C+and+Staffan+I.+Lindberg.+%282024%29.+Episodes+of+Regime+Transformation+Dataset+%28v14.0%29.+Varieties+of+Democracy+%28V-Dem%29+Project.+Available+at%3A+www.github.com%2Fvdeminstitute%2Fert&btnG="", ""children"": [{""text"": ""Episodes of Regime Transformation Dataset (v14.0)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Varieties of Democracy (V-Dem) Project. Available at: www.github.com/vdeminstitute/ert"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""def7f0b8e1c0f0b857371fa1a8a4729a634e0b1f"": {""id"": ""def7f0b8e1c0f0b857371fa1a8a4729a634e0b1f"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Fabio Angiolillo, Michael Bernhard, Cecilia Borella, Agnes Cornell, M. Steven Fish, Linnea Fox, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Oskar Rydén, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson, and Daniel Ziblatt. 2024. \"""", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Coppedge%2C+Michael%2C+John+Gerring%2C+Carl+Henrik+Knutsen%2C+Staffan+I.+Lindberg%2C+Jan+Teorell%2C+David+Altman%2C+Fabio+Angiolillo%2C+Michael+Bernhard%2C+Cecilia+Borella%2C+Agnes+Cornell%2C+M.+Steven+Fish%2C+Linnea+Fox%2C+Lisa+Gastaldi%2C+Haakon+Gjerl%C3%B8w%2C+Adam+Glynn%2C+Ana+Good+God%2C+Sandra+Grahn%2C+Allen+Hicken%2C+Katrin+Kinzelbach%2C+Joshua+Krusell%2C+Kyle+L.+Marquardt%2C+Kelly+McMann%2C+Valeriya+Mechkova%2C+Juraj+Medzihorsky%2C+Natalia+Natsika%2C+Anja+Neundorf%2C+Pamela+Paxton%2C+Daniel+Pemstein%2C+Josefine+Pernes%2C+Oskar+Ryd%C3%A9n%2C+Johannes+von+R%C3%B6mer%2C+Brigitte+Seim%2C+Rachel+Sigman%2C+Svend-Erik+Skaaning%2C+Jeffrey+Staton%2C+Aksel+Sundstr%C3%B6m%2C+Eitan+Tzelgov%2C+Yi-ting+Wang%2C+Tore+Wig%2C+Steven+Wilson+and+Daniel+Ziblatt.+2024.+%22V-Dem+Country-Year+Dataset+v14%22+Varieties+of+Democracy+%28V-Dem%29+Project.+https%3A%2F%2Fdoi.org%2F10.23696%2Fmcwt-fr58&btnG="", ""children"": [{""text"": ""V-Dem Country-Year Dataset v14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""\"" Varieties of Democracy (V-Dem) Project. https://doi.org/10.23696/mcwt-fr58"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""topic-page"", ""title"": ""Democracy"", ""authors"": [""Bastian Herre"", ""Lucas Rodés-Guirao"", ""Esteban Ortiz-Ospina"", ""Max Roser""], ""excerpt"": ""How has democracy spread across countries? Are we moving towards a more democratic world? Explore global data and research on democracy."", ""sticky-nav"": [{""text"": ""Introduction"", ""target"": ""#introduction""}, {""text"": ""Key Insights"", ""target"": ""#key-insights""}, {""text"": ""Data Explorer"", ""target"": ""#explore-data-on-democracy""}, {""text"": ""Research & Writing"", ""target"": ""#research-writing""}, {""text"": ""Charts"", ""target"": ""#all-charts""}, {""text"": ""Endnotes"", ""target"": ""#article-endnotes""}, {""text"": ""Cite This Work"", ""target"": ""#article-citation""}, {""text"": ""Reuse This Work"", ""target"": ""#article-licence""}], ""featured-image"": ""democracy-topic-page-featured-image.png""}",1,2023-07-21 16:48:57,2013-03-15 19:46:21,2024-02-26 17:56:39,unlisted,ALBJ4LvFElJiTHqmCDifVCx118nFzhX6YMhJ22GzL2O-kC1k-bF65lfmPbEq-Wo98P9xPCvUrCXtFlomXtBBMQ,,"Democracy is broadly understood to mean ‘rule by the people’. In practice, it is often defined as people choosing their leaders in free and fair elections. Other definitions go beyond this. For example, some of them see democracy as people having additional individual rights and being protected from the state. Democracy gives citizens the right to influence important decisions over their own lives and allows them to hold their leaders accountable. But it can have other benefits too: democratic countries seem [better governed](https://www.annualreviews.org/doi/abs/10.1146/annurev-polisci-060820-060910) than autocracies, seem to [grow faster](https://www.journals.uchicago.edu/doi/abs/10.1086/700936?mobileUi=0&), and foster more peaceful conduct [within](https://global.oup.com/academic/product/the-death-and-life-of-state-repression-9780197655375?cc=us&lang=en&#) and [between](https://www.cambridge.org/core/journals/international-organization/article/abs/suffragist-peace/3FC70A0BE87859F624E42984BEB0322B) them. On this page, you can find data, visualizations, and writing on how democracy has spread across countries, how it differs between them, and whether we are moving towards a more democratic world. ## Key Insights on Democracy ### The world has become much more democratic over the last two centuries Many more countries have become democracies over the last two hundred years. The chart shows — based on data from Regimes of the World (RoW) — that a much larger share of countries are now democracies. In the late 18th century, no country could be meaningfully characterized as a democracy. RoW classifies almost all of them as closed autocracies, in which citizens do not have the right to choose their political leaders through elections.1 Elections spread throughout the 19th century, but they were often marred by limitations. Many countries became electoral autocracies, in which political leaders were chosen through elections, but citizens lacked additional freedoms to make those elections free and fair. Only a few countries held elections that were sufficiently meaningful to call them electoral democracies.2 And even fewer had the additional individual and minority rights and the constrained governments to consider them liberal democracies.3 Electoral and liberal democracy then spread to many countries in the 20th century. By the end of the century, they had become common political systems around the globe and could be found across all world regions. Today, the world is about evenly split between autocracies and democracies, according to this data. Most non-democracies are electoral autocracies. And more than a third of all democracies have the additional individual and minority rights that characterize liberal democracies. ### Two centuries ago, everyone lacked democratic rights. Now, billions of people have them Billions of people have gained democratic rights over the last two centuries. The chart shows that many more people now live in democracies, based on the Regimes of the World (RoW) data. In the 19th century, few people had democratic political rights. In 1800, almost everyone lived in political systems that RoW classifies as closed autocracies. No country was a democracy, and only 22 million people lived in the two countries [classified as electoral autocracies](https://ourworldindata.org/explorers/democracy?time=2017&Dataset=Regimes+of+the+World&Metric=%C2%ADPolitical+regime&Sub-metric=Main+classification&country=ARG~AUS~BWA~CHN): the United Kingdom and the United States. Since then, many have gained democratic political rights, especially in the second half of the 20th century. By 2022, about 1.3 billion people lived in electoral democracies in all regions of the world: many live in the populous countries of Indonesia, Brazil, and South Africa. Another billion people lived in liberal democracies, such as those living in Chile, South Korea, and the United States. While democratic rights have spread far, they are still far from universal. Because the world’s population grew faster than democracy spread, the total number of people without democratic rights is higher than ever. Almost all of them reside in just one country: China. There have also been recent setbacks, with many people losing political rights. Most prominently the 1.4 billion people living in India, which — according to the RoW data — became an electoral autocracy in 2017. Other [data sources](https://ourworldindata.org/less-democratic#the-democratic-decline-has-been-substantial-but-more-uncertain-and-limited) agree that India has become less democratic, but overall still consider India a democracy.6 We have an article that discusses the trends in more detail: ### undefined undefined https://docs.google.com/document/d/1ghyH3pzHeRNxnDNZ78FXfIqea6ULupeS3KyCMRoH80A/edit ### Many democracies are less than a generation old. Dictatorship is far from a distant memory Democracy is young in most countries that are democratic today. The chart shows that most electoral democracies are younger than its more senior citizens, relying on the Regimes of the World data. Based on the data, many democracies are less than a generation old. Some are younger than 18 — not older than the children in these countries — and others are only as old as the country’s young adults. In these democracies, most young people have experienced authoritarian rule, and older people have lacked democratic political rights for most of their lives. A larger group of countries have been electoral democracies for one to three generations. In them, children and young adults have only known life in a democracy. But their parents and grandparents have still experienced non-democratic rule. Only a few countries have been democratic for a long time and have been electoral democracies for three generations or more. Democracy there is older than almost all of their citizens. Because electoral democracy is defined as political rights being broad, but not necessarily universal, not _everyone_ has enjoyed democratic political rights in these countries. For example, governments in some countries have forbidden parts of the population, such as women, to vote and stand in elections. [Liberal democracy](https://ourworldindata.org/grapher/number-liberal-democracies-age), in which citizens enjoy additional individual and minority rights, is an even rarer and more recent achievement than electoral democracy. And democracy is a recent achievement regardless of the [measure](https://ourworldindata.org/grapher/number-democracies-age-bmr) used. We have an article that provides more detail: ### undefined undefined https://docs.google.com/document/d/10mpw95MM1OZO__0g75j1rHhEp2pjBlDnUcY08uyvn_E/edit ### People around the world have gained democratic rights, but some have many more rights than others There are large differences in the degree to which citizens enjoy political rights — between democracies and non-democracies, but also within each group. The chart — relying on the Varieties of Democracy (V-Dem) data — shows that some countries are much more democratic than others. While almost all countries are much more democratic than they were [100 years ago](https://ourworldindata.org/grapher/distribution-electoral-democracy-vdem?time=1922), there are still large differences between them. Some countries — mostly located in Europe and the Americas — are highly democratic: they have elected political leaders, elections are broadly free and fair, and most citizens have the right to vote. Others, especially in Asia, are highly undemocratic. Based on V-Dem, this includes countries such as China, North Korea, and Saudi Arabia. There, citizens do not have the right to choose their political leaders in popular elections. Most countries, often in Africa and Asia, fall somewhere in the middle. Political leaders are elected and citizens have the right to vote there, but their rights to associate and express their opinions are limited, and elections are not entirely free and fair. The chart looks at electoral democratic institutions. Looking at [liberal democracy](https://ourworldindata.org/grapher/distribution-liberal-democracy-vdem) or data from [other leading approaches](https://ourworldindata.org/grapher/distribution-democracy-polity) shows similar differences. We have an article that provides more detail and discusses the trends over time: ### undefined undefined https://docs.google.com/document/d/1LNnYyow4VGpM-z8R80D3GbzGHfodTv0EEvBceJxvgR8/edit ### The world has recently become less democratic The world has become less democratic in recent years. The chart shows that more countries have been autocratizing recently, based on the Episodes of Regime Transformation (ERT) data. The number of countries that are autocratizing has been increasing: for 2022, ERT identifies 40 that were autocratizing — an all-time high. For a long time, the number of autocratizing countries was offset by democratizing ones. But since 2012, the number of countries that are becoming more autocratic has been higher. The countries that are becoming less democratic are both [eroding democracies and hardening autocracies](https://ourworldindata.org/grapher/countries-that-are-democratizing-and-autocratizing-within-regimes). Other approaches to measuring democracy also suggest that the world has recently become less democratic — the [number of democracies](https://ourworldindata.org/grapher/countries-democracies-autocracies-row?stackMode=absolute&country=~OWID_WRL) has declined; fewer [people are living in democracies](https://ourworldindata.org/grapher/people-living-in-democracies-autocracies), [countries](https://ourworldindata.org/grapher/electoral-democracy) and [people](https://ourworldindata.org/grapher/electoral-democracy-popw-vdem) have on average fewer democratic rights, and [more people live in](https://ourworldindata.org/grapher/people-living-in-democratizing-autocratizing-countries-ert) autocratizing countries. The world was at its democratic ‘all-time high’ in the early 2010s. But since then it has fallen, and now looks more like the 2000s, the 1990s, or even the late 1980s, depending on which democracy measure we rely on. We have seen similar democratic declines before, and past declines were reversed. People fought previous phases of autocratization in the 1930s and 1960/70s, turned the tide, and pushed democratic rights to unprecedented heights. We can do it again. We have an article that provides more detail: ### undefined undefined https://docs.google.com/document/d/14ThFloGyQv4uSGdEeZROWq_2yvHC9ggnAFij_9Oyz6g/edit # Explore Data on Democracy This explorer seeks to make data on democracy easier to access and understand. It provides and explains data from eight leading democracy datasets: their main democracy measures, indicators of specific characteristics, and global and regional overviews. You can learn more about the approaches — **and which democracy measure may be best to answer your questions** — in our article explaining how researchers measure democracy: ### undefined undefined https://docs.google.com/document/d/1vMsru_zjboUD_W5aBXoMKMMxCxqyJ4oYVCfchYBzDQM/edit ## Related research and writing * https://docs.google.com/document/d/1ghyH3pzHeRNxnDNZ78FXfIqea6ULupeS3KyCMRoH80A/edit ,* https://docs.google.com/document/d/14ThFloGyQv4uSGdEeZROWq_2yvHC9ggnAFij_9Oyz6g/edit ,* https://docs.google.com/document/d/1vMsru_zjboUD_W5aBXoMKMMxCxqyJ4oYVCfchYBzDQM/edit ,* https://docs.google.com/document/d/14zTYMg-mkPcMC68DqgQNb1eAe8VBwoD93yzyKGIeguw/edit ,* https://docs.google.com/document/d/1xO9wHbwM5LKlGFbHyet3CLmiqnI3ZOHS8V3P_81Itf4/edit ,* https://docs.google.com/document/d/10mpw95MM1OZO__0g75j1rHhEp2pjBlDnUcY08uyvn_E/edit ,* https://docs.google.com/document/d/1LNnYyow4VGpM-z8R80D3GbzGHfodTv0EEvBceJxvgR8/edit ,* https://docs.google.com/document/d/15TD9DyPmgOUFC8pMzMczLJmNTIw_j-gBM32JpInPJIU/edit The United Kingdom and the United States were the only countries that could be classified as electoral autocracies, because its political leaders were chosen through elections, but citizens lacked additional freedoms to make those elections free and fair. Examples are France and New Zealand. Examples are Australia, Belgium, and Switzerland. Lührmann, Anna, Marcus Tannenberg, and Staffan Lindberg. 2018. [Regimes of the World (RoW): Opening New Avenues for the Comparative Study of Political Regimes](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=L%C3%BChrmann%2C+Anna%2C+Marcus+Tannenberg%2C+and+Staffan+Lindberg.+2018.+Regimes+of+the+World+%28RoW%29%3A+Opening+New+Avenues+for+the+Comparative+Study+of+Political+Regimes.+Politics+and+Governance+6%281%29%3A+60-77.&btnG=). Politics and Governance 6(1): 60-77. Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Ana Good God, Sandra Grahn, Allen Hicken, Katrin Kinzelbach, Joshua Krusell, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Juraj Medzihorsky, Natalia Natsika, Anja Neundorf, Pamela Paxton, Daniel Pemstein, Josefine Pernes, Oskar Rydén, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, Steven Wilson and Daniel Ziblatt. 2023.[ ](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Coppedge%2C+Michael%2C+John+Gerring%2C+Carl+Henrik+Knutsen%2C+Staffan+I.+Lindberg%2C+Jan+Teorell%2C+David+Altman%2C+Michael+Bernhard%2C+Agnes+Cornell%2C+M.+Steven+Fish%2C+Lisa+Gastaldi%2C+Haakon+Gjerl%C3%B8w%2C+Adam+Glynn%2C+Ana+Good+God%2C+Sandra+Grahn%2C+Allen+Hicken%2C+Katrin+Kinzelbach%2C+Joshua+Krusell%2C+Kyle+L.+Marquardt%2C+Kelly+McMann%2C+Valeriya+Mechkova%2C+Juraj+Medzihorsky%2C+Natalia+Natsika%2C+Anja+Neundorf%2C+Pamela+Paxton%2C+Daniel+Pemstein%2C+Josefine+Pernes%2C+Oskar+Ryd%C3%A9n%2C+Johannes+von+R%C3%B6mer%2C+Brigitte+Seim%2C+Rachel+Sigman%2C+Svend-Erik+Skaaning%2C+Jeffrey+Staton%2C+Aksel+Sundstr%C3%B6m%2C+Eitan+Tzelgov%2C+Yi-ting+Wang%2C+Tore+Wig%2C+Steven+Wilson+and+Daniel+Ziblatt.+2023.+V-Dem+%5BCountry-Year%2FCountry-Date%5D+Dataset+v13.+Varieties+of+Democracy+%28V-Dem%29+Project.&btnG=)[V-Dem [Country-Year/Country-Date] Dataset v13.](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Coppedge%2C+Michael%2C+John+Gerring%2C+Carl+Henrik+Knutsen%2C+Staffan+I.+Lindberg%2C+Jan+Teorell%2C+David+Altman%2C+Michael+Bernhard%2C+Agnes+Cornell%2C+M.+Steven+Fish%2C+Lisa+Gastaldi%2C+Haakon+Gjerl%C3%B8w%2C+Adam+Glynn%2C+Ana+Good+God%2C+Sandra+Grahn%2C+Allen+Hicken%2C+Katrin+Kinzelbach%2C+Joshua+Krusell%2C+Kyle+L.+Marquardt%2C+Kelly+McMann%2C+Valeriya+Mechkova%2C+Juraj+Medzihorsky%2C+Natalia+Natsika%2C+Anja+Neundorf%2C+Pamela+Paxton%2C+Daniel+Pemstein%2C+Josefine+Pernes%2C+Oskar+Ryd%C3%A9n%2C+Johannes+von+R%C3%B6mer%2C+Brigitte+Seim%2C+Rachel+Sigman%2C+Svend-Erik+Skaaning%2C+Jeffrey+Staton%2C+Aksel+Sundstr%C3%B6m%2C+Eitan+Tzelgov%2C+Yi-ting+Wang%2C+Tore+Wig%2C+Steven+Wilson+and+Daniel+Ziblatt.+2023.+V-Dem+%5BCountry-Year%2FCountry-Date%5D+Dataset+v13.+Varieties+of+Democracy+%28V-Dem%29+Project.&btnG=) Varieties of Democracy (V-Dem) Project. RoW’s reclassification is the result of recent changes in the V-Dem data, which identify declines in the autonomy of the election management body, the freedom and fairness of elections, and especially the freedom of expression, the media, and civil society. You can read more in V-Dem’s 2021 annual report[ ](https://web.archive.org/web/20220130230849/https://v-dem.net/static/website/files/dr/dr_2021.pdf)[Autocratization Turns Viral](https://web.archive.org/web/20220130230849/https://v-dem.net/static/website/files/dr/dr_2021.pdf). Edgell, Amanda, Seraphine Maerz, Laura Maxwell, Richard Morgan, Juraj Medzihorsky, Matthew Wilson, Vanessa Boese, Sebastian Hellmeier, Jean Lachapelle, Patrik Lindenfors, Anna Lührmann, and Staffan Lindberg. 2022.[ ](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=dgell%2C+Amanda%2C+Seraphine+Maerz%2C+Laura+Maxwell%2C+Richard+Morgan%2C+Juraj+Medzihorsky%2C+Matthew+Wilson%2C+Vanessa+Boese%2C+Sebastian+Hellmeier%2C+Jean+Lachapelle%2C+Patrik+Lindenfors%2C+Anna+L%C3%BChrmann%2C+and+Staffan+Lindberg.+2022.+Episodes+of+Regime+Transformation+Dataset+%28v13%29.+Varieties+of+Democracy+%28V-Dem%29+Project.&btnG=)[Episodes of Regime Transformation Dataset (v13)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=dgell%2C+Amanda%2C+Seraphine+Maerz%2C+Laura+Maxwell%2C+Richard+Morgan%2C+Juraj+Medzihorsky%2C+Matthew+Wilson%2C+Vanessa+Boese%2C+Sebastian+Hellmeier%2C+Jean+Lachapelle%2C+Patrik+Lindenfors%2C+Anna+L%C3%BChrmann%2C+and+Staffan+Lindberg.+2022.+Episodes+of+Regime+Transformation+Dataset+%28v13%29.+Varieties+of+Democracy+%28V-Dem%29+Project.&btnG=). Varieties of Democracy (V-Dem) Project. Based on ERT, a country is autocratizing from when V-Dem’s electoral democracy index decreases by 0.01, until the score increases or remains unchanged for four years, and the total decrease between the start and end amounts to a decrease of at least 0.10. Democratizing countries are classified analogously. We exclude Ukraine from 2002 to 2004 and El Salvador from 2015 and 2017 because for them democratizing and autocratizing episodes happen to overlap. Seraphine Maerz, Amanda Edgell, Matthew Wilson, Sebastian Hellmeier, Staffan Lindberg. 2021. [A Framework for Understanding Regime Transformation: Introducing the ERT Dataset](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Seraphine+Maerz%2C+Amanda+Edgell%2C+Matthew+Wilson%2C+Sebastian+Hellmeier%2C+Staffan++Lindberg.+2021.+A+Framework+for+Understanding+Regime+Transformation%3A+Introducing+the+ERT+Dataset.+Varieties+of+Democracy+Institute%3A+Working+Paper+No.+113.++University+of+Gothenburg.&btnG=). Varieties of Democracy Institute: Working Paper No. 113. University of Gothenburg. This means, however, that some countries are not classified as autocratizing even though their score visibly declines. One example is the United States in the 2010s, whose decline between 2015 and 2020 fell just barely short of the ERT threshold. Bermeo, Nancy. 2016. [On democratic backsliding](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Bermeo%2C+Nancy.+2016.+On+democratic+backsliding.+Journal+of+Democracy+27%281%29%3A+5-19.&btnG=). Journal of Democracy 27(1): 5-19.",Democracy 1rzaBWNe1jarUZIhWvqJOB3Om4RzgJ1rlYu20Ic7O5Og,ai-impact,article,"{""toc"": [{""slug"": ""the-advantages-and-disadvantages-of-comparing-machine-and-human-intelligence"", ""text"": ""The advantages and disadvantages of comparing machine and human intelligence"", ""title"": ""The advantages and disadvantages of comparing machine and human intelligence"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""transformative-artificial-intelligence-is-defined-by-the-impact-this-technology-would-have-on-the-world"", ""text"": ""Transformative artificial intelligence is defined by the impact this technology would have on the world"", ""title"": ""Transformative artificial intelligence is defined by the impact this technology would have on the world"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""a-future-of-human-level-or-transformative-ai"", ""text"": ""A future of human-level or transformative AI?"", ""title"": ""A future of human-level or transformative AI?"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Why should you care about the development of artificial intelligence?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Think about what the alternative would look like. If you and the wider public do not get informed and engaged, then we leave it to a few entrepreneurs and engineers to decide how this technology will transform our world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""That is the status quo. This small number of people at a few tech firms directly working on artificial intelligence (AI) do understand how extraordinarily powerful this technology is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/brief-history-of-ai"", ""children"": [{""text"": ""becoming"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". If the rest of society does not become engaged, then it will be this small elite who decides how this technology will change our lives."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To change this status quo, I want to answer three questions in this article: Why is it hard to take the prospect of a world transformed by AI seriously? How can we imagine such a world? And what is at stake as this technology becomes more powerful?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Why is it hard to take the prospect of a world transformed by artificial intelligence seriously?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In some way, it should be obvious how technology can fundamentally transform the world. We just have to look at how much the world has already changed. If you could invite a family of hunter-gatherers from 20,000 years ago on your next flight, they would be pretty surprised. Technology has changed our world already, so we should expect that it can happen again."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But while we have seen the world transform before, we have seen these transformations play out over the course of generations. What is different now is how very rapid these technological changes have become. In the past, the technologies that our ancestors used in their childhood were still central to their lives in their old age. This has not been the case anymore for recent generations. Instead, it has "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/technology-long-run"", ""children"": [{""text"": ""become common"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that technologies unimaginable in one's youth become ordinary in later life."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is the first reason we might not take the prospect seriously: it is easy to underestimate the speed at which technology can change the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The second reason why it is difficult to take the possibility of transformative AI – potentially even AI as intelligent as humans – seriously is that it is an idea that we first heard in the cinema. It is not surprising that for many of us, the first reaction to a scenario in which machines have human-like capabilities is the same as if you had asked us to take seriously a future in which vampires, werewolves, or zombies roam the planet."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But, it is plausible that it is both the stuff of sci-fi fantasy "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""and"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" the central invention that could arrive in our, or our children’s, lifetimes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The third reason why it is difficult to take this prospect seriously is by failing to see that powerful AI could lead to very large changes. This is also understandable. It is difficult to form an idea of a future that is very different from our own time. There are two concepts that I find helpful in imagining a very different future with artificial intelligence. Let’s look at both of them."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How to develop an idea of what the future of artificial intelligence might look like?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When thinking about the future of artificial intelligence, I find it helpful to consider two different concepts in particular: human-level AI, and transformative AI."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The first concept highlights the AI’s capabilities and anchors them to a familiar benchmark, while transformative AI emphasizes the impact that this technology would have on the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""From where we are today, much of this may sound like science fiction. It is therefore worth keeping in mind that the majority of surveyed AI experts "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/ai-timelines"", ""children"": [{""text"": ""believe"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" there is a real chance that human-level artificial intelligence will be developed within the next decades, and some believe that it will exist much sooner."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""The advantages and disadvantages of comparing machine and human intelligence"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One way to think about human-level artificial intelligence is to contrast it with the current state of AI technology. While today’s AI systems often have capabilities similar to a particular, limited part of the human mind, a human-level AI would be a machine that is capable of carrying out the same "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""range"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of intellectual tasks that we humans are capable of."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" It is a machine that would be “able to learn to do anything that a human can do,” as Norvig and Russell put it in their textbook on AI."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Taken together, the range of abilities that characterize intelligence gives humans the ability to solve problems and achieve a wide variety of goals. A human-level AI would therefore be a system that could solve all those problems that we humans can solve, and do the tasks that humans do today. Such a machine, or collective of machines, would be able to do the work of a translator, an accountant, an illustrator, a teacher, a therapist, a truck driver, or the work of a trader on the world’s financial markets. Like us, it would also be able to do research and science, and to develop new technologies based on that."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The concept of human-level AI has some clear advantages. Using the familiarity of our own intelligence as a reference provides us with some clear guidance on how to imagine the capabilities of this technology."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, it also has clear disadvantages. Anchoring the imagination of future AI systems to the familiar reality of human intelligence carries the risk that it obscures the very real differences between them."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Some of these differences are obvious. For example, AI systems will have the immense memory of computer systems, against which our own capacity to store information pales. Another obvious difference is the speed at which a machine can absorb and process information. But information storage and processing speed are not the only differences. The domains in which machines already outperform humans is steadily increasing: in chess, after matching the level of the best human players in the late 90s, AI systems "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/computer-chess-ability"", ""children"": [{""text"": ""reached"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" superhuman levels more than a decade ago. In other games like Go or complex strategy games, this has happened more recently."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These differences mean that an AI that is at least as good as humans in every domain would overall be much more powerful than the human mind. Even the first “human-level AI” would therefore be quite superhuman in many ways."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Human intelligence is also a bad metaphor for machine intelligence in other ways. The way we think is often very different from machines, and as a consequence the output of thinking machines can be very alien to us."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most perplexing and most concerning are the strange and unexpected ways in which machine intelligence can fail. The AI-generated image of the horse below provides an example: on the one hand, AIs can do what no human can do – produce an image of anything, in any style (here photorealistic), in mere seconds – but on the other hand it can fail in ways that no human would fail."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" No human would make the mistake of drawing a horse with five legs."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Imagining a powerful future AI as just another human would therefore likely be a mistake. The differences might be so large that it will be a misnomer to call such systems “human-level.”"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""AI-generated image of a horse"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""alt"": ""A brown horse running in a grassy field. The horse appears to have five legs."", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""ai-generated-image-of-a-horse.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Transformative artificial intelligence is defined by the impact this technology would have on the world"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In contrast, the concept of transformative AI is not based on a comparison with human intelligence. This has the advantage of sidestepping the problems that the comparisons with our own mind bring. But it has the disadvantage that it is harder to imagine what such a system would look like and be capable of. It requires more from us. It requires us to imagine a world with intelligent actors that are potentially very different from ourselves."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Transformative AI is not defined by any specific capabilities, but by the real-world impact that the AI would have. To qualify as transformative, researchers think of it as AI that is “powerful enough to bring us into a new, qualitatively different future.”"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In humanity’s history, there have been two cases of such major transformations, the agricultural and the industrial revolutions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Transformative AI becoming a reality would be an event on that scale. Like the arrival of agriculture 10,000 years ago, or the transition from hand- to machine-manufacturing, it would be an event that would change the world for billions of people around the globe and for the entire trajectory of "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/longtermism"", ""children"": [{""text"": ""humanity’s future"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Technologies that fundamentally change how a wide range of goods or services are produced are called ‘general-purpose technologies’. The two previous transformative events were caused by the discovery of two particularly significant general-purpose technologies: the change in food production as humanity transitioned from hunting and gathering to farming, and the rise of machine manufacturing in the industrial revolution. Based on the evidence and arguments presented in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/artificial-intelligence#research-writing"", ""children"": [{""text"": ""this series"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" on AI development, I believe it is plausible that powerful AI could represent the introduction of a similarly significant general-purpose technology."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Timeline of the three transformative events in world history"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Timeline-of-Transformative-Events.png"", ""parseErrors"": []}, {""text"": [{""text"": ""A future of human-level or transformative AI?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The two concepts are closely related, but they are not the same. The creation of a human-level AI would certainly have a transformative impact on our world. If the work of most humans could be carried out by an AI, the lives of millions of people would change."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The opposite, however, is not true: we might see transformative AI without developing human-level AI. Since the human mind is in many ways a poor metaphor for the intelligence of machines, we might plausibly develop transformative AI before we develop human-level AI. Depending on how this goes, this might mean that we will never see any machine intelligence for which human intelligence is a helpful comparison."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When and if AI systems might reach either of these levels is of course difficult to predict. In my "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/ai-timelines"", ""children"": [{""text"": ""companion article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" on this question, I give an overview of what researchers in this field currently believe. Many AI experts believe there is a real chance that such systems will be developed within the next decades, and some believe that they will exist much sooner."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What is at stake as artificial intelligence becomes more powerful?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All major technological innovations lead to a range of positive and negative consequences. For AI, the spectrum of possible outcomes – from the most negative to the most positive – is extraordinarily wide."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""That the use of AI technology can cause harm is clear, because it is already happening."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""AI systems can cause harm when people use them maliciously. For example, when they are used in politically-motivated disinformation campaigns or to enable mass surveillance."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But AI systems can also cause unintended harm, when they act differently than intended or fail. For example, in the Netherlands the authorities used an AI system which falsely claimed that an estimated 26,000 parents made fraudulent claims for child care benefits. The false allegations led to hardship for many poor families, and also resulted in the resignation of the Dutch government in 2021."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As AI becomes more powerful, the possible negative impacts could become much larger. Many of these risks have rightfully received public attention: more powerful AI could lead to mass labor displacement, or extreme concentrations of power and wealth. In the hands of autocrats, it could empower totalitarianism through its suitability for mass surveillance and control."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The so-called "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""alignment problem"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of AI is another extreme risk. This is the concern that "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""nobody"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" would be able to control a powerful AI system, even if the AI takes actions that harm us humans, or humanity as a whole. This risk is unfortunately receiving little attention from the wider public, but it is seen as an extremely large risk by many leading AI researchers."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How could an AI possibly escape human control and end up harming humans?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The risk is not that an AI becomes self-aware, develops bad intentions, and “chooses” to do this. The risk is that we try to instruct the AI to pursue some specific goal – even a very worthwhile one – and in the pursuit of that goal it ends up harming humans. It is about unintended consequences. The AI does what we told it to do, but not what we wanted it to do."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Can’t we just tell the AI to not do those things? It is definitely possible to build an AI that avoids any particular problem we foresee, but it is hard to foresee "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""all"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" the possible harmful unintended consequences. The alignment problem arises because of “the impossibility of defining true human purposes correctly and completely,” as AI researcher Stuart Russell puts it."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-15"", ""children"": [{""children"": [{""text"": ""15"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Can’t we then just switch off the AI? This might also not be possible. That is because a powerful AI would know two things: it faces a risk that humans could turn it off, and it can’t achieve its goals once it has been turned off. As a consequence, the AI will pursue a very fundamental goal of ensuring that it won’t be switched off. This is why, once we realize that an extremely intelligent AI is causing unintended harm in the pursuit of some specific goal, it might not be possible to turn it off or change what the system does."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-16"", ""children"": [{""children"": [{""text"": ""16"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This risk – that humanity might not be able to stay in control once AI becomes very powerful, and that this might lead to an extreme catastrophe – has been recognized right from the early days of AI research more than 70 years ago."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-17"", ""children"": [{""children"": [{""text"": ""17"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The very rapid development of AI in recent years has made a solution to this problem much more urgent."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I have tried to summarize some of the risks of AI, but a short article is not enough space to address all possible questions. Especially on the very worst risks of AI systems, and what we can do now to reduce them, I recommend reading the book "", ""spanType"": ""span-simple-text""}, {""url"": ""https://brianchristian.org/the-alignment-problem/"", ""children"": [{""text"": ""The Alignment Problem"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" by Brian Christian and Benjamin Hilton’s article "", ""spanType"": ""span-simple-text""}, {""url"": ""https://80000hours.org/problem-profiles/artificial-intelligence"", ""children"": [{""text"": ""‘Preventing an AI-related catastrophe’"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we manage to avoid these risks, transformative AI could also lead to very positive consequences. Advances in science and technology were crucial to "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/a-history-of-global-living-conditions-in-5-charts"", ""children"": [{""text"": ""the many positive developments"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in humanity’s history. If artificial ingenuity can augment our own, it could help us make progress on the many large problems we face: from cleaner energy, to the replacement of unpleasant work, to much better healthcare."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This extremely large contrast between the possible positives and negatives makes clear that the stakes are unusually high with this technology. Reducing the negative risks and solving the alignment problem could mean the difference between a healthy, flourishing, and wealthy future for humanity – and the destruction of the same."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""How can we make sure that the development of AI goes well?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Making sure that the development of artificial intelligence goes well is not just one of the most crucial questions of our time, but likely one of the most crucial questions in human history. This needs public resources – public funding, public attention, and public engagement."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Currently, almost all resources that are dedicated to AI aim to speed up the development of this technology. Efforts that aim to increase the safety of AI systems, on the other hand, do not receive the resources they need. Researcher Toby Ord estimated that in 2020 between $10 to $50 million was spent on work to address the alignment problem."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-18"", ""children"": [{""children"": [{""text"": ""18"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Corporate AI investment in the same year was more than 2000-times larger, it "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/corporate-investment-in-artificial-intelligence-by-type"", ""children"": [{""text"": ""summed up"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" to $153 billion."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is not only the case for the AI alignment problem. The work on the entire range of negative social consequences from AI is under-resourced compared to the large investments to increase the power and use of AI systems."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is frustrating and concerning for society as a whole that AI safety work is extremely neglected and that little public funding is dedicated to this crucial field of research. On the other hand, for each "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""individual"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" person this neglect means that they have a good chance to actually make a positive difference, if they dedicate themselves to this problem now. And while the field of AI safety is small, it does provide "", ""spanType"": ""span-simple-text""}, {""url"": ""https://80000hours.org/problem-profiles/artificial-intelligence/#what-can-you-do-concretely-to-help"", ""children"": [{""text"": ""good resources"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" on what you can do concretely if you want to work on this problem."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""I hope that more people dedicate their individual careers to this cause, but it needs more than individual efforts. A technology that is transforming our society needs to be a central interest of all of us. As a society we have to think more about the societal impact of AI, become knowledgeable about the technology, and understand what is at stake."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When our children look back at today, I imagine that they will find it difficult to understand how little attention and resources we dedicated to the development of safe AI. I hope that this changes in the coming years, and that we begin to dedicate more resources to making sure that powerful AI gets developed in a way that benefits us and the next generations."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we fail to develop this broad-based understanding, then it will remain the small elite that finances and builds this technology that will determine how one of the – or plausibly "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""the"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" – most powerful technology in human history will transform our world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we leave the development of artificial intelligence entirely to private companies, then we are also leaving it up these private companies what our future — the future of humanity — will be."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""With our work at Our World in Data we want to do our small part to enable a better informed public conversation on AI and the future we want to live in. You can find these resources on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/artificial-intelligence"", ""children"": [{""text"": ""OurWorldinData.org/artificial-intelligence"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""Acknowledgements:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""I would like to thank my colleagues Daniel Bachler, Charlie Giattino, and Edouard Mathieu for their helpful comments to drafts of this essay."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""15b8d11f280a1dbf1aeb137a5de9c080632bd7c2"": {""id"": ""15b8d11f280a1dbf1aeb137a5de9c080632bd7c2"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The fact that humans are capable of a "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""range"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of intellectual tasks means that you arrive at different definitions of intelligence depending on which aspect within that range you focus on (the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/Intelligence"", ""children"": [{""text"": ""Wikipedia entry on intelligence"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", for example, lists a number of definitions from various researchers and different disciplines). As a consequence there are also various definitions of ‘human-level AI’."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are also several closely related terms: Artificial General Intelligence, High-Level Machine Intelligence, Strong AI, or Full AI are sometimes synonymously used, and sometimes defined in similar, yet different ways. In specific discussions, it is necessary to define this concept more narrowly; for example, in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/ai-timelines"", ""children"": [{""text"": ""studies on AI timelines"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" researchers offer more precise definitions of what human-level AI refers to in their particular study."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""32cd94a3d7dd125248573a23c5156035d19b4bbb"": {""id"": ""32cd94a3d7dd125248573a23c5156035d19b4bbb"", ""index"": 17, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Toby Ord – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://theprecipice.com/"", ""children"": [{""text"": ""The Precipice"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". He makes this projection in footnote 55 of chapter 2. It is based on the 2017 estimate by Farquhar."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""36b08a8447eb6df6bc2e607673339303cde4a5b9"": {""id"": ""36b08a8447eb6df6bc2e607673339303cde4a5b9"", ""index"": 13, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Overviews are provided in Stuart Russell (2019) – Human Compatible (especially chapter 5) and Brian Christian’s 2020 book "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/The_Alignment_Problem"", ""children"": [{""text"": ""The Alignment Problem"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Christian presents the thinking of many leading AI researchers from the earliest days up to now and presents an excellent overview of this problem. It is also seen as a large risk by some of the leading private firms who work towards powerful AI – see OpenAI's article \"""", ""spanType"": ""span-simple-text""}, {""url"": ""https://openai.com/blog/our-approach-to-alignment-research/"", ""children"": [{""text"": ""Our approach to alignment research"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""\"" from August 2022."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3803a99b99b604e8cbf87ef5bcfedad6d8a0f314"": {""id"": ""3803a99b99b604e8cbf87ef5bcfedad6d8a0f314"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Both of these concepts are widely used in the scientific literature on artificial intelligence. For example, questions about the timelines for the development of future AI are often framed using these terms. See "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/ai-timelines"", ""children"": [{""text"": ""my article on this topic"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""4fe0e7c8a9ed61157baa1a73b31b84abf5c5727c"": {""id"": ""4fe0e7c8a9ed61157baa1a73b31b84abf5c5727c"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Via "", ""spanType"": ""span-simple-text""}, {""url"": ""https://fchollet.com/"", ""children"": [{""text"": ""François Chollet"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", who published it "", ""spanType"": ""span-simple-text""}, {""url"": ""https://twitter.com/fchollet/status/1573752180720312320?s=46&t=qPwLwDgLdJrLlXxa878BDQ"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Based on Chollet’s comments it seems that this image was created by the AI system ‘Stable Diffusion’."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""776753891ec3a927ce8aeb984fd7751a45d25152"": {""id"": ""776753891ec3a927ce8aeb984fd7751a45d25152"", ""index"": 16, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""In 1950 the computer science pioneer Alan Turing put it like this: "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""“If a machine can think, it might think more intelligently than we do, and then where should we be? … [T]his new danger is much closer. If it comes at all it will almost certainly be within the next millennium. It is remote but not astronomically remote, and is certainly something which can give us anxiety. It is customary, in a talk or article on this subject, to offer a grain of comfort, in the form of a statement that some particularly human characteristic could never be imitated by a machine. … I cannot offer any such comfort, for I believe that no such bounds can be set.”"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" Alan. M. Turing (1950) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1093/mind/LIX.236.433"", ""children"": [{""text"": ""Computing Machinery and Intelligence"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", In Mind, Volume LIX, Issue 236, October 1950, Pages 433–460."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Norbert Wiener is another pioneer who saw the alignment problem very early. One way he put it was “If we use, to achieve our purposes, a mechanical agency with whose operation we cannot interfere effectively … we had better be quite sure that the purpose put into the machine is the purpose which we really desire.” quoted from Norbert Wiener (1960) – Some Moral and Technical Consequences of Automation: As machines learn they may develop unforeseen strategies at rates that baffle their programmers. In Science."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 1950 – the same year in which Turing published the cited article – Wiener published his book The Human Use of Human Beings, whose front-cover blurb reads: “The ‘mechanical brain’ and similar machines can destroy human values or enable us to realize them as never before.”"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7e556c5304f23ca82f3ed158aadb0dd3f564b931"": {""id"": ""7e556c5304f23ca82f3ed158aadb0dd3f564b931"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This problem becomes even larger when we try to imagine how a future with a human-level AI might play out. Any "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""particular"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" scenario will not only involve the idea that this powerful AI exists, but a whole range of additional assumptions about the future context in which this happens. It is therefore hard to communicate a scenario of a world with human-level AI that does not sound contrived, bizarre or even silly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""960b4be1a02e0e0cbf607a69487f11c7dc45520d"": {""id"": ""960b4be1a02e0e0cbf607a69487f11c7dc45520d"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""An overview of how AI systems can fail can be found in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://spectrum.ieee.org/ai-failures"", ""children"": [{""text"": ""Charles Choi – 7 Revealing Ways AIs Fail"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". It is also worth reading through the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.aiaaic.org/aiaaic-repository/ai-and-algorithmic-incidents-and-controversies"", ""children"": [{""text"": ""AIAAIC Repository"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" which “details recent incidents and controversies driven by or relating to AI, algorithms, and automation.\"""", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9784e80f3d4b24c2c512a8b421b52c8376dc6453"": {""id"": ""9784e80f3d4b24c2c512a8b421b52c8376dc6453"", ""index"": 12, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See for example the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/Dutch_childcare_benefits_scandal"", ""children"": [{""text"": ""Wikipedia entry"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" on the ‘Dutch childcare benefits scandal’ and Melissa Heikkilä (2022) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20221117053636/https://www.politico.eu/article/dutch-scandal-serves-as-a-warning-for-europe-over-risks-of-using-algorithms/"", ""children"": [{""text"": ""‘Dutch scandal serves as a warning for Europe over risks of using algorithms’"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", in Politico. The technology can also reinforce discrimination in terms of race and gender. See Brian Christian’s book The Alignment Problem and the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ainowinstitute.org/reports.html"", ""children"": [{""text"": ""reports of the AI Now Institute"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a57211a6812c3f1b91fbb97b51a92a4a143300a3"": {""id"": ""a57211a6812c3f1b91fbb97b51a92a4a143300a3"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The AI system "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/AlphaGo"", ""children"": [{""text"": ""AlphaGo"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and its various successors, won against Go masters. The AI system "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/Pluribus_(poker_bot)"", ""children"": [{""text"": ""Pluribus"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" beat humans at no-limit Texas hold 'em poker. The AI system Cicero can strategize and use human language to win the strategy game Diplomacy. See: Meta Fundamental AI Research Diplomacy Team (FAIR), Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, et al. (2022) – ‘Human-Level Play in the Game of Diplomacy by Combining Language Models with Strategic Reasoning’. In "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Science"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" 0, no. 0 (22 November 2022): eade9097."", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1126/science.ade9097"", ""children"": [{""text"": "" https://doi.org/10.1126/science.ade9097"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""af2007409462ca43d46ceb247c62bdea530b5f37"": {""id"": ""af2007409462ca43d46ceb247c62bdea530b5f37"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Peter Norvig and Stuart Russell (2021) — Artificial Intelligence: A Modern Approach. Fourth edition. Published by Pearson."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c0154595c4bb771c9ef95e78235cce456af9d8ea"": {""id"": ""c0154595c4bb771c9ef95e78235cce456af9d8ea"", ""index"": 15, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""A question that follows from this is, why build such a powerful AI in the first place?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The incentives are very high. As I emphasize below, this innovation has the potential to lead to very positive developments. In addition to the large social benefits there are also large incentives for those who develop it – the governments that can use it for their goals, the individuals who can use it to become more powerful and wealthy. Additionally, it is of scientific interest and might help us to understand our own mind and intelligence better. And lastly, even if we wanted to stop building powerful AIs, it is likely very hard to actually achieve it. It is very hard to coordinate across the whole world and agree to stop building more advanced AI – countries around the world would have to agree and then find ways to actually implement it."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d3da710183f88e9790f68121e8bdb79d80c48f07"": {""id"": ""d3da710183f88e9790f68121e8bdb79d80c48f07"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""I have taken this example from "", ""spanType"": ""span-simple-text""}, {""url"": ""https://fchollet.com/"", ""children"": [{""text"": ""AI researcher François Chollet"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", who published it "", ""spanType"": ""span-simple-text""}, {""url"": ""https://twitter.com/fchollet/status/1573752180720312320?s=46&t=qPwLwDgLdJrLlXxa878BDQ"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d5cff4ba485710dea7e721b63d1e62ee9cbb638f"": {""id"": ""d5cff4ba485710dea7e721b63d1e62ee9cbb638f"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This also poses a problem when we evaluate how the intelligence of a machine compares with the intelligence of humans. If intelligence was a general ability, a single capacity, then we could easily compare and evaluate it, but the fact that it is a range of skills makes it much more difficult to compare across machine and human intelligence. Tests for AI systems are therefore comprising a wide range of tasks. See for example Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt (2020) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://arxiv.org/abs/2009.03300"", ""children"": [{""text"": ""Measuring Massive Multitask Language Understanding"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" or the definition of what would qualify as artificial general intelligence in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.metaculus.com/questions/5121/date-of-artificial-general-intelligence/"", ""children"": [{""text"": ""this Metaculus prediction"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d5d34650c210dc1faabd1ef78e9c786152bf1635"": {""id"": ""d5d34650c210dc1faabd1ef78e9c786152bf1635"", ""index"": 14, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Stuart Russell (2019) – Human Compatible"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d785795c377b1e7625cdb90aa7510ea471cb4908"": {""id"": ""d785795c377b1e7625cdb90aa7510ea471cb4908"", ""index"": 9, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This quote is from Holden Karnofsky (2021) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.cold-takes.com/where-ai-forecasting-stands-today/"", ""children"": [{""text"": ""AI Timelines: Where the Arguments, and the \""Experts,\"" Stand"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". For Holden Karnofsky’s earlier thinking on this conceptualization of AI see his 2016 article "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.openphilanthropy.org/research/some-background-on-our-views-regarding-advanced-artificial-intelligence/#Sec1"", ""children"": [{""text"": ""‘Some Background on Our Views Regarding Advanced Artificial Intelligence’"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Ajeya Cotra, whose research on AI timelines I discuss in other articles of this series, attempts to give a quantitative definition of what would qualify as transformative AI. in her widely cited "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.alignmentforum.org/posts/KrJfoZzpSDpnrv9va/draft-report-on-ai-timelines"", ""children"": [{""text"": ""report on AI timelines"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" she defines it as a change in software technology that brings the growth rate of gross world product \""to 20%-30% per year\"". Several other researchers define TAI in similar terms."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e76fc141fcfb37efe234648d1215b2738ef11ebc"": {""id"": ""e76fc141fcfb37efe234648d1215b2738ef11ebc"", ""index"": 10, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Human-level AI is typically defined as a software system that can carry out at least 90% or 99% of all economically relevant tasks that humans carry out. A lower-bar definition would be an AI system that can carry out all those tasks that can currently be done by another human who is working remotely on a computer."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e9873918d62a99c88e50611c4281f06bcee87c9c"": {""id"": ""e9873918d62a99c88e50611c4281f06bcee87c9c"", ""index"": 11, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""On the use of AI in politically-motivated disinformation campaigns see for example John Villasenor (November 2020) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20220907044354/https://www.brookings.edu/research/how-to-deal-with-ai-enabled-disinformation/"", ""children"": [{""text"": ""How to deal with AI-enabled disinformation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". More generally on this topic see Brundage and Avin et al. (2018) – The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, published at "", ""spanType"": ""span-simple-text""}, {""url"": ""https://maliciousaireport.com/"", ""children"": [{""text"": ""maliciousaireport.com"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". A starting point for literature and reporting on mass surveillance by governments is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/List_of_government_mass_surveillance_projects"", ""children"": [{""text"": ""the relevant Wikipedia entry"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Artificial intelligence is transforming our world — it is on all of us to make sure that it goes well"", ""authors"": [""Max Roser""], ""excerpt"": ""How AI gets built is currently decided by a small group of technologists. It should be in all of our interest to become informed and engaged."", ""dateline"": ""December 15, 2022"", ""subtitle"": ""How AI gets built is currently decided by a small group of technologists. As this technology is transforming our lives, it should be in all of our interest to become informed and engaged."", ""featured-image"": ""featured-image-Timeline-of-Transformative-Events-1.png""}",1,2023-10-13 06:26:10,2022-12-15 05:00:00,2023-12-28 16:31:11,listed,ALBJ4LsU9pT56T0znmJ5pPAg-p879caIZht0P67H_gRP5Gy66J10grB_l8rxNU7CEPXPFC1GV3K86aquyKll8g,,"Why should you care about the development of artificial intelligence? Think about what the alternative would look like. If you and the wider public do not get informed and engaged, then we leave it to a few entrepreneurs and engineers to decide how this technology will transform our world. That is the status quo. This small number of people at a few tech firms directly working on artificial intelligence (AI) do understand how extraordinarily powerful this technology is [becoming](https://ourworldindata.org/brief-history-of-ai). If the rest of society does not become engaged, then it will be this small elite who decides how this technology will change our lives. To change this status quo, I want to answer three questions in this article: Why is it hard to take the prospect of a world transformed by AI seriously? How can we imagine such a world? And what is at stake as this technology becomes more powerful? # Why is it hard to take the prospect of a world transformed by artificial intelligence seriously? In some way, it should be obvious how technology can fundamentally transform the world. We just have to look at how much the world has already changed. If you could invite a family of hunter-gatherers from 20,000 years ago on your next flight, they would be pretty surprised. Technology has changed our world already, so we should expect that it can happen again. But while we have seen the world transform before, we have seen these transformations play out over the course of generations. What is different now is how very rapid these technological changes have become. In the past, the technologies that our ancestors used in their childhood were still central to their lives in their old age. This has not been the case anymore for recent generations. Instead, it has [become common](https://ourworldindata.org/technology-long-run) that technologies unimaginable in one's youth become ordinary in later life. This is the first reason we might not take the prospect seriously: it is easy to underestimate the speed at which technology can change the world. The second reason why it is difficult to take the possibility of transformative AI – potentially even AI as intelligent as humans – seriously is that it is an idea that we first heard in the cinema. It is not surprising that for many of us, the first reaction to a scenario in which machines have human-like capabilities is the same as if you had asked us to take seriously a future in which vampires, werewolves, or zombies roam the planet.1 But, it is plausible that it is both the stuff of sci-fi fantasy _and_ the central invention that could arrive in our, or our children’s, lifetimes. The third reason why it is difficult to take this prospect seriously is by failing to see that powerful AI could lead to very large changes. This is also understandable. It is difficult to form an idea of a future that is very different from our own time. There are two concepts that I find helpful in imagining a very different future with artificial intelligence. Let’s look at both of them. # How to develop an idea of what the future of artificial intelligence might look like? When thinking about the future of artificial intelligence, I find it helpful to consider two different concepts in particular: human-level AI, and transformative AI.2 The first concept highlights the AI’s capabilities and anchors them to a familiar benchmark, while transformative AI emphasizes the impact that this technology would have on the world. From where we are today, much of this may sound like science fiction. It is therefore worth keeping in mind that the majority of surveyed AI experts [believe](https://ourworldindata.org/ai-timelines) there is a real chance that human-level artificial intelligence will be developed within the next decades, and some believe that it will exist much sooner. ## The advantages and disadvantages of comparing machine and human intelligence One way to think about human-level artificial intelligence is to contrast it with the current state of AI technology. While today’s AI systems often have capabilities similar to a particular, limited part of the human mind, a human-level AI would be a machine that is capable of carrying out the same _range_ of intellectual tasks that we humans are capable of.3 It is a machine that would be “able to learn to do anything that a human can do,” as Norvig and Russell put it in their textbook on AI.4 Taken together, the range of abilities that characterize intelligence gives humans the ability to solve problems and achieve a wide variety of goals. A human-level AI would therefore be a system that could solve all those problems that we humans can solve, and do the tasks that humans do today. Such a machine, or collective of machines, would be able to do the work of a translator, an accountant, an illustrator, a teacher, a therapist, a truck driver, or the work of a trader on the world’s financial markets. Like us, it would also be able to do research and science, and to develop new technologies based on that. The concept of human-level AI has some clear advantages. Using the familiarity of our own intelligence as a reference provides us with some clear guidance on how to imagine the capabilities of this technology. However, it also has clear disadvantages. Anchoring the imagination of future AI systems to the familiar reality of human intelligence carries the risk that it obscures the very real differences between them. Some of these differences are obvious. For example, AI systems will have the immense memory of computer systems, against which our own capacity to store information pales. Another obvious difference is the speed at which a machine can absorb and process information. But information storage and processing speed are not the only differences. The domains in which machines already outperform humans is steadily increasing: in chess, after matching the level of the best human players in the late 90s, AI systems [reached](https://ourworldindata.org/grapher/computer-chess-ability) superhuman levels more than a decade ago. In other games like Go or complex strategy games, this has happened more recently.5 These differences mean that an AI that is at least as good as humans in every domain would overall be much more powerful than the human mind. Even the first “human-level AI” would therefore be quite superhuman in many ways.6 Human intelligence is also a bad metaphor for machine intelligence in other ways. The way we think is often very different from machines, and as a consequence the output of thinking machines can be very alien to us. Most perplexing and most concerning are the strange and unexpected ways in which machine intelligence can fail. The AI-generated image of the horse below provides an example: on the one hand, AIs can do what no human can do – produce an image of anything, in any style (here photorealistic), in mere seconds – but on the other hand it can fail in ways that no human would fail.7 No human would make the mistake of drawing a horse with five legs.8 Imagining a powerful future AI as just another human would therefore likely be a mistake. The differences might be so large that it will be a misnomer to call such systems “human-level.” **AI-generated image of a horse**9 ## Transformative artificial intelligence is defined by the impact this technology would have on the world In contrast, the concept of transformative AI is not based on a comparison with human intelligence. This has the advantage of sidestepping the problems that the comparisons with our own mind bring. But it has the disadvantage that it is harder to imagine what such a system would look like and be capable of. It requires more from us. It requires us to imagine a world with intelligent actors that are potentially very different from ourselves. Transformative AI is not defined by any specific capabilities, but by the real-world impact that the AI would have. To qualify as transformative, researchers think of it as AI that is “powerful enough to bring us into a new, qualitatively different future.”10 In humanity’s history, there have been two cases of such major transformations, the agricultural and the industrial revolutions. Transformative AI becoming a reality would be an event on that scale. Like the arrival of agriculture 10,000 years ago, or the transition from hand- to machine-manufacturing, it would be an event that would change the world for billions of people around the globe and for the entire trajectory of [humanity’s future](https://ourworldindata.org/longtermism). Technologies that fundamentally change how a wide range of goods or services are produced are called ‘general-purpose technologies’. The two previous transformative events were caused by the discovery of two particularly significant general-purpose technologies: the change in food production as humanity transitioned from hunting and gathering to farming, and the rise of machine manufacturing in the industrial revolution. Based on the evidence and arguments presented in [this series](https://ourworldindata.org/artificial-intelligence#research-writing) on AI development, I believe it is plausible that powerful AI could represent the introduction of a similarly significant general-purpose technology. **Timeline of the three transformative events in world history** ## A future of human-level or transformative AI? The two concepts are closely related, but they are not the same. The creation of a human-level AI would certainly have a transformative impact on our world. If the work of most humans could be carried out by an AI, the lives of millions of people would change.11 The opposite, however, is not true: we might see transformative AI without developing human-level AI. Since the human mind is in many ways a poor metaphor for the intelligence of machines, we might plausibly develop transformative AI before we develop human-level AI. Depending on how this goes, this might mean that we will never see any machine intelligence for which human intelligence is a helpful comparison. When and if AI systems might reach either of these levels is of course difficult to predict. In my [companion article](https://ourworldindata.org/ai-timelines) on this question, I give an overview of what researchers in this field currently believe. Many AI experts believe there is a real chance that such systems will be developed within the next decades, and some believe that they will exist much sooner. # What is at stake as artificial intelligence becomes more powerful? All major technological innovations lead to a range of positive and negative consequences. For AI, the spectrum of possible outcomes – from the most negative to the most positive – is extraordinarily wide. That the use of AI technology can cause harm is clear, because it is already happening. AI systems can cause harm when people use them maliciously. For example, when they are used in politically-motivated disinformation campaigns or to enable mass surveillance.12 But AI systems can also cause unintended harm, when they act differently than intended or fail. For example, in the Netherlands the authorities used an AI system which falsely claimed that an estimated 26,000 parents made fraudulent claims for child care benefits. The false allegations led to hardship for many poor families, and also resulted in the resignation of the Dutch government in 2021.13 As AI becomes more powerful, the possible negative impacts could become much larger. Many of these risks have rightfully received public attention: more powerful AI could lead to mass labor displacement, or extreme concentrations of power and wealth. In the hands of autocrats, it could empower totalitarianism through its suitability for mass surveillance and control. The so-called _alignment problem_ of AI is another extreme risk. This is the concern that _nobody_ would be able to control a powerful AI system, even if the AI takes actions that harm us humans, or humanity as a whole. This risk is unfortunately receiving little attention from the wider public, but it is seen as an extremely large risk by many leading AI researchers.14 How could an AI possibly escape human control and end up harming humans? The risk is not that an AI becomes self-aware, develops bad intentions, and “chooses” to do this. The risk is that we try to instruct the AI to pursue some specific goal – even a very worthwhile one – and in the pursuit of that goal it ends up harming humans. It is about unintended consequences. The AI does what we told it to do, but not what we wanted it to do. Can’t we just tell the AI to not do those things? It is definitely possible to build an AI that avoids any particular problem we foresee, but it is hard to foresee _all_ the possible harmful unintended consequences. The alignment problem arises because of “the impossibility of defining true human purposes correctly and completely,” as AI researcher Stuart Russell puts it.15 Can’t we then just switch off the AI? This might also not be possible. That is because a powerful AI would know two things: it faces a risk that humans could turn it off, and it can’t achieve its goals once it has been turned off. As a consequence, the AI will pursue a very fundamental goal of ensuring that it won’t be switched off. This is why, once we realize that an extremely intelligent AI is causing unintended harm in the pursuit of some specific goal, it might not be possible to turn it off or change what the system does.16 This risk – that humanity might not be able to stay in control once AI becomes very powerful, and that this might lead to an extreme catastrophe – has been recognized right from the early days of AI research more than 70 years ago.17 The very rapid development of AI in recent years has made a solution to this problem much more urgent. I have tried to summarize some of the risks of AI, but a short article is not enough space to address all possible questions. Especially on the very worst risks of AI systems, and what we can do now to reduce them, I recommend reading the book [The Alignment Problem](https://brianchristian.org/the-alignment-problem/) by Brian Christian and Benjamin Hilton’s article [‘Preventing an AI-related catastrophe’](https://80000hours.org/problem-profiles/artificial-intelligence). If we manage to avoid these risks, transformative AI could also lead to very positive consequences. Advances in science and technology were crucial to [the many positive developments](https://ourworldindata.org/a-history-of-global-living-conditions-in-5-charts) in humanity’s history. If artificial ingenuity can augment our own, it could help us make progress on the many large problems we face: from cleaner energy, to the replacement of unpleasant work, to much better healthcare. This extremely large contrast between the possible positives and negatives makes clear that the stakes are unusually high with this technology. Reducing the negative risks and solving the alignment problem could mean the difference between a healthy, flourishing, and wealthy future for humanity – and the destruction of the same. # How can we make sure that the development of AI goes well? Making sure that the development of artificial intelligence goes well is not just one of the most crucial questions of our time, but likely one of the most crucial questions in human history. This needs public resources – public funding, public attention, and public engagement. Currently, almost all resources that are dedicated to AI aim to speed up the development of this technology. Efforts that aim to increase the safety of AI systems, on the other hand, do not receive the resources they need. Researcher Toby Ord estimated that in 2020 between $10 to $50 million was spent on work to address the alignment problem.18 Corporate AI investment in the same year was more than 2000-times larger, it [summed up](https://ourworldindata.org/grapher/corporate-investment-in-artificial-intelligence-by-type) to $153 billion. This is not only the case for the AI alignment problem. The work on the entire range of negative social consequences from AI is under-resourced compared to the large investments to increase the power and use of AI systems. It is frustrating and concerning for society as a whole that AI safety work is extremely neglected and that little public funding is dedicated to this crucial field of research. On the other hand, for each _individual_ person this neglect means that they have a good chance to actually make a positive difference, if they dedicate themselves to this problem now. And while the field of AI safety is small, it does provide [good resources](https://80000hours.org/problem-profiles/artificial-intelligence/#what-can-you-do-concretely-to-help) on what you can do concretely if you want to work on this problem. I hope that more people dedicate their individual careers to this cause, but it needs more than individual efforts. A technology that is transforming our society needs to be a central interest of all of us. As a society we have to think more about the societal impact of AI, become knowledgeable about the technology, and understand what is at stake. When our children look back at today, I imagine that they will find it difficult to understand how little attention and resources we dedicated to the development of safe AI. I hope that this changes in the coming years, and that we begin to dedicate more resources to making sure that powerful AI gets developed in a way that benefits us and the next generations. If we fail to develop this broad-based understanding, then it will remain the small elite that finances and builds this technology that will determine how one of the – or plausibly _the_ – most powerful technology in human history will transform our world. If we leave the development of artificial intelligence entirely to private companies, then we are also leaving it up these private companies what our future — the future of humanity — will be. --- The fact that humans are capable of a _range_ of intellectual tasks means that you arrive at different definitions of intelligence depending on which aspect within that range you focus on (the [Wikipedia entry on intelligence](https://en.wikipedia.org/wiki/Intelligence), for example, lists a number of definitions from various researchers and different disciplines). As a consequence there are also various definitions of ‘human-level AI’. There are also several closely related terms: Artificial General Intelligence, High-Level Machine Intelligence, Strong AI, or Full AI are sometimes synonymously used, and sometimes defined in similar, yet different ways. In specific discussions, it is necessary to define this concept more narrowly; for example, in [studies on AI timelines](https://ourworldindata.org/ai-timelines) researchers offer more precise definitions of what human-level AI refers to in their particular study. Toby Ord – [The Precipice](https://theprecipice.com/). He makes this projection in footnote 55 of chapter 2. It is based on the 2017 estimate by Farquhar. Overviews are provided in Stuart Russell (2019) – Human Compatible (especially chapter 5) and Brian Christian’s 2020 book [The Alignment Problem](https://en.wikipedia.org/wiki/The_Alignment_Problem). Christian presents the thinking of many leading AI researchers from the earliest days up to now and presents an excellent overview of this problem. It is also seen as a large risk by some of the leading private firms who work towards powerful AI – see OpenAI's article ""[Our approach to alignment research](https://openai.com/blog/our-approach-to-alignment-research/)"" from August 2022. Both of these concepts are widely used in the scientific literature on artificial intelligence. For example, questions about the timelines for the development of future AI are often framed using these terms. See [my article on this topic](https://ourworldindata.org/ai-timelines). Via [François Chollet](https://fchollet.com/), who published it [here](https://twitter.com/fchollet/status/1573752180720312320?s=46&t=qPwLwDgLdJrLlXxa878BDQ). Based on Chollet’s comments it seems that this image was created by the AI system ‘Stable Diffusion’. In 1950 the computer science pioneer Alan Turing put it like this: _“If a machine can think, it might think more intelligently than we do, and then where should we be? … [T]his new danger is much closer. If it comes at all it will almost certainly be within the next millennium. It is remote but not astronomically remote, and is certainly something which can give us anxiety. It is customary, in a talk or article on this subject, to offer a grain of comfort, in the form of a statement that some particularly human characteristic could never be imitated by a machine. … I cannot offer any such comfort, for I believe that no such bounds can be set.”_ Alan. M. Turing (1950) – [Computing Machinery and Intelligence](https://doi.org/10.1093/mind/LIX.236.433), In Mind, Volume LIX, Issue 236, October 1950, Pages 433–460. Norbert Wiener is another pioneer who saw the alignment problem very early. One way he put it was “If we use, to achieve our purposes, a mechanical agency with whose operation we cannot interfere effectively … we had better be quite sure that the purpose put into the machine is the purpose which we really desire.” quoted from Norbert Wiener (1960) – Some Moral and Technical Consequences of Automation: As machines learn they may develop unforeseen strategies at rates that baffle their programmers. In Science. In 1950 – the same year in which Turing published the cited article – Wiener published his book The Human Use of Human Beings, whose front-cover blurb reads: “The ‘mechanical brain’ and similar machines can destroy human values or enable us to realize them as never before.” This problem becomes even larger when we try to imagine how a future with a human-level AI might play out. Any _particular_ scenario will not only involve the idea that this powerful AI exists, but a whole range of additional assumptions about the future context in which this happens. It is therefore hard to communicate a scenario of a world with human-level AI that does not sound contrived, bizarre or even silly. An overview of how AI systems can fail can be found in [Charles Choi – 7 Revealing Ways AIs Fail](https://spectrum.ieee.org/ai-failures). It is also worth reading through the [AIAAIC Repository](https://www.aiaaic.org/aiaaic-repository/ai-and-algorithmic-incidents-and-controversies) which “details recent incidents and controversies driven by or relating to AI, algorithms, and automation."" See for example the [Wikipedia entry](https://en.wikipedia.org/wiki/Dutch_childcare_benefits_scandal) on the ‘Dutch childcare benefits scandal’ and Melissa Heikkilä (2022) – [‘Dutch scandal serves as a warning for Europe over risks of using algorithms’](https://web.archive.org/web/20221117053636/https://www.politico.eu/article/dutch-scandal-serves-as-a-warning-for-europe-over-risks-of-using-algorithms/), in Politico. The technology can also reinforce discrimination in terms of race and gender. See Brian Christian’s book The Alignment Problem and the [reports of the AI Now Institute](https://ainowinstitute.org/reports.html). The AI system [AlphaGo](https://en.wikipedia.org/wiki/AlphaGo), and its various successors, won against Go masters. The AI system [Pluribus](https://en.wikipedia.org/wiki/Pluribus_(poker_bot)) beat humans at no-limit Texas hold 'em poker. The AI system Cicero can strategize and use human language to win the strategy game Diplomacy. See: Meta Fundamental AI Research Diplomacy Team (FAIR), Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, et al. (2022) – ‘Human-Level Play in the Game of Diplomacy by Combining Language Models with Strategic Reasoning’. In _Science_ 0, no. 0 (22 November 2022): eade9097.[ https://doi.org/10.1126/science.ade9097](https://doi.org/10.1126/science.ade9097). Peter Norvig and Stuart Russell (2021) — Artificial Intelligence: A Modern Approach. Fourth edition. Published by Pearson. A question that follows from this is, why build such a powerful AI in the first place? The incentives are very high. As I emphasize below, this innovation has the potential to lead to very positive developments. In addition to the large social benefits there are also large incentives for those who develop it – the governments that can use it for their goals, the individuals who can use it to become more powerful and wealthy. Additionally, it is of scientific interest and might help us to understand our own mind and intelligence better. And lastly, even if we wanted to stop building powerful AIs, it is likely very hard to actually achieve it. It is very hard to coordinate across the whole world and agree to stop building more advanced AI – countries around the world would have to agree and then find ways to actually implement it. I have taken this example from [AI researcher François Chollet](https://fchollet.com/), who published it [here](https://twitter.com/fchollet/status/1573752180720312320?s=46&t=qPwLwDgLdJrLlXxa878BDQ). This also poses a problem when we evaluate how the intelligence of a machine compares with the intelligence of humans. If intelligence was a general ability, a single capacity, then we could easily compare and evaluate it, but the fact that it is a range of skills makes it much more difficult to compare across machine and human intelligence. Tests for AI systems are therefore comprising a wide range of tasks. See for example Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt (2020) – [Measuring Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300) or the definition of what would qualify as artificial general intelligence in [this Metaculus prediction](https://www.metaculus.com/questions/5121/date-of-artificial-general-intelligence/). Stuart Russell (2019) – Human Compatible This quote is from Holden Karnofsky (2021) – [AI Timelines: Where the Arguments, and the ""Experts,"" Stand](https://www.cold-takes.com/where-ai-forecasting-stands-today/). For Holden Karnofsky’s earlier thinking on this conceptualization of AI see his 2016 article [‘Some Background on Our Views Regarding Advanced Artificial Intelligence’](https://www.openphilanthropy.org/research/some-background-on-our-views-regarding-advanced-artificial-intelligence/#Sec1). Ajeya Cotra, whose research on AI timelines I discuss in other articles of this series, attempts to give a quantitative definition of what would qualify as transformative AI. in her widely cited [report on AI timelines](https://www.alignmentforum.org/posts/KrJfoZzpSDpnrv9va/draft-report-on-ai-timelines) she defines it as a change in software technology that brings the growth rate of gross world product ""to 20%-30% per year"". Several other researchers define TAI in similar terms. Human-level AI is typically defined as a software system that can carry out at least 90% or 99% of all economically relevant tasks that humans carry out. A lower-bar definition would be an AI system that can carry out all those tasks that can currently be done by another human who is working remotely on a computer. On the use of AI in politically-motivated disinformation campaigns see for example John Villasenor (November 2020) – [How to deal with AI-enabled disinformation](https://web.archive.org/web/20220907044354/https://www.brookings.edu/research/how-to-deal-with-ai-enabled-disinformation/). More generally on this topic see Brundage and Avin et al. (2018) – The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, published at [maliciousaireport.com](https://maliciousaireport.com/). A starting point for literature and reporting on mass surveillance by governments is [the relevant Wikipedia entry](https://en.wikipedia.org/wiki/List_of_government_mass_surveillance_projects).",Artificial intelligence is transforming our world — it is on all of us to make sure that it goes well 1rxlMh5sfPnyj_cPFxR8OSUSOfzGaARbn81ge1M1rRJI,coal-power-is-dying-in-western-europe,data-insight,"{""body"": [{""size"": ""narrow"", ""type"": ""image"", ""filename"": ""coal-europe-desktop.png"", ""hasOutline"": false, ""parseErrors"": [], ""smallFilename"": ""coal-europe-mobile.png""}, {""type"": ""text"", ""value"": [{""text"": ""Coal dominated Europe's electricity mix over the 20th century, but it is quickly dying in many countries in the 21st."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows the share of electricity that comes from coal for a range of countries in Western Europe. The data comes from "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ember-climate.org/"", ""children"": [{""text"": ""Ember"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Transitioning away from coal has helped "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/co2?stackMode=relative&time=2000..latest&facet=none&country=GBR~IRL~ESP~PRT~GRC~DNK~NLD~DEU&Gas+or+Warming=CO%E2%82%82&Accounting=Territorial&Fuel+or+Land+Use+Change=All+fossil+emissions&Count=Per+country"", ""children"": [{""text"": ""reduce carbon emissions"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/air-pollution?time=1990..latest&facet=entity&uniformYAxis=0&country=GBR~IRL~ESP~DNK~DEU~GRC~NLD~PRT~ITA&Pollutant=Nitrogen+oxides+%28NOx%29&Sector=From+all+sectors+%28Total%29&Per+capita=false"", ""children"": [{""text"": ""local air pollutants"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/grapher/share-electricity-coal"", ""children"": [{""text"": ""Explore the data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" →"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""data-insight"", ""title"": ""Coal power is dying in Western Europe"", ""authors"": [""Hannah Ritchie""], ""approved-by"": ""Ed"", ""grapher-url"": ""https://ourworldindata.org/grapher/share-electricity-coal""}",1,2024-06-06 10:16:41,2024-06-18 07:00:16,2024-06-10 10:13:20,unlisted,ALBJ4LuDsghb3t7vOkPo2zJSRKcZ-8QO-y594qlKaYp7ODnQQHfSun55BTeeqAoNYENUEucWE8FWWKAKWOw1jA,," Coal dominated Europe's electricity mix over the 20th century, but it is quickly dying in many countries in the 21st. The chart shows the share of electricity that comes from coal for a range of countries in Western Europe. The data comes from [Ember](https://ember-climate.org/). Transitioning away from coal has helped [reduce carbon emissions](https://ourworldindata.org/explorers/co2?stackMode=relative&time=2000..latest&facet=none&country=GBR~IRL~ESP~PRT~GRC~DNK~NLD~DEU&Gas+or+Warming=CO%E2%82%82&Accounting=Territorial&Fuel+or+Land+Use+Change=All+fossil+emissions&Count=Per+country) and [local air pollutants](https://ourworldindata.org/explorers/air-pollution?time=1990..latest&facet=entity&uniformYAxis=0&country=GBR~IRL~ESP~DNK~DEU~GRC~NLD~PRT~ITA&Pollutant=Nitrogen+oxides+%28NOx%29&Sector=From+all+sectors+%28Total%29&Per+capita=false). [Explore the data](https://ourworldindata.org/grapher/share-electricity-coal) →",Coal power is dying in Western Europe 1raCycEPaXLaock-eDMjpF3suxgThwImjR5dNBFhotx0,saving-maternal-lives,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""830 women die from pregnancy-related causes every day."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In 2015, 302,700 women in the world "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/how-many-women-die-in-childbirth"", ""children"": [{""text"": ""died as a result"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of pregnancy or childbirth."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For many aspects of global development, it is true that the world made a lot of progress in the past and we know that we can make a lot of progress still. We’ve shown this before for child mortality and it is true for maternal health too: maternal health is much better than in the past; it’s still awful today; and we can do much better."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the visualization here we compare three scenarios:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""How many mothers would die today if we still had the very poor health of the past?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" Even the countries with the best maternal health today had very high maternal mortality rates in the past. In Sweden and Finland in 1800, for example, around "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/maternal-mortality?tab=chart&country=FIN~SWE"", ""children"": [{""text"": ""900 mothers died"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" for every 100,000 live births [nearly 1-in-100]."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In today’s world where "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/annual-number-of-births-by-world-region"", ""children"": [{""text"": ""140 million"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" women give birth each year this would mean that 1.26 million would die."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""The world today"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": the actual "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/how-many-women-die-in-childbirth"", ""children"": [{""text"": ""number of women who died"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" from pregnancy-related causes in 2015 was 302,700."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""If all regions had the health of today’s best-off countries"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": Today the world region with the lowest maternal mortality is the European Union, where "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/maternal-mortality?tab=chart&country=~European+Union"", ""children"": [{""text"": ""8 women die per 100,000"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" live births. In today’s world where "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/annual-number-of-births-by-world-region"", ""children"": [{""text"": ""140 million"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" women give birth each year, if all countries had this level of maternal mortality, 11,000 would die."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""how-many-women-could-we-save -from-dying-in-pregnancy-or-childbirth.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We can see how much global maternal health has improved: if we still had the living standards of 1800, around 1.26 million women would die from pregnancy every year. 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Maternal mortality is much more common in poorer countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It's unacceptable that a woman in Sierra Leone is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/maternal-mortality?tab=chart&time=latest&country=SLE~SWE~FIN"", ""children"": [{""text"": ""300 to 400 times more likely"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" to die during pregnancy or childbirth than a woman in Sweden or Finland, and we know it is possible to prevent these deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we can make maternal deaths as rare as they are in the healthiest countries in the world we can save almost 300,000 mothers each year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""2b9ac4e34aac9e8bb44f67ddd0f1bd43f9905c79"": {""id"": ""2b9ac4e34aac9e8bb44f67ddd0f1bd43f9905c79"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Here we assume a global maternal mortality rate of 8 per 100,000 live births in 2015. The UN estimates there were around 140 million births in 2015. This works out at around 11,000 maternal deaths [140 million / [8 / 100,000] = 11,000]."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""439fa3a73725e944866ff2f76d68ddf21cd1de01"": {""id"": ""439fa3a73725e944866ff2f76d68ddf21cd1de01"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Here we assume a global maternal mortality rate of 900 per 100,000 live births in 2015. The UN estimates there were around "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/annual-number-of-births-by-world-region"", ""children"": [{""text"": ""140 million births"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in 2015. This works out at around 1.26 million maternal deaths [140 million / [900 / 100,000] = 1.26 million]."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""948f1d1d3dd04bd62cca7206c65dace29e189ab6"": {""id"": ""948f1d1d3dd04bd62cca7206c65dace29e189ab6"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The World Health Organization estimates that in 2015, there were 302,680 maternal deaths globally. Averaged over the year, this would be equal to around 830 maternal deaths per day [302,680 / 365 = 830]."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e8b2c984726f51d97ed777c77ea153f31dd4ce94"": {""id"": ""e8b2c984726f51d97ed777c77ea153f31dd4ce94"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This of course opens up a much larger question of how many deaths are truly preventable, i.e. how low could maternal mortality really go. We base our scenario here on the EU average rate, but we know some countries "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/maternal-mortality?time=1991..2015&country=European%20Union+FIN+SWE"", ""children"": [{""text"": ""have an even lower rate"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "": Finland has a rate of 3 per 100,000 live births, and Sweden has a rate of 4. It’s unclear how attainable this is for all countries, or whether this rate could fall even further."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""If we can make maternal deaths as rare as they are in the healthiest countries, we can save almost 300,000 mothers each year"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""Maternal mortality was much more common in the past. Today, it is much lower — but there are still large inequalities across the world."", ""dateline"": ""September 20, 2019"", ""subtitle"": ""Maternal mortality was much more common in the past. Today, it is much lower — but there are still large inequalities across the world."", ""featured-image"": ""maternal-mortality-saving.png""}",1,2023-06-20 14:39:34,2019-09-20 09:55:38,2024-03-18 15:41:59,listed,ALBJ4LskLVaIRTXvzHKE4cTEVJPjYFAuaPwqrYs7GI0OANNm1AQJYRemr_0ey0l2qaZ_SRLvg6dSButDO4rTUg,,"830 women die from pregnancy-related causes every day.1 In 2015, 302,700 women in the world [died as a result](https://ourworldindata.org/how-many-women-die-in-childbirth) of pregnancy or childbirth. For many aspects of global development, it is true that the world made a lot of progress in the past and we know that we can make a lot of progress still. We’ve shown this before for child mortality and it is true for maternal health too: maternal health is much better than in the past; it’s still awful today; and we can do much better. In the visualization here we compare three scenarios: * **How many mothers would die today if we still had the very poor health of the past?** Even the countries with the best maternal health today had very high maternal mortality rates in the past. In Sweden and Finland in 1800, for example, around [900 mothers died](https://ourworldindata.org/grapher/maternal-mortality?tab=chart&country=FIN~SWE) for every 100,000 live births [nearly 1-in-100].2 In today’s world where [140 million](https://ourworldindata.org/grapher/annual-number-of-births-by-world-region) women give birth each year this would mean that 1.26 million would die. * **The world today**: the actual [number of women who died](https://ourworldindata.org/how-many-women-die-in-childbirth) from pregnancy-related causes in 2015 was 302,700. * **If all regions had the health of today’s best-off countries**: Today the world region with the lowest maternal mortality is the European Union, where [8 women die per 100,000](https://ourworldindata.org/grapher/maternal-mortality?tab=chart&country=~European+Union) live births. In today’s world where [140 million](https://ourworldindata.org/grapher/annual-number-of-births-by-world-region) women give birth each year, if all countries had this level of maternal mortality, 11,000 would die.3 We can see how much global maternal health has improved: if we still had the living standards of 1800, around 1.26 million women would die from pregnancy every year. Almost one million more women would die each year. But we also see how far we could go. If all regions achieved the healthcare and living standards of the EU very few women would die. Almost 300,000 fewer deaths; a reduction of over 95%. If we think of it in this way, almost all of the world’s maternal deaths are preventable with adequate [maternal care](https://ourworldindata.org/grapher/share-of-mothers-receiving-at-least-one-antenatal-visit-during-pregnancy), [safe deliveries](https://ourworldindata.org/maternal-mortality#what-share-of-births-are-attended-by-health-staff), good [nutrition](https://ourworldindata.org/micronutrient-deficiency), and [hygiene and sanitation](https://ourworldindata.org/explorers/water-and-sanitation).4 This is also the [message](https://www.who.int/news-room/fact-sheets/detail/maternal-mortality) of the World Health Organization: “Every day, approximately 830 women die from preventable causes related to pregnancy and childbirth."" That most of the world’s maternal deaths could be prevented also becomes clear when we consider that 95% occur [in low and lower-middle-income](https://ourworldindata.org/grapher/maternal-deaths-by-income-group) countries. Maternal mortality is much more common in poorer countries. It's unacceptable that a woman in Sierra Leone is [300 to 400 times more likely](https://ourworldindata.org/grapher/maternal-mortality?tab=chart&time=latest&country=SLE~SWE~FIN) to die during pregnancy or childbirth than a woman in Sweden or Finland, and we know it is possible to prevent these deaths. If we can make maternal deaths as rare as they are in the healthiest countries in the world we can save almost 300,000 mothers each year. The World Health Organization estimates that in 2015, there were 302,680 maternal deaths globally. Averaged over the year, this would be equal to around 830 maternal deaths per day [302,680 / 365 = 830]. Here we assume a global maternal mortality rate of 900 per 100,000 live births in 2015. The UN estimates there were around [140 million births](https://ourworldindata.org/grapher/annual-number-of-births-by-world-region) in 2015. This works out at around 1.26 million maternal deaths [140 million / [900 / 100,000] = 1.26 million]. Here we assume a global maternal mortality rate of 8 per 100,000 live births in 2015. The UN estimates there were around 140 million births in 2015. This works out at around 11,000 maternal deaths [140 million / [8 / 100,000] = 11,000]. This of course opens up a much larger question of how many deaths are truly preventable, i.e. how low could maternal mortality really go. We base our scenario here on the EU average rate, but we know some countries [have an even lower rate](https://ourworldindata.org/grapher/maternal-mortality?time=1991..2015&country=European%20Union+FIN+SWE): Finland has a rate of 3 per 100,000 live births, and Sweden has a rate of 4. It’s unclear how attainable this is for all countries, or whether this rate could fall even further.","If we can make maternal deaths as rare as they are in the healthiest countries, we can save almost 300,000 mothers each year" 1rEhPkFIeAvIQeOHj69HHlHLd1MygsBX7jb79MnpFJmI,owid-data-collection-inequality-and-poverty,article,"{""toc"": [{""slug"": ""poverty-indicators"", ""text"": ""Poverty indicators:"", ""title"": ""Poverty indicators:"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""inequality-indicators"", ""text"": ""Inequality indicators:"", ""title"": ""Inequality indicators:"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""incomes-across-the-distribution"", ""text"": ""Incomes across the distribution:"", ""title"": ""Incomes across the distribution:"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""world-bank-pip"", ""text"": ""World Bank PIP"", ""title"": ""World Bank PIP"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""luxembourg-income-study"", ""text"": ""Luxembourg Income Study"", ""title"": ""Luxembourg Income Study"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""world-inequality-database"", ""text"": ""World Inequality Database"", ""title"": ""World Inequality Database"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""At Our World in Data, we are building an extensive dataset of inequality and poverty indicators, pulling together multiple sources to provide as comprehensive a view as possible."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To make it easier to navigate this wide range of data, below we provide links to a set of Data Explorers that allow you to explore a very detailed range of indicators and compare data across sources. 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Information about the definitions and methods behind the data from each of these sources is provided at the bottom of this page."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The detailed data contained in the explorers collected below is intended for experts or researchers who are already quite familiar with the measures and concepts involved. Users with a more general interest are likely to benefit more from the Data Explorers shown in our topic pages on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/economic-inequality#explore-data-on-economic-inequality"", ""children"": [{""text"": ""Inequality"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/poverty#explore-data-on-poverty"", ""children"": [{""text"": ""Poverty"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". These provide an overview of the key indicators from this collection."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""Poverty indicators:"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/explorers/poverty-wb"", ""children"": [{""text"": ""Poverty Data Explorer: World Bank PIP data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/explorers/poverty-explorer-2011-vs-2017-ppp"", ""children"": [{""text"": ""Poverty Data Explorer: World Bank PIP data - 2011 vs. 2017 prices"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": 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""span-link""}, {""text"": "" is an interactive website and API that the World Bank uses to share the estimates it produces in its activities of monitoring global poverty, inequality and shared prosperity."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Here we summarize some key aspects of the definitions and methods used in the platform’s data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For a more detailed discussion, see the World Bank PIP "", ""spanType"": ""span-simple-text""}, {""url"": ""https://datanalytics.worldbank.org/PIP-Methodology/"", ""children"": [{""text"": ""methodology document"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Welfare measure"", ""spanType"": ""span-simple-text""}], 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The World Bank data shown above is all measured in 2017 international dollars."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Primary data sources"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The World Bank PIP estimates are derived from a large collection of household surveys."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In addition to the difference between income and consumption data mentioned above, there are several other ways in which comparability across household surveys can be limited, both across countries and over time. In collating this survey data, the World Bank takes various steps to harmonize it where possible, but comparability issues remain. 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In these cases, their estimates are based on ‘grouped data’ – tabulations of the average incomes of richer and poorer segments of the population. To produce its poverty and inequality estimates, the World Bank fits a distribution to this grouped data, by making certain assumptions about the shape of that distribution."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we discuss more in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://docs.google.com/document/d/1M2S6EP-CAZL1Oi4szQ5k0BiuPUdaUDbAXTCjnwsQK3o/edit"", ""children"": [{""text"": ""this article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", a well-known issue with household survey data is that the incomes of the richest are often poorly captured. This can lead to underestimates of inequality. Statistical offices organizing household surveys may adopt various strategies to minimize this, but this varies across countries and over time. In processing this survey data, the World Bank takes no steps to further correct the problem of missing top incomes. 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Our definitions align with those used in LIS’ "", ""spanType"": ""span-simple-text""}, {""url"": ""https://dart.lisdatacenter.org/dart"", ""children"": [{""text"": ""DART"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" data visualization tool and their "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.lisdatacenter.org/lis-ikf-webapp/app/search-ikf-figures"", ""children"": [{""text"": ""Key Figures"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" estimates, described "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.lisdatacenter.org/data-access/dart/methodology/"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As a measure of after-tax income, we use their measure of ‘disposable household income’. This refers to “cash and non-cash income from labor, income from capital, income from pensions (including private and public pensions) and non-pension public social benefits stemming from insurance, universal or assistance schemes (including in-kind social assistance transfers), as well as cash and non-cash private transfers, after deduction of the amount of income taxes and social contributions paid”."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As a measure of before-tax income we use their measure of ‘market income’. This refers to “income received by the households before public redistribution takes place; it includes cash and non-cash income from labor, income from capital, income from private pensions, as well as cash and non-cash private transfers, before deduction of income taxes and social contributions paid”."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In order to make absolute comparisons of standards of living across countries and over time, we convert the data – measured in local currencies at current prices – into constant international dollars. The LIS data shown above is all measured in 2017 international dollars."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Primary data sources"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The LIS data is a ‘harmonized’ collection of household survey data. This means that the raw data produced by different statistical offices has been reorganized to align the concepts behind the data as much as possible."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The underlying survey data is, however, very heterogeneous, and not all comparability issues can be resolved. To communicate these issues, LIS has released the very helpful "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.lisdatacenter.org/our-data/survey-comparability-tool/"", ""children"": [{""text"": ""Compare.it"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" tool, which provides very detailed comparability notes for each country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Accounting for resource sharing within households"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The surveys LIS collates are conducted at the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""household"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" level. The income or consumption reported in the survey data sums across all members of the household."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""From the LIS microdata, we calculate poverty and inequality indicators based on two approaches for accounting for resource sharing within households:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Per capita"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "" income:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" here, each member of the household (both adults and children) is attributed an income equal to total household income divided by the number of household members."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""children"": [{""text"": ""Equivalized"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "" income:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" on this basis, incomes are adjusted to account for the fact that people in the same household can share costs like rent and heating. We use the ‘square root’ equivalence scale to make this adjustment: each household member (both adults and children) is attributed an income equal to the total household income divided by the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""square root"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of the number of household members."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Methods and assumptions applied"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""LIS provides very detailed documentation of how they process the original survey data on two dedicated metadata platforms: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.lisdatacenter.org/frontend#/home"", ""children"": [{""text"": ""METIS"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.lisdatacenter.org/our-data/survey-comparability-tool/"", ""children"": [{""text"": ""Compare.it"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" tool."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In calculating inequality and poverty estimates from the LIS microdata, we apply the same ‘top-’ and ‘bottom-coding’ procedure as used by LIS to calculate their summary statistics presented on their website – both the LIS ‘Key Figures’ and the DART interactive visualization tool. This is done to remove extreme values from the raw survey data and to make the data across countries more comparable. For a more detailed discussion of why this is done, the methods used, and how it impacts resulting estimates, see the helpful "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.lisdatacenter.org/newsletter/nl-2020-15-im-4/"", ""children"": [{""text"": ""explainer from LIS"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A well-known issue with household survey data is that the incomes of the richest are often poorly captured. This can lead to underestimates of inequality. Statistical offices organizing household surveys may adopt various strategies to minimize this, but this varies across countries and over time. In processing this survey data, the Luxembourg Income Study itself takes no steps to try to further correct the problem of missing top incomes. As such, inequality indicators based on this data – particularly those sensitive to the top, such as top income shares – may, in many cases, underestimate inequality."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""World Inequality Database"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""url"": ""https://wid.world/"", ""children"": [{""text"": ""World Inequality Database"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (WID) is an extensive database on the distribution of income and wealth maintained by the World Inequality Lab (WIL), located at the Paris School of Economics (PSE). The database is the result of a collaborative effort involving many researchers worldwide."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Primary data sources, welfare measures, and methods"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A distinctive feature of the WID data is the broad range of raw data sources it draws on."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most sources of inequality data draw exclusively on household surveys. As we discuss more in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://docs.google.com/document/d/1M2S6EP-CAZL1Oi4szQ5k0BiuPUdaUDbAXTCjnwsQK3o/edit"", ""children"": [{""text"": ""this article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", A downside of this approach is that the incomes of the richest are often poorly captured in survey data. This can lead to underestimates of inequality, particularly for measures focused on the top of the distribution, such as the share of income of the richest 1%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The WID database emerged from the substantial literature on ‘top incomes’ that sought to address this shortcoming of survey data by relying instead on data obtained from tax records, or tabulations of such data released by tax authorities. The use of such tax data often limited what concept of income could be analyzed. Inequality estimates produced within the ‘top incomes’ literature have generally been measured in terms of before-tax income, with the exact definitions varying due to differences in the tax system across countries or over time. Since, in many places or periods, it is only a relatively small population of high-earning individuals that file tax returns, the use of tax data also required a focus on the top of the income distribution – for example, on the share of income received by the top 1 or 10%."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These methodologies have continued to develop, and the WID database has established a more standardized set of methods. Within this approach, tax data is combined with data from household surveys and national accounts to produce "", ""spanType"": ""span-simple-text""}, {""url"": ""https://wid.world/document/distributional-national-accounts-guidelines-2020-concepts-and-methods-used-in-the-world-inequality-database/"", ""children"": [{""text"": ""Distributional National Accounts"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (DINA). The survey and tax data are used to understand how different income components are distributed across the population. This is then scaled to match the aggregates given in national accounts. This allows WID to account for income missing from tax and survey data – notably, the profits of firms that are not distributed to shareholders – and to provide a more consistent basis for international comparisons. Using this approach, inequality estimates can be produced not only for top pre-tax income shares but across the whole distribution, according to a range of different income concepts – including after-tax income."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another general difference between WID and other main data sources on inequality is that its methodological approach is aimed more at describing the distribution of earnings itself, rather than the distribution of welfare this income generates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Because of these differences in the goals and raw data sources used by WID, some definitions and methods they use differ from those of other providers of inequality data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For example, the scaling of incomes to match national accounts aggregates means that the absolute income levels reported in WID data are "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/gdocs/1rEhPkFIeAvIQeOHj69HHlHLd1MygsBX7jb79MnpFJmI/preview#details-on-the-methods-used-by-each-source-included-in-this-article"", ""children"": [{""text"": ""much higher across the distribution"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", compared to other sources based on survey data alone. This reflects the very different income concepts being measured.The after-tax income concept used in the data presented above includes, for example, the addition and also the value of public services like schools, hospitals or the armed forces."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another example is WID’s unusual approach to accounting for pensions in before- and after-tax income concepts. Typically, public pensions are considered part of the redistribution achieved by governments; private pensions are not."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The before-tax income concept we present in the data above is described by WID as ‘pre-tax, post-replacement’ income. It measures income after the operation of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""both"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" public and private pension systems. This unusual definition of income is used to yield more consistent comparisons across countries, less impacted by the different ways countries organize pensions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is worth pointing out that, at its fullest, the Distributional National Accounts approach is very data intensive. At the same time, WID aims to provide wide coverage across countries and time. As such, for many countries and periods, the raw data required to produce DINAs according to the full methodology is often lacking. Depending on data availability, the way the general approach is implemented in particular countries and periods varies considerably. To document the different assumptions and methods applied in particular cases, WID provides notes on its "", ""spanType"": ""span-simple-text""}, {""url"": ""https://wid.world/methodology/#library-browse-by-country"", ""children"": [{""text"": ""methodology by country"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Accounting for resource sharing within households"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The WID data we gather above counts adult individuals only. For example, the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/inequality-wid?facet=none&country=CHL~ZAF~USA~FRA~CHN~BRA&Indicator=Share+of+the+richest+1%25&Income+measure=Before+tax"", ""children"": [{""text"": ""income share of the richest 1%"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" means the income received by the richest 1% of adult individuals as a share of income received by all adult individuals."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The focus on adult individuals means that this data does not make any adjustment to account for the number of children that a person’s income needs to support. This is another reason why the absolute income levels reported in WID data are "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/gdocs/1rEhPkFIeAvIQeOHj69HHlHLd1MygsBX7jb79MnpFJmI/preview#details-on-the-methods-used-by-each-source-included-in-this-article"", ""children"": [{""text"": ""much higher"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" than in other sources."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The underlying raw data partly drive this choice: in some cases, the reliance on tax data can make it difficult to identify whole households. But it also reflects the goal of the WID data: to measure the distribution of what people earn, rather than the welfare this income generates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We use the ‘equal split’ data from WID. On this basis, income earned by adults living together is summed up and then allocated equally between them. Depending on the country, this income sharing is done either amongst couples or else amongst all adults living in a household (e.g., both parents and grandparents in multi-generational households). Income is un-equivalized in this data: it makes no adjustment to account for any sharing of costs that multi-person households may benefit from with respect to single-person households."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""0f4ebde8a0bc36e4af667dd61a6652ab388277d8"": {""id"": ""0f4ebde8a0bc36e4af667dd61a6652ab388277d8"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This binary distinction is a simplification. It does not capture well the many different types of pensions: for example, whether an employer contributes or not, whether it is organized collectively or individually, and whether it is mandatory or not. The exact treatment of each kind can vary somewhat between data sources."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3e0260c7473b061d923112d36b616fa745b47143"": {""id"": ""3e0260c7473b061d923112d36b616fa745b47143"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""It’s important to realize that ‘monetary’ poverty also captures sources of income that do not involve money. For example, standard monetary measures of poverty account for the value of food that subsistence farmers grow for their own consumption."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""5e5866f8653b51407daed533f754f1bf96f548b9"": {""id"": ""5e5866f8653b51407daed533f754f1bf96f548b9"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""WID also produces estimates of after-tax income in which collective expenditures and in-kind transfers like these are not added to individuals’ income, though these are available with lower coverage."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9e7efc2a8f0bf1bfad211177ae02d7746deb7704"": {""id"": ""9e7efc2a8f0bf1bfad211177ae02d7746deb7704"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""We calculate before-tax (‘market’) income as the sum of income from labor and capital (LIS code: ‘hifactor’), private cash transfers and in-kind goods and services provided (hiprivate), and private pensions (hi33). We only calculate before-tax income for surveys in which the required data on tax and contributions are fully captured (including where it has been imputed)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a8854a4253ab93ada146ec2625cf0143218f8912"": {""id"": ""a8854a4253ab93ada146ec2625cf0143218f8912"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Anthony Atkinson, Thomas Piketty, and Emmanuel Saez (2011) provide a good overview of initial research efforts in this field. Atkinson, Anthony B., Thomas Piketty, and Emmanuel Saez. ‘Top Incomes in the Long Run of History’. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Journal of Economic Literature"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" 49, no. 1 (March 2011): 3–71."", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1257/jel.49.1.3"", ""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""Available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://eml.berkeley.edu/~saez/atkinson-piketty-saezJEL10.pdf"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""eb8bf95e6676bfce2f55d1e55ba9645c92fc2956"": {""id"": ""eb8bf95e6676bfce2f55d1e55ba9645c92fc2956"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""As noted in the World Bank PIP "", ""spanType"": ""span-simple-text""}, {""url"": ""https://datanalytics.worldbank.org/PIP-Methodology/"", ""children"": [{""text"": ""methodology documentation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""OWID Data Collection: Inequality and Poverty"", ""authors"": [""Joe Hasell"", ""Pablo Arriagada""], ""excerpt"": ""Explore a wide range of indicators on inequality and poverty and compare sources."", ""subtitle"": ""Explore a wide range of indicators on inequality and poverty and compare sources."", ""featured-image"": ""owid-data-collection-inequality-and-poverty-featured-image2.png""}",1,2023-06-27 21:06:25,2023-07-06 15:03:13,2024-02-02 19:18:21,listed,ALBJ4Lsksiwgi7dsjLLNVO1TYlW-fQZvjyiMckvxCHyosCPRBZGcaULAcI2Gzx1H7qEWDg7rHhD31MqT1JlJcQ,,"At Our World in Data, we are building an extensive dataset of inequality and poverty indicators, pulling together multiple sources to provide as comprehensive a view as possible. To make it easier to navigate this wide range of data, below we provide links to a set of Data Explorers that allow you to explore a very detailed range of indicators and compare data across sources. The explorers draw from three prominent sources, each offering a global perspective on poverty and inequality: the [World Bank Poverty and Inequality Platform](https://pip.worldbank.org/home), the [Luxembourg Income Study](https://www.lisdatacenter.org/), and the [World Inequality Database](https://wid.world/). Information about the definitions and methods behind the data from each of these sources is provided at the bottom of this page. The detailed data contained in the explorers collected below is intended for experts or researchers who are already quite familiar with the measures and concepts involved. Users with a more general interest are likely to benefit more from the Data Explorers shown in our topic pages on [Inequality](https://ourworldindata.org/economic-inequality#explore-data-on-economic-inequality) and [Poverty](https://ourworldindata.org/poverty#explore-data-on-poverty). These provide an overview of the key indicators from this collection. --- ## Poverty indicators: * [Poverty Data Explorer: World Bank PIP data](https://ourworldindata.org/explorers/poverty-wb) * [Poverty Data Explorer: World Bank PIP data - 2011 vs. 2017 prices](https://ourworldindata.org/explorers/poverty-explorer-2011-vs-2017-ppp) * [Poverty Data Explorer: Luxembourg Income Study data](https://ourworldindata.org/explorers/poverty-lis) * [Poverty Data Explorer: Compare World Bank and LIS data](https://ourworldindata.org/explorers/poverty-comparison) ## Inequality indicators: * [Inequality Data Explorer: World Bank PIP data](https://ourworldindata.org/explorers/inequality-wb) * [Inequality Data Explorer: Luxembourg Income Study data](https://ourworldindata.org/explorers/inequality-lis) * [Inequality Data Explorer: World Inequality Database data](https://ourworldindata.org/explorers/inequality-wid) * [Inequality Data Explorer: Compare World Bank, WID and LIS data](https://ourworldindata.org/explorers/inequality-comparison) ## Incomes across the distribution: * [Incomes Across the Distribution Data Explorer: World Bank data](https://ourworldindata.org/explorers/incomes-across-distribution-wb) * [Incomes Across the Distribution Data Explorer: Luxembourg Income Study data](https://ourworldindata.org/explorers/incomes-across-distribution-lis) * [Incomes Across the Distribution Data Explorer: World Inequality Database data](https://ourworldindata.org/explorers/incomes-across-distribution-wid) * [Incomes Across the Distribution Data Explorer: Compare World Bank, WID and LIS data](https://ourworldindata.org/explorers/incomes-across-distribution-comparison) ### Download the data All the data contained in this article is available to download as a single file from GitHub: ### Download our poverty and inequality data on GitHub A wide range of poverty and inequality indicators collated from the World Bank Poverty and Inequality Platform, Luxembourg Income Study and World Inequality Database https://ourworldindata.org --- # Details on the methods used by each data source ## World Bank PIP The [World Bank Poverty and Inequality Platform](https://pip.worldbank.org/home) is an interactive website and API that the World Bank uses to share the estimates it produces in its activities of monitoring global poverty, inequality and shared prosperity. Here we summarize some key aspects of the definitions and methods used in the platform’s data. For a more detailed discussion, see the World Bank PIP [methodology document](https://datanalytics.worldbank.org/PIP-Methodology/). **Welfare measure** The data collated in the PIP data relates to a mix of after-tax income and consumption, depending on the country and year. While in most high-income countries, the data relates to after-tax income, in poorer countries, the data tends to relate to consumption. The World Bank pools the data to get a global picture of poverty and inequality. But it’s essential to remember that, depending on the country or year, somewhat different things are being measured. In the Data Explorers of the World Bank data above, we provide the option of plotting these after-tax income and consumption data points separately. The World Bank PIP data provides no indicators in terms of before-tax income. To make absolute comparisons of living standards across countries and over time, the World Bank converts the survey data – measured in local currencies at current prices – into constant international dollars. The World Bank data shown above is all measured in 2017 international dollars. **Primary data sources** The World Bank PIP estimates are derived from a large collection of household surveys. In addition to the difference between income and consumption data mentioned above, there are several other ways in which comparability across household surveys can be limited, both across countries and over time. In collating this survey data, the World Bank takes various steps to harmonize it where possible, but comparability issues remain. The PIP [Methodology Handbook](https://datanalytics.worldbank.org/PIP-Methodology/) provides a good summary of the comparability and data quality issues affecting this data and how it tries to address them. To help communicate this limitation of the data, the World Bank produces a companion indicator that groups data points within each country into ‘spells’. The surveys underlying the data within a given spell for a particular country are considered by World Bank researchers to be more comparable. In the Data Explorers of the World Bank data above, we provide the option of plotting the data with the breaks between spells shown. **Accounting for resource sharing within households** The surveys on which the World Bank estimates are conducted at the _household_ level. The income or consumption reported in the survey data sums across all household members. In calculating its poverty and inequality indicators, the World Bank uses _per capita_ income or consumption: it attributes an equal share of household income to each member – adults and children. **Methods and assumptions applied** In some cases, the raw household survey data itself is not made available to the World Bank. In these cases, their estimates are based on ‘grouped data’ – tabulations of the average incomes of richer and poorer segments of the population. To produce its poverty and inequality estimates, the World Bank fits a distribution to this grouped data, by making certain assumptions about the shape of that distribution. As we discuss more in [this article](https://docs.google.com/document/d/1M2S6EP-CAZL1Oi4szQ5k0BiuPUdaUDbAXTCjnwsQK3o/edit), a well-known issue with household survey data is that the incomes of the richest are often poorly captured. This can lead to underestimates of inequality. Statistical offices organizing household surveys may adopt various strategies to minimize this, but this varies across countries and over time. In processing this survey data, the World Bank takes no steps to further correct the problem of missing top incomes. As such, inequality indicators based on this data – particularly those sensitive to the top, such as top income shares – may, in many cases, underestimate inequality. ## Luxembourg Income Study The [Luxembourg Income Study](https://www.lisdatacenter.org/) (LIS) is a collection of household survey data, in which the raw data produced by different statistical offices is reorganized to make them more comparable. One particular benefit of LIS is that it provides access to the ‘microdata’ – the data for particular individuals and households participating in the survey. From this microdata, Our World in Data calculates a range of poverty and inequality indicators. **Welfare measure** We calculate poverty and inequality indicators for both after-tax and before-tax income. Our definitions align with those used in LIS’ [DART](https://dart.lisdatacenter.org/dart) data visualization tool and their [Key Figures](https://www.lisdatacenter.org/lis-ikf-webapp/app/search-ikf-figures) estimates, described [here](https://www.lisdatacenter.org/data-access/dart/methodology/). As a measure of after-tax income, we use their measure of ‘disposable household income’. This refers to “cash and non-cash income from labor, income from capital, income from pensions (including private and public pensions) and non-pension public social benefits stemming from insurance, universal or assistance schemes (including in-kind social assistance transfers), as well as cash and non-cash private transfers, after deduction of the amount of income taxes and social contributions paid”. As a measure of before-tax income we use their measure of ‘market income’. This refers to “income received by the households before public redistribution takes place; it includes cash and non-cash income from labor, income from capital, income from private pensions, as well as cash and non-cash private transfers, before deduction of income taxes and social contributions paid”.1 In order to make absolute comparisons of standards of living across countries and over time, we convert the data – measured in local currencies at current prices – into constant international dollars. The LIS data shown above is all measured in 2017 international dollars. **Primary data sources** The LIS data is a ‘harmonized’ collection of household survey data. This means that the raw data produced by different statistical offices has been reorganized to align the concepts behind the data as much as possible. The underlying survey data is, however, very heterogeneous, and not all comparability issues can be resolved. To communicate these issues, LIS has released the very helpful [Compare.it](https://www.lisdatacenter.org/our-data/survey-comparability-tool/) tool, which provides very detailed comparability notes for each country. **Accounting for resource sharing within households** The surveys LIS collates are conducted at the _household_ level. The income or consumption reported in the survey data sums across all members of the household. From the LIS microdata, we calculate poverty and inequality indicators based on two approaches for accounting for resource sharing within households: * **_Per capita_**** income:** here, each member of the household (both adults and children) is attributed an income equal to total household income divided by the number of household members. * **_Equivalized_**** income:** on this basis, incomes are adjusted to account for the fact that people in the same household can share costs like rent and heating. We use the ‘square root’ equivalence scale to make this adjustment: each household member (both adults and children) is attributed an income equal to the total household income divided by the _square root_ of the number of household members. **Methods and assumptions applied** LIS provides very detailed documentation of how they process the original survey data on two dedicated metadata platforms: [METIS](https://www.lisdatacenter.org/frontend#/home) and the [Compare.it](https://www.lisdatacenter.org/our-data/survey-comparability-tool/) tool. In calculating inequality and poverty estimates from the LIS microdata, we apply the same ‘top-’ and ‘bottom-coding’ procedure as used by LIS to calculate their summary statistics presented on their website – both the LIS ‘Key Figures’ and the DART interactive visualization tool. This is done to remove extreme values from the raw survey data and to make the data across countries more comparable. For a more detailed discussion of why this is done, the methods used, and how it impacts resulting estimates, see the helpful [explainer from LIS](https://www.lisdatacenter.org/newsletter/nl-2020-15-im-4/). A well-known issue with household survey data is that the incomes of the richest are often poorly captured. This can lead to underestimates of inequality. Statistical offices organizing household surveys may adopt various strategies to minimize this, but this varies across countries and over time. In processing this survey data, the Luxembourg Income Study itself takes no steps to try to further correct the problem of missing top incomes. As such, inequality indicators based on this data – particularly those sensitive to the top, such as top income shares – may, in many cases, underestimate inequality. ## World Inequality Database The [World Inequality Database](https://wid.world/) (WID) is an extensive database on the distribution of income and wealth maintained by the World Inequality Lab (WIL), located at the Paris School of Economics (PSE). The database is the result of a collaborative effort involving many researchers worldwide. **Primary data sources, welfare measures, and methods** A distinctive feature of the WID data is the broad range of raw data sources it draws on. Most sources of inequality data draw exclusively on household surveys. As we discuss more in [this article](https://docs.google.com/document/d/1M2S6EP-CAZL1Oi4szQ5k0BiuPUdaUDbAXTCjnwsQK3o/edit), A downside of this approach is that the incomes of the richest are often poorly captured in survey data. This can lead to underestimates of inequality, particularly for measures focused on the top of the distribution, such as the share of income of the richest 1%. The WID database emerged from the substantial literature on ‘top incomes’ that sought to address this shortcoming of survey data by relying instead on data obtained from tax records, or tabulations of such data released by tax authorities. The use of such tax data often limited what concept of income could be analyzed. Inequality estimates produced within the ‘top incomes’ literature have generally been measured in terms of before-tax income, with the exact definitions varying due to differences in the tax system across countries or over time. Since, in many places or periods, it is only a relatively small population of high-earning individuals that file tax returns, the use of tax data also required a focus on the top of the income distribution – for example, on the share of income received by the top 1 or 10%.2 These methodologies have continued to develop, and the WID database has established a more standardized set of methods. Within this approach, tax data is combined with data from household surveys and national accounts to produce [Distributional National Accounts](https://wid.world/document/distributional-national-accounts-guidelines-2020-concepts-and-methods-used-in-the-world-inequality-database/) (DINA). The survey and tax data are used to understand how different income components are distributed across the population. This is then scaled to match the aggregates given in national accounts. This allows WID to account for income missing from tax and survey data – notably, the profits of firms that are not distributed to shareholders – and to provide a more consistent basis for international comparisons. Using this approach, inequality estimates can be produced not only for top pre-tax income shares but across the whole distribution, according to a range of different income concepts – including after-tax income. Another general difference between WID and other main data sources on inequality is that its methodological approach is aimed more at describing the distribution of earnings itself, rather than the distribution of welfare this income generates. Because of these differences in the goals and raw data sources used by WID, some definitions and methods they use differ from those of other providers of inequality data. For example, the scaling of incomes to match national accounts aggregates means that the absolute income levels reported in WID data are [much higher across the distribution](https://ourworldindata.org/gdocs/1rEhPkFIeAvIQeOHj69HHlHLd1MygsBX7jb79MnpFJmI/preview#details-on-the-methods-used-by-each-source-included-in-this-article), compared to other sources based on survey data alone. This reflects the very different income concepts being measured.The after-tax income concept used in the data presented above includes, for example, the addition and also the value of public services like schools, hospitals or the armed forces.3 Another example is WID’s unusual approach to accounting for pensions in before- and after-tax income concepts. Typically, public pensions are considered part of the redistribution achieved by governments; private pensions are not.4 The before-tax income concept we present in the data above is described by WID as ‘pre-tax, post-replacement’ income. It measures income after the operation of _both_ public and private pension systems. This unusual definition of income is used to yield more consistent comparisons across countries, less impacted by the different ways countries organize pensions. It is worth pointing out that, at its fullest, the Distributional National Accounts approach is very data intensive. At the same time, WID aims to provide wide coverage across countries and time. As such, for many countries and periods, the raw data required to produce DINAs according to the full methodology is often lacking. Depending on data availability, the way the general approach is implemented in particular countries and periods varies considerably. To document the different assumptions and methods applied in particular cases, WID provides notes on its [methodology by country](https://wid.world/methodology/#library-browse-by-country). **Accounting for resource sharing within households** The WID data we gather above counts adult individuals only. For example, the [income share of the richest 1%](https://ourworldindata.org/explorers/wid-inequality?time=2019&facet=none&country=CHL~ZAF~USA~FRA~CHN~BRA&Indicator=Share+of+the+richest+1%25&Income+measure=Before+tax) means the income received by the richest 1% of adult individuals as a share of income received by all adult individuals. The focus on adult individuals means that this data does not make any adjustment to account for the number of children that a person’s income needs to support. This is another reason why the absolute income levels reported in WID data are [much higher](https://ourworldindata.org/gdocs/1rEhPkFIeAvIQeOHj69HHlHLd1MygsBX7jb79MnpFJmI/preview#details-on-the-methods-used-by-each-source-included-in-this-article) than in other sources. The underlying raw data partly drive this choice: in some cases, the reliance on tax data can make it difficult to identify whole households. But it also reflects the goal of the WID data: to measure the distribution of what people earn, rather than the welfare this income generates. We use the ‘equal split’ data from WID. On this basis, income earned by adults living together is summed up and then allocated equally between them. Depending on the country, this income sharing is done either amongst couples or else amongst all adults living in a household (e.g., both parents and grandparents in multi-generational households). Income is un-equivalized in this data: it makes no adjustment to account for any sharing of costs that multi-person households may benefit from with respect to single-person households. We calculate before-tax (‘market’) income as the sum of income from labor and capital (LIS code: ‘hifactor’), private cash transfers and in-kind goods and services provided (hiprivate), and private pensions (hi33). We only calculate before-tax income for surveys in which the required data on tax and contributions are fully captured (including where it has been imputed). Anthony Atkinson, Thomas Piketty, and Emmanuel Saez (2011) provide a good overview of initial research efforts in this field. Atkinson, Anthony B., Thomas Piketty, and Emmanuel Saez. ‘Top Incomes in the Long Run of History’. _Journal of Economic Literature_ 49, no. 1 (March 2011): 3–71.[ ](https://doi.org/10.1257/jel.49.1.3)Available [here](https://eml.berkeley.edu/~saez/atkinson-piketty-saezJEL10.pdf). WID also produces estimates of after-tax income in which collective expenditures and in-kind transfers like these are not added to individuals’ income, though these are available with lower coverage. This binary distinction is a simplification. It does not capture well the many different types of pensions: for example, whether an employer contributes or not, whether it is organized collectively or individually, and whether it is mandatory or not. The exact treatment of each kind can vary somewhat between data sources. As noted in the World Bank PIP [methodology documentation](https://datanalytics.worldbank.org/PIP-Methodology/). It’s important to realize that ‘monetary’ poverty also captures sources of income that do not involve money. For example, standard monetary measures of poverty account for the value of food that subsistence farmers grow for their own consumption.",OWID Data Collection: Inequality and Poverty 1rD09jXoEG2FDKNkHsfs_iDL4wW1nGwUf1KEl-uhNTKA,how-to-embed,article,"{""toc"": [{""slug"": ""an-example"", ""text"": ""An example"", ""title"": ""An example"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""all-you-have-to-do-to-embed-it-in-your-article"", ""text"": ""All you have to do to embed it in your article"", ""title"": ""All you have to do to embed it in your article"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""change-the-map-focus-change-the-year"", ""text"": ""Change the map focus, change the year"", ""title"": ""Change the map focus, change the year"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""static-visualizations-for-your-text-or-presentation"", ""text"": ""Static visualizations for your text or presentation"", ""title"": ""Static visualizations for your text or presentation"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""You can use any of the interactive visualizations from Our World in Data in your articles. This is possible because everything is permissively licensed (under "", ""spanType"": ""span-simple-text""}, {""url"": ""https://creativecommons.org/licenses/by-sa/3.0/"", ""children"": [{""text"": ""CC-BY-SA"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "") and because there is an easy embed feature on every chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Here are two recent examples of articles that embed OWID visualizations:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""url"": ""http://ofuturodascoisas.com/projecao-da-populacao-pelo-nivel-educacional-ate-2100/"", ""children"": [{""text"": ""The Brazilian website "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""children"": [{""url"": ""http://ofuturodascoisas.com/projecao-da-populacao-pelo-nivel-educacional-ate-2100/"", ""children"": [{""text"": ""o futuro das coisas"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""text"": "" "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ofuturodascoisas.com/projecao-da-populacao-pelo-nivel-educacional-ate-2100/"", ""children"": [{""text"": ""in an article about the future of global education"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""http://www.vox.com/2016/4/25/11488196/world-malaria-day"", ""children"": [{""text"": ""Vox.com in an article on the decline of global malaria deaths on World Malaria Day"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""An example"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For example let's assume you want to write about fertility and on the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/fertility/"", ""children"": [{""text"": ""fertility"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" page you find this map that you want to embed in your own article:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/children-per-woman-un"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""All you have to do to embed it in your article"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At the bottom right of the chart you click the little icon and then chose the option "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" Embed"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "". You will see a box popping up with the following bit of text:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": """", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Now you just take this bit of html code and place it in the text of your own article."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""An iframe is used to display a website within another website ("", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""w3schools"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" has "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.w3schools.com/html/html_iframe.asp"", ""children"": [{""text"": ""more info on iframes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "".) Similar to when you embed a YouTube video in your article, your article now embeds an Our World in Data visualization."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Change the map focus, change the year"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We try to make the embed tool as useful as possible: For example, you can focus on Africa instead of World in the map above; and you can move the time slider to 2015 (you will get "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/children-per-woman-un?region=Africa&year=2015"", ""children"": [{""text"": ""this"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""). Now when you click on"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": """", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" Embed"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" you get the following bit of code:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": """", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you copy-paste this code your article will embed the map with a focus on Africa and the fertility rate for 2015."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And the same works for the chart view. Just switch to Chart in the visualization above and add the countries that you are interested in – like "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/children-per-woman-un?tab=chart&country=DEU+IRN"", ""children"": [{""text"": ""this"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". When you click on"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": """", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" Embed"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" you can now get the code to embed this line chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Static visualizations for your text or presentation"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is also possible to put static versions of Our World in Data visualizations in web articles, text documents or presentations. Just click on PNG below the chart and you have the chart that you need."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""article"", ""title"": ""How to embed Our World in Data visualizations in your article"", ""authors"": [""Max Roser""], ""excerpt"": ""Learn how to use any of the interactive visualizations from Our World in Data in your articles."", ""dateline"": ""June 13, 2016"", ""subtitle"": """", ""featured-image"": """"}",1,2023-11-30 06:50:48,2016-06-13 09:58:00,2023-12-28 16:31:11,listed,ALBJ4LtTgSZgqtMTAnYygsW8SLdIHaug3g5MR3KIx-Nw8v383EEvQb2cTruTmVbEg2e_p16pR-b34x5M7bUecA,,"You can use any of the interactive visualizations from Our World in Data in your articles. This is possible because everything is permissively licensed (under [CC-BY-SA](https://creativecommons.org/licenses/by-sa/3.0/)) and because there is an easy embed feature on every chart. Here are two recent examples of articles that embed OWID visualizations: * [The Brazilian website ](http://ofuturodascoisas.com/projecao-da-populacao-pelo-nivel-educacional-ate-2100/)_[o futuro das coisas](http://ofuturodascoisas.com/projecao-da-populacao-pelo-nivel-educacional-ate-2100/)_ [in an article about the future of global education](http://ofuturodascoisas.com/projecao-da-populacao-pelo-nivel-educacional-ate-2100/). * [Vox.com in an article on the decline of global malaria deaths on World Malaria Day](http://www.vox.com/2016/4/25/11488196/world-malaria-day). ## An example For example let's assume you want to write about fertility and on the [fertility](https://ourworldindata.org/fertility/) page you find this map that you want to embed in your own article: ## All you have to do to embed it in your article At the bottom right of the chart you click the little icon and then chose the option ** Embed**. You will see a box popping up with the following bit of text: Now you just take this bit of html code and place it in the text of your own article. An iframe is used to display a website within another website (_w3schools_ has [more info on iframes](http://www.w3schools.com/html/html_iframe.asp).) Similar to when you embed a YouTube video in your article, your article now embeds an Our World in Data visualization. ## Change the map focus, change the year We try to make the embed tool as useful as possible: For example, you can focus on Africa instead of World in the map above; and you can move the time slider to 2015 (you will get [this](https://ourworldindata.org/grapher/children-per-woman-un?region=Africa&year=2015)). Now when you click on** **** Embed** you get the following bit of code: If you copy-paste this code your article will embed the map with a focus on Africa and the fertility rate for 2015. And the same works for the chart view. Just switch to Chart in the visualization above and add the countries that you are interested in – like [this](https://ourworldindata.org/grapher/children-per-woman-un?tab=chart&country=DEU+IRN). When you click on** **** Embed** you can now get the code to embed this line chart. ## Static visualizations for your text or presentation It is also possible to put static versions of Our World in Data visualizations in web articles, text documents or presentations. Just click on PNG below the chart and you have the chart that you need.",How to embed Our World in Data visualizations in your article 1qws9Gt7OyX0J8T0yz_PIWDDBP4q9hu3WXpUGVXaSJ_g,reduction-of-cases-and-deaths-of-vaccine-preventable-diseases-in-the-us,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""The visualization here shows the reduction in cases and deaths from vaccine-preventable diseases in the United States after the introduction of each vaccine. This data was published by Roush and Murphy (2007)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For several diseases, the US has achieved a 100% reduction of cases and deaths and for many other diseases, the reduction is often very substantial as well."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Vaccine_Reduction-of-Cases-and-Deaths.png"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""4ba68da4ba753476b5cbb291e7788c38d6bf1524"": {""id"": ""4ba68da4ba753476b5cbb291e7788c38d6bf1524"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Roush and Murphy (2007) – Historical comparisons of morbidity and mortality for vaccine-preventable diseases in the United States. In the Journal of the American Medical Association, 298, 18, 2155–2163. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ncbi.nlm.nih.gov/pubmed/18000199"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Reduction of cases and deaths of vaccine-preventable diseases in the US"", ""authors"": [""Max Roser""], ""excerpt"": ""Vaccines dramatically cut the rates of vaccine-preventable diseases in the US"", ""dateline"": ""March 19, 2015"", ""subtitle"": """", ""featured-image"": ""featured-image-Vaccine_Reduction-of-Cases-and-Deaths.png""}",1,2023-11-30 05:57:25,2015-03-19 14:38:33,2024-03-18 15:41:59,listed,ALBJ4LtrhEfgvouZDYHX5z4f4XEsmLnJxtMYBIxAQvHXFHyTXV8v_6lLGJN0fLZzhbkqkISmZu8Qb4asmTsJwA,,"The visualization here shows the reduction in cases and deaths from vaccine-preventable diseases in the United States after the introduction of each vaccine. This data was published by Roush and Murphy (2007).1 For several diseases, the US has achieved a 100% reduction of cases and deaths and for many other diseases, the reduction is often very substantial as well. Roush and Murphy (2007) – Historical comparisons of morbidity and mortality for vaccine-preventable diseases in the United States. In the Journal of the American Medical Association, 298, 18, 2155–2163. [here](https://www.ncbi.nlm.nih.gov/pubmed/18000199)",Reduction of cases and deaths of vaccine-preventable diseases in the US 1qwNdKtC2-yxP5VksCZMWprDYmMipNpk3U7Z1msxfQr0,alcohol-consumption,linear-topic-page,"{""toc"": [{""slug"": ""alcohol-consumption-across-the-world-today"", ""text"": ""Alcohol consumption across the world today"", ""title"": ""Alcohol consumption across the world today"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""share-of-adults-who-drink-alcohol"", ""text"": ""Share of adults who drink alcohol"", ""title"": ""Share of adults who drink alcohol"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""alcohol-consumption-by-sex"", ""text"": ""Alcohol consumption by sex"", ""title"": ""Alcohol consumption by sex"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""heavy-drinking-sessions"", ""text"": ""Heavy drinking sessions"", ""title"": ""Heavy drinking sessions"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""share-of-adults-who-don-t-drink-alcohol"", ""text"": ""Share of adults who don't drink alcohol"", ""title"": ""Share of adults who don't drink alcohol"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""historical-perspective-on-alcohol-consumption"", ""text"": ""Historical perspective on alcohol consumption"", ""title"": ""Historical perspective on alcohol consumption"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""total-alcohol-consumption-over-the-long-run"", ""text"": ""Total alcohol consumption over the long-run"", ""title"": ""Total alcohol consumption over the long-run"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""alcohol-consumption-by-type-of-alcoholic-beverage"", ""text"": ""Alcohol consumption by type of alcoholic beverage"", ""title"": ""Alcohol consumption by type of alcoholic beverage"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""alcohol-consumption-in-the-united-states-since-1850"", ""text"": ""Alcohol consumption in the United States since 1850"", ""title"": ""Alcohol consumption in the United States since 1850"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""global-beer-consumption"", ""text"": ""Global beer consumption"", ""title"": ""Global beer consumption"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""global-wine-consumption"", ""text"": ""Global wine consumption"", ""title"": ""Global wine consumption"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""global-consumption-of-spirits"", ""text"": ""Global consumption of spirits"", ""title"": ""Global consumption of spirits"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""expenditures-on-alcohol-and-alcohol-consumption-by-income"", ""text"": ""Expenditures on alcohol and alcohol consumption by income"", ""title"": ""Expenditures on alcohol and alcohol consumption by income"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""alcohol-consumption-vs-income"", ""text"": ""Alcohol consumption vs. income"", ""title"": ""Alcohol consumption vs. income"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""alcohol-expenditure"", ""text"": ""Alcohol expenditure"", ""title"": ""Alcohol expenditure"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""the-health-impact-of-alcohol"", ""text"": ""The health impact of alcohol"", ""title"": ""The health impact of alcohol"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""alcohol-is-responsible-for-many-premature-deaths-each-year"", ""text"": ""Alcohol is responsible for many premature deaths each year"", ""title"": ""Alcohol is responsible for many premature deaths each year"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""alcohol-as-a-risk-factor-for-mortality"", ""text"": ""Alcohol as a risk factor for mortality"", ""title"": ""Alcohol as a risk factor for mortality"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""alcoholism-and-alcohol-use-disorders"", ""text"": ""Alcoholism and alcohol use disorders"", ""title"": ""Alcoholism and alcohol use disorders"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""the-disease-burden-from-alcohol-use-disorders"", ""text"": ""The disease burden from alcohol use disorders"", ""title"": ""The disease burden from alcohol use disorders"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""risk-factors-for-alcohol-use-disorders"", ""text"": ""Risk factors for alcohol use disorders"", ""title"": ""Risk factors for alcohol use disorders"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""alcohol-crime-and-road-deaths"", ""text"": ""Alcohol, crime, and road deaths"", ""title"": ""Alcohol, crime, and road deaths"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""alcohol-related-road-traffic-deaths"", ""text"": ""Alcohol-related road traffic deaths"", ""title"": ""Alcohol-related road traffic deaths"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""definitions-and-measurement"", ""text"": ""Definitions and Measurement"", ""title"": ""Definitions and Measurement"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-is-a-standard-drink-measure"", ""text"": ""What is a standard drink measure?"", ""title"": ""What is a standard drink measure?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""further-resources-guidance"", ""text"": ""Further Resources & Guidance"", ""title"": ""Further Resources & Guidance"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""all-charts"", ""text"": ""Interactive charts on alcohol consumption"", ""title"": ""Interactive charts on alcohol consumption"", ""isSubheading"": false}, {""slug"": ""article-endnotes"", ""text"": ""Endnotes"", ""title"": ""Endnotes"", ""isSubheading"": false}, {""slug"": ""article-citation"", ""text"": ""Citation"", ""title"": ""Citation"", ""isSubheading"": false}, {""slug"": ""article-licence"", ""text"": ""Licence"", ""title"": ""Licence"", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Alcohol has historically, and continues to, hold an important role in social engagement and bonding for many. Social drinking or moderate alcohol consumption for many is pleasurable."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, alcohol consumption – especially in excess – is linked to a number of negative outcomes: as a risk factor for diseases and health impacts, crime, road incidents, and, for some, alcohol dependence."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This topic page looks at the data on global patterns of alcohol consumption, patterns of drinking, beverage types, the prevalence of alcoholism, and consequences, including crime, mortality, and road incidents."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Related topics:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Data on other drug use can be found on our full topic page "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/substance-use"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Drug use disorders are often classified within the same category as mental health disorders — research and data on mental health can be found on our topic page "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/mental-health"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Support for alcohol dependency"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At the end of this topic page, you will "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/alcohol-consumption#further-resources-guidance"", ""children"": [{""text"": ""find additional resources"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and guidance if you, or someone you know, needs support in dealing with alcohol dependency."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""url"": ""#all-charts"", ""children"": [{""text"": ""See all interactive charts on Alcohol Consumption ↓"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""text"": [{""text"": ""Alcohol consumption across the world today"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This interactive map shows the annual average alcohol consumption of alcohol, expressed per person aged 15 years or older. To account for the differences in alcohol content of different alcoholic drinks (e.g., beer, wine, spirits), this is reported in liters of pure alcohol per year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To make this average more understandable, we can express it in bottles of wine. Wine contains around 12% pure alcohol per volume"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" so that one liter of wine contains 0.12 liters of pure alcohol. So, a value of 6 liters of pure alcohol per person per year is equivalent to 50 liters of wine. Or, 67 standard bottles of wine (which have a volume of 0.75 liters)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As the map shows, the average per capita alcohol consumption varies widely globally."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see large geographical differences: Alcohol consumption across North Africa and the Middle East is particularly low — in many countries, close to zero. At the upper end of the scale, alcohol intake across Europe is higher."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/total-alcohol-consumption-per-capita-litres-of-pure-alcohol"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Share of adults who drink alcohol"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This interactive map shows the share of adults who drink alcohol. This is given as the share of adults aged 15 years and older who have drunk alcohol within the previous year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In many countries, the majority of adults drink some alcohol. Across Europe, for example, more than two-thirds do in most countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Again, the prevalence of drinking across North Africa and the Middle East is notably lower than elsewhere. Typically, 5 to 10 percent of adults across these regions drank in the preceding year, and in a number of countries, this was below 5 percent."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-adults-who-drank-alcohol-in-last-year"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Alcohol consumption by sex"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When we look at gender differences, we see that in all countries, men have a higher alcohol consumption than women."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In a related chart, you can see the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-who-drank-alcohol-last-week"", ""children"": [{""text"": ""share who drink alcohol by gender and age group in the UK"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/alcohol-consumption-per-capita-men-women"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Heavy drinking sessions"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Alcohol consumption – whilst a risk factor for a number of health outcomes – typically has the greatest negative impacts when consumed within heavy sessions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This pattern of drinking is often termed 'binging,' where individuals consume large amounts of alcohol within a single session versus small quantities more frequently."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Heavy episodic drinking is defined as the proportion of adult drinkers who have had at least 60 grams or more of pure alcohol on at least one occasion in the past 30 days. An intake of 60 grams of pure alcohol is approximately equal to 6 "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/alcohol-consumption#what-is-a-standard-drink-measure"", ""children"": [{""text"": ""standard alcoholic drinks"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The map shows heavy drinkers – those who had an episode of heavy drinking in the previous 30 days – as a share of total drinkers (i.e., those who have drunk less than one alcoholic drink in the last 12 months are excluded)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The comparison of this map with the previous maps makes clear that heavy drinking is not necessarily most common in the same countries where alcohol consumption is most common."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Data on the prevalence of binge drinking by age and gender in the UK can be found "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-drinkers-who-binged"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and trends in heavy and binge drinking in the USA can be found "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-who-drank-drank-usa"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/drinkers-had-a-heavy-session-in-past-30-days"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Share of adults who don't drink alcohol"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Global trends on alcohol abstinence show a mirror image of drinking prevalence data. This is shown in the charts as the share of adults who had not drunk in the prior year and those who have never drunk alcohol."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Here, we see particularly high levels of alcohol abstinence across North Africa and the Middle East. In most countries in this region, the majority of adults have never drunk alcohol."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-who-have-not-drank-alcohol-in-last-year"", ""type"": ""chart"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-population-who-never-drink-alcohol"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Data on the share who don't drink alcohol by gender and age group in the UK is available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-who-dont-drink-alcohol"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Historical perspective on alcohol consumption"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Total alcohol consumption over the long-run"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows alcohol consumption since 1890 in a number of countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A century ago, some countries had much higher levels of alcohol consumption. In France in the 1920s, the average was 22.1 liters of pure alcohol per person per year. This equals 184 one-liter wine bottles per person per year."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Note that in contrast to the modern statistics that are expressed in alcohol consumption per person older than 15 years, this includes children as well – the average alcohol consumption per adult was, therefore, even higher."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/per-capita-alcohol-1890"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Alcohol consumption by type of alcoholic beverage"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This chart shows the change in consumption of alcoholic beverages."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""By default, the data for France is shown – in recent decades, here, the share of beer consumption increased to make up around a fifth of alcohol consumption in France."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""With the change country feature, it is possible to view the same data for other countries. Sweden, for example, increased the share of wine consumption and, therefore, reduced the share of spirits."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/alcohol-by-type-1890?country=~FRA"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Alcohol consumption in the United States since 1850"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Long-run data on alcohol consumption from the United States gives us one perspective of drinking since 1850. In the chart, we see the average consumption (in liters of ethanol) of different beverage types per person in the USA since the mid-nineteenth century."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Over this long time period, we see that per capita drinking quantities have been relatively constant — typically averaging around 8 to 9 liters per year. Over the period 1920-1933, there was a ban on the production, importation, transportation, and sale of alcoholic beverages in the United States (known as the 'National Alcohol Prohibition'). Since the statistics here reflect reported sales and consumption statistics, they assume zero consumption of alcohol over this time. However, there is evidence that alcohol consumption continued through the black market and illegal sales, particularly in the sales of spirits. It's estimated that at the beginning of Prohibition, alcohol consumption decreased to approximately 30 percent of pre-prohibition levels but slowly increased to 60-70 percent by the end of the period."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we see, following prohibition, levels of alcohol consumption returned to similar levels as in the pre-prohibition period."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/alcohol-consumption-per-person-us"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Global beer consumption"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The charts show global consumption of beer, first in terms of beer as a share of total alcohol consumption, and then the estimated average consumption per person."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Both are measured in terms of pure alcohol/ethanol intake rather than the total quantity of the beverage. Beer contains around 5% of pure alcohol per volume"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" so that one liter of beer contains 0.05 liters of pure alcohol. This means that 5 liters of pure alcohol equals 100 liters of beer."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/beer-as-share-alcohol-consumption"", ""type"": ""chart"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/beer-consumption-per-person"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Global wine consumption"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The charts show global consumption of wine, first in terms of wine as a share of total alcohol consumption, and then the estimated average consumption per person."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Both are measured in terms of pure alcohol/ethanol intake rather than the total quantity of the beverage. Wine contains around 12% pure alcohol per volume, so that one liter of wine contains 0.12 liters of pure alcohol."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/wine-as-share-alcohol-consumption"", ""type"": ""chart"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/wine-consumption-per-capita"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Global consumption of spirits"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The charts show global consumption of spirits, which are distilled alcoholic drinks, including gin, rum, whisky, tequila, and vodka."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The first map shows this in terms of spirits as a share of total alcohol consumption. In many Asian countries, spirits account for most of total alcohol consumption."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The second map shows the estimated average consumption per person."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Both are measured in terms of pure alcohol/ethanol intake rather than the total quantity of the beverage."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/spirits-as-share-total-alcohol-consumption"", ""type"": ""chart"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/spirits-consumption-per-person"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Expenditures on alcohol and alcohol consumption by income"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Alcohol consumption vs. income"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Does alcohol consumption increase as countries get richer?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, we see the relationship between average per capita alcohol consumption – in liters of pure alcohol per year – versus gross domestic product (GDP) per capita across countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When we look at national averages in this way, there is no distinct relationship between income and alcohol consumption. As shown by clusters of countries (for example, Middle Eastern countries with low alcohol intake but high GDP per capita), we tend to see strong cultural patterns that tend to alter the standard income-consumption relationship we may expect."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/alcohol-consumption-vs-gdp-per-capita"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, when we look at consumption data within given countries, we sometimes do see a clear income correlation. Taking 2016 data in the UK as an example, we see that people within higher income brackets tend to drink more frequently. This correlation is also likely to be influenced by other lifestyle determinants and habits; the UK ONS also reports that when grouped by education status, those with a university tend to drink more in total and more frequently than those of lower education status. There are also differences when grouped by profession: individuals in managerial or professional positions tend to drink more frequently than those in intermediate or manual labor roles."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-who-drank-on-5-or-more-days-by-income"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We also find correlates in drinking "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""patterns"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" when we look at groupings of income, education or work status. Although those in lower income or educational status groups often drink less overall, they are more likely to have lower-frequency, higher-intensity drinking patterns. Overall, these groups drink less, but a higher percentage will drink heavily when they do."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Alcohol expenditure"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This interactive chart shows the average share of household expenditure that is spent on alcohol."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Data on alcohol expenditure is typically limited to North America, Europe, and Oceania."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/alcohol-expenditure-as-share-of-total"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Alcohol expenditure over the long-term"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This shows the expenditure on alcohol in the United States, differentiated by where the alcohol has been purchased and consumed."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/alcohol-expenditure"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The health impact of alcohol"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Alcohol is responsible for many premature deaths each year"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Alcohol is one of the world's largest risk factors for premature death."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Institute for Health Metrics and Evaluation (IHME), in its "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Global Burden of Disease"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" study, provides estimates of the number of deaths attributed to the range of risk factors."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In the visualization, we see the number of deaths per year attributed to each risk factor. This chart is shown for the global total but can be explored for any country or region using the \""Change country or region\"" toggle."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/number-of-deaths-by-risk-factor"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Alcohol as a risk factor for mortality"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Alcohol consumption is a known risk factor for a number of health conditions, and potential mortality cases. Alcohol consumption has a causal impact on more than 200 health conditions (diseases and injuries)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, we see estimates of the alcohol-attributable fraction (AAF), which is the proportion of deaths that are caused or exacerbated by alcohol (i.e., that proportion that would disappear if alcohol consumption was removed). We see that the proportion of deaths attributed to alcohol consumption is lower in North Africa and the Middle East and much higher in Eastern Europe."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/alcohol-attributable-fraction-of-mortality"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Rate of premature deaths due to alcohol"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Shown here is the rate of premature deaths caused by alcohol."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Globally, the age-standardized death rate has declined from approximately 40 deaths per 100,000 people in the early 1990s to 30 deaths per 100,000 in 2019."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/rate-of-premature-deaths-due-to-alcohol"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Alcohol-related deaths by age"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows the age distribution of those dying premature deaths due to alcohol."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is possible to switch this data to any other country or region in the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/deaths-attributed-to-alcohol-use-by-age"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Alcoholism and alcohol use disorders"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Alcohol use disorder"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" (AUD) refers to the drinking of alcohol that causes mental and physical health problems."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Alcohol use disorder, which includes alcohol dependence, is defined in the WHO's "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""International Classification of Diseases"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" (available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://icd.who.int/browse11/l-m/en#/http://id.who.int/icd/entity/1676588433"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "")."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/alcohol-consumption#further-resources-guidance"", ""children"": [{""text"": ""end of this topic page"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", we provide a number of potential sources of support and guidance for those concerned about uncontrolled drinking or alcohol dependency."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A definite diagnosis of dependence should usually be made only if three or more of the following have been present together at some time during the previous year:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""(a) a strong desire or sense of compulsion to take the substance;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""(b) difficulties in controlling substance-taking behaviour in terms of its onset, termination, or levels of use;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""(c) a physiological withdrawal state when substance use has ceased or been reduced, as evidenced by: the characteristic withdrawal syndrome for the substance; or use of the same (or a closely related) substance with the intention of relieving or avoiding withdrawal symptoms;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""(d) evidence of tolerance, such that increased doses of the psychoactive substance are required in order to achieve effects originally produced by lower doses (clear examples of this are found in alcohol- and opiate-dependent individuals who may take daily doses sufficient to incapacitate or kill nontolerant users);"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""(e) progressive neglect of alternative pleasures or interests because of psychoactive substance use, increased amount of time necessary to obtain or take the substance or to recover from its effects;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""(f) persisting with substance use despite clear evidence of overtly harmful consequences, such as harm to the liver through excessive drinking, depressive mood states consequent to periods of heavy substance use, or drug-related impairment of cognitive functioning; efforts should be made to determine that the user was actually, or could be expected to be, aware of the nature and extent of the harm."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Prevalence of alcohol use disorders"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It's estimated that globally, around 1 percent of the population has an alcohol use disorder. At the country level, as shown in the chart, this ranges from around 0.5 to 5 percent of the population."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When we look at the variance in prevalence "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/prevalence-of-alcohol-use-disorders-by-age"", ""children"": [{""text"": ""across age groups"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", we see that globally, the prevalence is highest in those aged between 15 and 49 years old."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The breakdown of alcohol use disorders by gender for any country can be viewed "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/number-with-alcohol-use-disorders"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""; the majority of people with alcohol use disorders – around three-quarters – are male."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-suffering-from-alcohol-use-disorders"", ""type"": ""chart"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/prevalence-of-alcohol-disorders-males-vs-females"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The scatter plot compares the prevalence of alcohol use disorders in males versus that of females. The prevalence of alcohol dependence in men is typically higher than in women across all countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Deaths from alcohol use disorders"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Deaths from alcohol dependence can occur both directly or indirectly. Indirect deaths from alcohol use disorders can occur indirectly through suicide. Although clear attribution of suicide deaths is challenging, alcohol use disorders are a known and established risk factor. It's estimated that the relative risk of suicide in an individual with alcohol dependence is around ten times higher than in an individual without."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows direct death rates (not including suicide deaths) from alcohol use disorders across the world. The death rates are typically higher in Eastern Europe and lower in North Africa and the Middle East."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/death-rates-from-alcohol-use-disorders"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The total estimated number of deaths by country from 1990 to 2019 is found "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/deaths-from-alcohol-use-disorders"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Alcohol use disorder treatment"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Global data on the prevalence and effectiveness of alcohol use disorder treatment is incomplete."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, we see data across some countries on the share of people with an alcohol use disorder who received treatment. This data is based on estimates of prevalence and treatment published by the World Health Organization (WHO)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-with-alcohol-use-disorders-receiving-treatment"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Alcohol use disorder vs. average alcohol intake"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Do countries with higher average alcohol consumption have a higher prevalence of alcohol use disorders?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, we see the prevalence of alcohol dependence versus the average per capita alcohol consumption. There is no clear evidence that high overall consumption (particularly in moderate quantities) is connected to the onset of alcohol dependency."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-with-alcohol-use-disorder-vs-alcohol-consumption"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""The disease burden from alcohol use disorders"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Measuring the health impact by mortality alone fails to capture the impact that alcohol use disorders have on an individual's well-being. The '"", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/burden-of-disease"", ""children"": [{""text"": ""disease burden"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""' – measured in Disability-Adjusted Life Years (DALYs) – considers mortality and years lived with disability or health burden. The map shows DALYs per 100,000 people, which result from alcohol use disorders."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""DALY rates differentiated by age group can be found "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/dalys-from-alcohol-use-disorders-by-age"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/alcohol-disorders-dalys-age-standardized-rate"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Risk factors for alcohol use disorders"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Many of the risk factors for alcohol dependency are similar to those of overall drug use disorders (including illicit drug disorders). Further discussion on these risk factors can be found on our topic page on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/substance-use#risk-factors-for-substance-use-disorders"", ""children"": [{""text"": ""drug use"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Mental health disorders as a risk factor for alcohol dependency"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart we show results from a study published by Swendsen et al. (2010)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this study, the authors followed a cohort of more than 5,000 individuals with and without a "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/mental-health"", ""children"": [{""text"": ""mental health"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" disorder (but without a drug use disorder) over a 10-year period. 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Available "", ""spanType"": ""span-simple-text""}, {""url"": ""http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0091936#s1"", ""children"": [{""text"": ""online"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ceab559fef6fd594ed665f1caec85d0958f1e217"": {""id"": ""ceab559fef6fd594ed665f1caec85d0958f1e217"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Full data with confidence intervals and statistical significance can be found in our table "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/mental-health-disorders-as-risk-for-substance-use"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ede55f3a8fd55d1c5697174f981907dda2e373ef"": {""id"": ""ede55f3a8fd55d1c5697174f981907dda2e373ef"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""22.1 liters per person in France equals 22.1l / 0.12l = 184 bottles per year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""linear-topic-page"", ""title"": ""Alcohol Consumption"", ""authors"": [""Hannah Ritchie"", ""Max Roser""], ""excerpt"": ""Who consumes the most alcohol? How has consumption changed over time? And what are the health impacts?"", ""dateline"": ""This article was first published in April 2018. It was revised in January 2024."", ""subtitle"": ""Who consumes the most alcohol? How has consumption changed over time? And what are the health impacts?"", ""sidebar-toc"": true, ""featured-image"": ""alcohol-consumption-thumbnail.png""}",1,2023-11-10 14:26:29,2022-01-01 14:31:00,2024-02-29 15:40:42,unlisted,ALBJ4LuZf9-Jagc8coa-jMoFbA-QONXgC_hFiNI2OHQ6VjKfmnpBy-EmsvzmP2PvZL33byCAhCD9rkFAg2pFqA,,"Alcohol has historically, and continues to, hold an important role in social engagement and bonding for many. Social drinking or moderate alcohol consumption for many is pleasurable. However, alcohol consumption – especially in excess – is linked to a number of negative outcomes: as a risk factor for diseases and health impacts, crime, road incidents, and, for some, alcohol dependence. This topic page looks at the data on global patterns of alcohol consumption, patterns of drinking, beverage types, the prevalence of alcoholism, and consequences, including crime, mortality, and road incidents. **Related topics:** Data on other drug use can be found on our full topic page [here](https://ourworldindata.org/substance-use). Drug use disorders are often classified within the same category as mental health disorders — research and data on mental health can be found on our topic page [here](https://ourworldindata.org/mental-health). **Support for alcohol dependency** At the end of this topic page, you will [find additional resources](https://ourworldindata.org/alcohol-consumption#further-resources-guidance) and guidance if you, or someone you know, needs support in dealing with alcohol dependency. **[See all interactive charts on Alcohol Consumption ↓](#all-charts)** # Alcohol consumption across the world today This interactive map shows the annual average alcohol consumption of alcohol, expressed per person aged 15 years or older. To account for the differences in alcohol content of different alcoholic drinks (e.g., beer, wine, spirits), this is reported in liters of pure alcohol per year. To make this average more understandable, we can express it in bottles of wine. Wine contains around 12% pure alcohol per volume1 so that one liter of wine contains 0.12 liters of pure alcohol. So, a value of 6 liters of pure alcohol per person per year is equivalent to 50 bottles of wine per year. As the map shows, the average per capita alcohol consumption varies widely globally. We see large geographical differences: Alcohol consumption across North Africa and the Middle East is particularly low — in many countries, close to zero. At the upper end of the scale, alcohol intake across Europe is higher. ## Share of adults who drink alcohol This interactive map shows the share of adults who drink alcohol. This is given as the share of adults aged 15 years and older who have drunk alcohol within the previous year. In many countries, the majority of adults drink some alcohol. Across Europe, for example, more than two-thirds do in most countries. Again, the prevalence of drinking across North Africa and the Middle East is notably lower than elsewhere. Typically, 5 to 10 percent of adults across these regions drank in the preceding year, and in a number of countries, this was below 5 percent. ## Alcohol consumption by sex When we look at gender differences, we see that in all countries, men have a higher alcohol consumption than women. In a related chart, you can see the [share who drink alcohol by gender and age group in the UK](https://ourworldindata.org/grapher/share-who-drank-alcohol-last-week). ## Heavy drinking sessions Alcohol consumption – whilst a risk factor for a number of health outcomes – typically has the greatest negative impacts when consumed within heavy sessions. This pattern of drinking is often termed 'binging,' where individuals consume large amounts of alcohol within a single session versus small quantities more frequently. Heavy episodic drinking is defined as the proportion of adult drinkers who have had at least 60 grams or more of pure alcohol on at least one occasion in the past 30 days. An intake of 60 grams of pure alcohol is approximately equal to 6 [standard alcoholic drinks](https://ourworldindata.org/alcohol-consumption#what-is-a-standard-drink-measure). The map shows heavy drinkers – those who had an episode of heavy drinking in the previous 30 days – as a share of total drinkers (i.e., those who have drunk less than one alcoholic drink in the last 12 months are excluded). The comparison of this map with the previous maps makes clear that heavy drinking is not necessarily most common in the same countries where alcohol consumption is most common. Data on the prevalence of binge drinking by age and gender in the UK can be found [here](https://ourworldindata.org/grapher/share-of-drinkers-who-binged), and trends in heavy and binge drinking in the USA can be found [here](https://ourworldindata.org/grapher/share-who-drank-drank-usa). ## Share of adults who don't drink alcohol Global trends on alcohol abstinence show a mirror image of drinking prevalence data. This is shown in the charts as the share of adults who had not drunk in the prior year and those who have never drunk alcohol. Here, we see particularly high levels of alcohol abstinence across North Africa and the Middle East. In most countries in this region, the majority of adults have never drunk alcohol. Data on the share who don't drink alcohol by gender and age group in the UK is available [here](https://ourworldindata.org/grapher/share-who-dont-drink-alcohol). # Historical perspective on alcohol consumption ## Total alcohol consumption over the long-run The chart shows alcohol consumption since 1890 in a number of countries. A century ago, some countries had much higher levels of alcohol consumption. In France in the 1920s, the average was 22.1 liters of pure alcohol per person per year. This equals 184 one-liter wine bottles per person per year.2 Note that in contrast to the modern statistics that are expressed in alcohol consumption per person older than 15 years, this includes children as well – the average alcohol consumption per adult was, therefore, even higher. ## Alcohol consumption by type of alcoholic beverage This chart shows the change in consumption of alcoholic beverages. By default, the data for France is shown – in recent decades, here, the share of beer consumption increased to make up around a fifth of alcohol consumption in France. With the change country feature, it is possible to view the same data for other countries. Sweden, for example, increased the share of wine consumption and, therefore, reduced the share of spirits. ## Alcohol consumption in the United States since 1850 Long-run data on alcohol consumption from the United States gives us one perspective of drinking since 1850. In the chart, we see the average consumption (in liters of ethanol) of different beverage types per person in the USA since the mid-nineteenth century. Over this long time period, we see that per capita drinking quantities have been relatively constant — typically averaging around 8 to 9 liters per year. Over the period 1920-1933, there was a ban on the production, importation, transportation, and sale of alcoholic beverages in the United States (known as the 'National Alcohol Prohibition'). Since the statistics here reflect reported sales and consumption statistics, they assume zero consumption of alcohol over this time. However, there is evidence that alcohol consumption continued through the black market and illegal sales, particularly in the sales of spirits. It's estimated that at the beginning of Prohibition, alcohol consumption decreased to approximately 30 percent of pre-prohibition levels but slowly increased to 60-70 percent by the end of the period.3 As we see, following prohibition, levels of alcohol consumption returned to similar levels as in the pre-prohibition period. ## Global beer consumption The charts show global consumption of beer, first in terms of beer as a share of total alcohol consumption, and then the estimated average consumption per person. Both are measured in terms of pure alcohol/ethanol intake rather than the total quantity of the beverage. Beer contains around 5% of pure alcohol per volume1 so that one liter of beer contains 0.05 liters of pure alcohol. This means that 5 liters of pure alcohol equals 100 liters of beer. ## Global wine consumption The charts show global consumption of wine, first in terms of wine as a share of total alcohol consumption, and then the estimated average consumption per person. Both are measured in terms of pure alcohol/ethanol intake rather than the total quantity of the beverage. Wine contains around 12% pure alcohol per volume, so that one liter of wine contains 0.12 liters of pure alcohol. ## Global consumption of spirits The charts show global consumption of spirits, which are distilled alcoholic drinks, including gin, rum, whisky, tequila, and vodka. The first map shows this in terms of spirits as a share of total alcohol consumption. In many Asian countries, spirits account for most of total alcohol consumption. The second map shows the estimated average consumption per person. Both are measured in terms of pure alcohol/ethanol intake rather than the total quantity of the beverage. # Expenditures on alcohol and alcohol consumption by income ## Alcohol consumption vs. income Does alcohol consumption increase as countries get richer? In the chart, we see the relationship between average per capita alcohol consumption – in liters of pure alcohol per year – versus gross domestic product (GDP) per capita across countries. When we look at national averages in this way, there is no distinct relationship between income and alcohol consumption. As shown by clusters of countries (for example, Middle Eastern countries with low alcohol intake but high GDP per capita), we tend to see strong cultural patterns that tend to alter the standard income-consumption relationship we may expect. However, when we look at consumption data within given countries, we sometimes do see a clear income correlation. Taking 2016 data in the UK as an example, we see that people within higher income brackets tend to drink more frequently. This correlation is also likely to be influenced by other lifestyle determinants and habits; the UK ONS also reports that when grouped by education status, those with a university tend to drink more in total and more frequently than those of lower education status. There are also differences when grouped by profession: individuals in managerial or professional positions tend to drink more frequently than those in intermediate or manual labor roles.4 We also find correlates in drinking _patterns_ when we look at groupings of income, education or work status. Although those in lower income or educational status groups often drink less overall, they are more likely to have lower-frequency, higher-intensity drinking patterns. Overall, these groups drink less, but a higher percentage will drink heavily when they do. ## Alcohol expenditure This interactive chart shows the average share of household expenditure that is spent on alcohol. Data on alcohol expenditure is typically limited to North America, Europe, and Oceania. ### Alcohol expenditure over the long-term This shows the expenditure on alcohol in the United States, differentiated by where the alcohol has been purchased and consumed. # The health impact of alcohol ## Alcohol is responsible for many premature deaths each year Alcohol is one of the world's largest risk factors for premature death. The Institute for Health Metrics and Evaluation (IHME), in its _Global Burden of Disease_ study, provides estimates of the number of deaths attributed to the range of risk factors.5 In the visualization, we see the number of deaths per year attributed to each risk factor. This chart is shown for the global total but can be explored for any country or region using the ""Change country or region"" toggle. ## Alcohol as a risk factor for mortality Alcohol consumption is a known risk factor for a number of health conditions, and potential mortality cases. Alcohol consumption has a causal impact on more than 200 health conditions (diseases and injuries). In the chart, we see estimates of the alcohol-attributable fraction (AAF), which is the proportion of deaths that are caused or exacerbated by alcohol (i.e., that proportion that would disappear if alcohol consumption was removed). We see that the proportion of deaths attributed to alcohol consumption is lower in North Africa and the Middle East and much higher in Eastern Europe. ### Rate of premature deaths due to alcohol Shown here is the rate of premature deaths caused by alcohol. Globally, the age-standardized death rate has declined from approximately 40 deaths per 100,000 people in the early 1990s to 30 deaths per 100,000 in 2019. ### Alcohol-related deaths by age The chart shows the age distribution of those dying premature deaths due to alcohol. It is possible to switch this data to any other country or region in the world. ## Alcoholism and alcohol use disorders _Alcohol use disorder_ (AUD) refers to the drinking of alcohol that causes mental and physical health problems. Alcohol use disorder, which includes alcohol dependence, is defined in the WHO's _International Classification of Diseases_ (available [here](https://icd.who.int/browse11/l-m/en#/http://id.who.int/icd/entity/1676588433)). At the [end of this topic page](https://ourworldindata.org/alcohol-consumption#further-resources-guidance), we provide a number of potential sources of support and guidance for those concerned about uncontrolled drinking or alcohol dependency. A definite diagnosis of dependence should usually be made only if three or more of the following have been present together at some time during the previous year: * (a) a strong desire or sense of compulsion to take the substance; * (b) difficulties in controlling substance-taking behaviour in terms of its onset, termination, or levels of use; * (c) a physiological withdrawal state when substance use has ceased or been reduced, as evidenced by: the characteristic withdrawal syndrome for the substance; or use of the same (or a closely related) substance with the intention of relieving or avoiding withdrawal symptoms; * (d) evidence of tolerance, such that increased doses of the psychoactive substance are required in order to achieve effects originally produced by lower doses (clear examples of this are found in alcohol- and opiate-dependent individuals who may take daily doses sufficient to incapacitate or kill nontolerant users); * (e) progressive neglect of alternative pleasures or interests because of psychoactive substance use, increased amount of time necessary to obtain or take the substance or to recover from its effects; * (f) persisting with substance use despite clear evidence of overtly harmful consequences, such as harm to the liver through excessive drinking, depressive mood states consequent to periods of heavy substance use, or drug-related impairment of cognitive functioning; efforts should be made to determine that the user was actually, or could be expected to be, aware of the nature and extent of the harm. ### Prevalence of alcohol use disorders It's estimated that globally, around 1 percent of the population has an alcohol use disorder. At the country level, as shown in the chart, this ranges from around 0.5 to 5 percent of the population. When we look at the variance in prevalence [across age groups](https://ourworldindata.org/grapher/prevalence-of-alcohol-use-disorders-by-age), we see that globally, the prevalence is highest in those aged between 15 and 49 years old. The breakdown of alcohol use disorders by gender for any country can be viewed [here](https://ourworldindata.org/grapher/number-with-alcohol-use-disorders); the majority of people with alcohol use disorders – around three-quarters – are male. The scatter plot compares the prevalence of alcohol use disorders in males versus that of females. The prevalence of alcohol dependence in men is typically higher than in women across all countries. ### Deaths from alcohol use disorders Deaths from alcohol dependence can occur both directly or indirectly. Indirect deaths from alcohol use disorders can occur indirectly through suicide. Although clear attribution of suicide deaths is challenging, alcohol use disorders are a known and established risk factor. It's estimated that the relative risk of suicide in an individual with alcohol dependence is around ten times higher than in an individual without.6 The chart shows direct death rates (not including suicide deaths) from alcohol use disorders across the world. The death rates are typically higher in Eastern Europe and lower in North Africa and the Middle East. The total estimated number of deaths by country from 1990 to 2019 is found [here](https://ourworldindata.org/grapher/deaths-from-alcohol-use-disorders). ### Alcohol use disorder treatment Global data on the prevalence and effectiveness of alcohol use disorder treatment is incomplete. In the chart, we see data across some countries on the share of people with an alcohol use disorder who received treatment. This data is based on estimates of prevalence and treatment published by the World Health Organization (WHO). ### Alcohol use disorder vs. average alcohol intake Do countries with higher average alcohol consumption have a higher prevalence of alcohol use disorders? In the chart, we see the prevalence of alcohol dependence versus the average per capita alcohol consumption. There is no clear evidence that high overall consumption (particularly in moderate quantities) is connected to the onset of alcohol dependency. ## The disease burden from alcohol use disorders Measuring the health impact by mortality alone fails to capture the impact that alcohol use disorders have on an individual's well-being. The '[disease burden](https://ourworldindata.org/burden-of-disease)' – measured in Disability-Adjusted Life Years (DALYs) – considers mortality and years lived with disability or health burden. The map shows DALYs per 100,000 people, which result from alcohol use disorders. DALY rates differentiated by age group can be found [here](https://ourworldindata.org/grapher/dalys-from-alcohol-use-disorders-by-age). ## Risk factors for alcohol use disorders Many of the risk factors for alcohol dependency are similar to those of overall drug use disorders (including illicit drug disorders). Further discussion on these risk factors can be found on our topic page on [drug use](https://ourworldindata.org/substance-use#risk-factors-for-substance-use-disorders). ### Mental health disorders as a risk factor for alcohol dependency In the chart we show results from a study published by Swendsen et al. (2010).7 In this study, the authors followed a cohort of more than 5,000 individuals with and without a [mental health](https://ourworldindata.org/mental-health) disorder (but without a drug use disorder) over a 10-year period. Following the ten-year period, they re-assessed such individuals for whether they had either nicotine, alcohol, or illicit drug dependency.8 The results in the chart show the increased risk of developing alcohol dependency (we show results for illicit drug dependency in our topic page on [drug use](https://ourworldindata.org/grapher/mental-health-as-risk-for-drug-dependency)) for someone with a given mental health disorder (relative to those without). For example, a value of 3.6 for bipolar disorder indicates that illicit drug dependency became more than three times more likely in individuals with bipolar disorder than those without. The risk of an alcohol use disorder is highest in individuals with intermittent explosive disorder, dysthymia, ODD, bipolar disorder, and social phobia. # Alcohol, crime, and road deaths ## Alcohol-related road traffic deaths The map shows the share of all road traffic deaths attributed to alcohol consumption over the national legal limit for alcohol consumption. In South Africa and Papua New Guinea, more than half of all traffic deaths are attributable to alcohol consumption. In the US, Canada, Australia, New Zealand, Argentina, and many European countries, alcohol is responsible for around a third of all traffic deaths. # Definitions and Measurement ## What is a standard drink measure? Whilst the World Health Organization (WHO) and most national guidelines typically quantify one unit of alcohol as equal to 10 grams of pure alcohol, the metric used as a 'standard measure' can vary across countries. Most countries across Europe use this 10-gram metric. However, this can vary, with several adopting 12 or 14 grams per unit. In North America, a unit is typically taken as 14 grams of pure alcohol. In Japan, this is as high as around 20 grams per unit. # Further Resources & Guidance ### Alcohol Rehab Guide * **Information**: Guidance on the signs of alcoholism, unhealthy drinking behaviors, and support on where to go for help * **Geographical coverage:** Universal guidance; support options for the United States * **Available at: **[https://www.alcoholrehabguide.org/support/](https://www.alcoholrehabguide.org/support/) ### Hello Sunday Morning * **Information**: A social movement with the aim to reduce stigma around alcohol and to encourages people to consider their relationship with alcohol. * **Available at: **[HelloSundayMorning.org](https://www.hellosundaymorning.org/) ### Drink Aware * **Information**: List and contact details of a range of places for support on alcohol issues * **Geographical coverage:** United Kingdom * **Available at: **[https://www.drinkaware.co.uk/alcohol-support-services/](https://www.drinkaware.co.uk/alcohol-support-services/) ### Rethinking Drinking * **Information**: Test to assess your drinking patterns relative to the US population * **Geographical coverage:** Global; assesses relative to US drinking patterns * **Available at: **[What's your drinking pattern?](https://www.rethinkingdrinking.niaaa.nih.gov/How-much-is-too-much/Is-your-drinking-pattern-risky/Whats-Your-Pattern.aspx) ### Rehab 4 Addiction * **Information**: An advisory and referral service for people who suffer from alcohol, drug, and behavioral addiction. * **Geographical coverage:**  Universal guidance; support options for the United Kingdom * **Available at:** [https://www.rehab4addiction.co.uk/](https://www.rehab4addiction.co.uk/) Alcohol.org has [this overview](https://www.alcohol.org/statistics-information/abv/) of the range of alcohol by volume of beer, wine, & liquor. 22.1 liters per person in France equals 22.1l / 0.12l = 184 bottles per year. Miron & Zwiebel (1991). Alcohol Consumption During Prohibition. _The American Economic Review_, Vol. 81, No. 2, pp. 242-247, (May 1991). Available [online](http://www.nber.org/papers/w3675). ONS (2018). Adult drinking habits in Great Britain. _UK Office of National Statistics_. Available at: [https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/drugusealcoholandsmoking/datasets/adultdrinkinghabits](https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/drugusealcoholandsmoking/datasets/adultdrinkinghabits) GBD 2019 Risk Factor Collaborators. ""Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019"" (2020). [Link here](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30752-2/fulltext) Ferrari et al. (2015). The Burden Attributable to Mental and Substance Use Disorders as Risk Factors for Suicide: Findings from the Global Burden of Disease Study 2010. _PLOS ONE_. Available [online](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0091936#s1). Swendsen, J., Conway, K. P., Degenhardt, L., Glantz, M., Jin, R., Merikangas, K. R., … & Kessler, R. C. (2010). Mental disorders as risk factors for substance use, abuse, and dependence: results from the 10‐year follow‐up of the National Comorbidity Survey. _Addiction_, _105_(6), 1117-1128. Available at: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1360-0443.2010.02902.x Full data with confidence intervals and statistical significance can be found in our table [here](https://ourworldindata.org/mental-health-disorders-as-risk-for-substance-use).",Alcohol Consumption 1qrYOvwqqKYdImSdGChhXmBFaeBx8dYeR7GMjoPspu8s,indoor-air-pollution,linear-topic-page,"{""toc"": [{""slug"": ""indoor-air-pollution-is-one-of-the-leading-risk-factors-for-premature-death"", ""text"": ""Indoor air pollution is one of the leading risk factors for premature death"", ""title"": ""Indoor air pollution is one of the leading risk factors for premature death"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""indoor-air-pollution-is-a-leading-risk-factor-for-deaths-in-poor-countries"", ""text"": ""Indoor air pollution is a leading risk factor for deaths in poor countries"", ""title"": ""Indoor air pollution is a leading risk factor for deaths in poor countries"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""the-global-distribution-of-deaths-from-indoor-air-pollution"", ""text"": ""The global distribution of deaths from indoor air pollution"", ""title"": ""The global distribution of deaths from indoor air pollution"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""death-rates-are-highest-across-low-income-countries"", ""text"": ""Death rates are highest across low-income countries"", ""title"": ""Death rates are highest across low-income countries"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""how-has-mortality-from-indoor-air-pollution-changed-over-time"", ""text"": ""How has mortality from indoor air pollution changed over time?"", ""title"": ""How has mortality from indoor air pollution changed over time?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""annual-deaths-from-indoor-air-pollution-have-declined-globally"", ""text"": ""Annual deaths from indoor air pollution have declined globally"", ""title"": ""Annual deaths from indoor air pollution have declined globally"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""indoor-air-pollution-deaths-have-declined-in-most-countries"", ""text"": ""Indoor air pollution deaths have declined in most countries"", ""title"": ""Indoor air pollution deaths have declined in most countries"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""death-rates-have-declined-in-almost-all-countries-in-the-world"", ""text"": ""Death rates have declined in almost all countries in the world"", ""title"": ""Death rates have declined in almost all countries in the world"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""deaths-by-age"", ""text"": ""Deaths by age"", ""title"": ""Deaths by age"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""how-to-make-progress-against-indoor-air-pollution"", ""text"": ""How to make progress against indoor air pollution?"", ""title"": ""How to make progress against indoor air pollution?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""indoor-air-pollution-results-from-poor-access-to-clean-cooking-fuels"", ""text"": ""Indoor air pollution results from poor access to clean cooking fuels"", ""title"": ""Indoor air pollution results from poor access to clean cooking fuels"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""poorer-households-have-a-higher-dependence-on-solid-fuels"", ""text"": ""Poorer households have a higher dependence on solid fuels"", ""title"": ""Poorer households have a higher dependence on solid fuels"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""access-to-clean-fuels-for-cooking"", ""text"": ""Access to clean fuels for cooking"", ""title"": ""Access to clean fuels for cooking"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""all-charts"", ""text"": ""Interactive charts on indoor air pollution"", ""title"": ""Interactive charts on indoor air pollution"", ""isSubheading"": false}, {""slug"": ""article-endnotes"", ""text"": ""Endnotes"", ""title"": ""Endnotes"", ""isSubheading"": false}, {""slug"": ""article-citation"", ""text"": ""Citation"", ""title"": ""Citation"", ""isSubheading"": false}, {""slug"": ""article-licence"", ""text"": ""Licence"", ""title"": ""Licence"", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Indoor air pollution is caused by burning solid fuel sources – such as firewood, crop waste, and dung – for cooking and heating."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Burning such fuels, particularly in poor households, results in air pollution that leads to respiratory diseases, which can result in premature death. 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This chart is shown for the global total but can be explored for any country or region using the \""change country or region\"" toggle."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Indoor air pollution is a risk factor for several of the world's leading causes of death, including heart disease, pneumonia, stroke, diabetes, and lung cancer."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In the chart, we see that it is one of the leading risk factors for death globally."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The health impact of indoor air pollution is especially high in low-income countries. If we look at the breakdown for "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/number-of-deaths-by-risk-factor?country=~Low+Income+%28WB%29"", ""children"": [{""text"": ""low-income countries"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" we see that indoor air pollution is among the worst risk factors."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/number-of-deaths-by-risk-factor"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Read more on how researchers estimate the death toll caused by each risk factor here:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1iH_m2GlsBuif80sDwfg0fNGZmpf9X0-TFM5oHQr9fPA/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""text"": [{""text"": ""The global distribution of deaths from indoor air pollution"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the map here, we see the share of annual deaths attributed to indoor air pollution across the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is based on the share of deaths that are attributable to indoor air pollution based on the impact of air pollution on the risk of diseases like cardiovascular disease, lung disease, lung cancer, and others. In other words, it is an estimate of the share of all deaths that would be prevented if indoor air pollution was absent."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-deaths-indoor-pollution"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Death rates are highest across low-income countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Death rates from indoor air pollution give us an accurate comparison of differences in mortality impacts between countries and over time. In contrast to the share of deaths that we studied before, death rates are not influenced by how other causes or risk factors for death are changing."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this map, we see death rates from indoor air pollution across the world. Death rates measure the number of deaths per 100,000 people in a given country or region."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What becomes clear is the large differences in death rates between countries: rates are high in lower-income countries, particularly across Sub-Saharan Africa and Asia."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Compare these rates with those across high-income countries: across North America, rates are below 0.1 deaths per 100,000. That’s a greater than 1000-fold difference."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The issue of indoor air pollution, therefore, has a clear economic split: it is a problem that has almost been entirely eliminated across high-income countries but remains a large environmental and health problem for lower-income countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see this relationship clearly when we plot death rates versus income, as shown "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://ourworldindata.org/grapher/death-rates-from-indoor-air-pollution-vs-gdp-per-capita"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""text"": "". There is a strong negative relationship: death rates decline as countries get richer. This is also broadly true when "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/death-rates-from-indoor-air-pollution-vs-share-of-population-in-absolute-poverty"", ""children"": [{""text"": ""making this comparison"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" between extreme poverty rates and pollution effects."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/death-rate-by-source-from-indoor-air-pollution"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""How has mortality from indoor air pollution changed over time?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Annual deaths from indoor air pollution have declined globally"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Whilst indoor air pollution is still one of the leading risk factors for mortality, particularly in low-income countries, the world has also made significant progress in recent decades."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Globally, the number of annual deaths from indoor air pollution has fallen substantially since 1990. We see this in the visualization, which shows the annual number of deaths attributed to indoor air pollution globally."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This means that despite continued "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/population-and-demography?facet=none&country=OWID_WRL~Low-income+countries~High-income+countries~Upper-middle-income+countries~Lower-middle-income+countries&hideControls=false&Metric=Population&Sex=Both+sexes&Age+group=Total&Projection+Scenario=None"", ""children"": [{""text"": ""population growth"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in recent decades, the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""total"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" number of deaths from indoor air pollution has still declined."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/household-air-pollution-deaths-by-region"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Indoor air pollution deaths have declined in most countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We have seen this progress across a large number of countries. In the scatterplot, you see the comparison of the number of deaths from indoor air pollution in 1990 (shown on the y-axis) versus the number in the latest year (on the x-axis). The grey line here marks where the number of deaths would be equal in both years; countries which lie above the line had a higher number of deaths in 1990, and the opposite is true for countries below the line."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most countries lie above the grey line, meaning most have seen a decline in the number of deaths from indoor air pollution in recent decades."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can explore data on the number of deaths from indoor air pollution across the world "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://ourworldindata.org/grapher/absolute-number-of-deaths-from-household-air-pollution"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/indoor-pollution-deaths-1990-2017"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Death rates have declined in almost all countries in the world"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we know, populations have increased over recent decades, so it might be that deaths from indoor air pollution are increasing in some countries just because there are more people. To account for this, we should look at death rates – the number of deaths per 100,000 people – rather than the total number of deaths from indoor air pollution."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Which countries in the world have made progress in reducing death rates from indoor air pollution in recent decades? All countries have."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the scatterplot here we see the comparison between the death rate from indoor air pollution in 1990 (shown on the y-axis) versus the death rate in the latest year (on the x-axis). The grey line here marks where the death rate would be equal in both years; countries that lie above the line had a higher death rate in 1990, and the opposite is true for countries below the line."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All countries lie above the grey line. This means progress has been made almost everywhere in the world in recent decades."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/death-rate-indoor-pollution-1990-2017"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Deaths by age"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This visualization shows the breakdown of deaths from indoor air pollution by age group."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/deaths-from-indoor-air-pollution-by-age"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""How to make progress against indoor air pollution?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Indoor air pollution results from poor access to clean cooking fuels"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Indoor air pollution is mostly the result of the burning of solid fuels such as crop waste, dung, charcoal, and coal for cooking and heating in households. Burning these fuels produces particulate matter – a major health risk, particularly for respiratory diseases. The burning of such fuels in enclosed spaces, such as small households, is a major risk factor for the exacerbation of these diseases."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Low-income households tend to rely on solid fuels for cooking because cleaner fuels are either unavailable or too expensive. We, therefore, see a strong link between death rates from indoor air pollution and access to clean fuels for cooking."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is shown in the visualization: here, we see that countries with the highest death rate from indoor air pollution are those with very low access to clean fuels (i.e., have a high dependence on solid fuels instead). As access to clean fuels and technologies increases, death rates from household air pollution begin and continue to fall."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/indoor-pollution-death-rates-clean-fuels"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Poorer households have a higher dependence on solid fuels"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Income is a strong determinant of energy access and types of fuel sources."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the figure, we see the World Health Organization (WHO)'s depiction of the 'energy ladder'. It shows how the dominant source of energy changes depending on the level of income."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At low-income levels, households rely mostly on solid traditional fuel sources such as crop waste, dung, and firewood. As incomes rise, this energy mix tends to transition towards charcoal and coal. Only at higher income levels do households shift from harmful solid fuels to cleaner non-solid fuels such as ethanol and natural gas. Electricity is only available for households at a high-income level."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The latest data on this relationship between fuel type and income is shown in the scatterplot. It shows the percentage of households in countries around the world with access to clean fuels and technologies for cooking (on the y-axis) versus the average income in the country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The share of households with access to clean energy in countries below a GDP per capita level of $2,000 per year is typically less than 10%. As countries begin to bridge that gap between low and middle incomes, this share begins to increase until a final transition towards high income, where the majority of households have clean fuels and technologies for cooking."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": ""The-energy-ladder---household-energy-and-development-inextricably-linked-– WHO-(2006)"", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""the-energy-ladder-household-energy-and-development-inextricably-linked-who-2006.png"", ""hasOutline"": false, ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/access-to-clean-fuels-for-cooking-vs-gdp-per-capita"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Access to clean fuels for cooking"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Only 60% of the world has access to clean cooking fuels"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The obvious way to avoid indoor air pollution from solid fuel burning is for households to transition from traditional ways of cooking and heating towards more modern, cleaner methods. This can, for example, be in the form of transitioning towards non-solid fuels such as natural gas, ethanol, or even electric technologies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""'Clean fuels' are defined by emission rate targets and recommendations for and against particular fuel use in the World Health Organization's guidelines for indoor air quality: household fuel combustion."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The WHO recommends against the use of solid fuels, unprocessed coal, and kerosene for indoor cooking since these fuels exceed its emission rate targets. The 'clean fuels' recommended include biogas, ethanol, LPG, natural gas, and electricity. Solar cookstoves can also be an important solution where conditions are suitable."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The map here shows the percentage of households with access to clean fuels and technologies for cooking."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Globally, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/access-to-clean-fuels-and-technologies-for-cooking?tab=chart&country=~OWID_WRL"", ""children"": [{""text"": ""access has been increasing"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "": in 2000, around half of households globally had access; in more recent years, this has increased to seven in ten. Still, this means access to clean fuels is not universal."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This share has been "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/access-to-clean-fuels-and-technologies-for-cooking?tab=chart&country=~Lower-middle-income+countries"", ""children"": [{""text"": ""increasing for most countries with low-to-middle incomes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""; however, rates of increase vary by country and region."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Access is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/access-to-clean-fuels-and-technologies-for-cooking?tab=chart&country=~Sub-Saharan+Africa+%28WB%29"", ""children"": [{""text"": ""lowest across sub-Saharan Africa"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", where a minority of households have access to clean fuels. Progress has been "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/access-to-clean-fuels-and-technologies-for-cooking?tab=chart&country=Sub-Saharan+Africa+%28WB%29~South+Asia+%28WB%29~East+Asia+and+Pacific+%28WB%29"", ""children"": [{""text"": ""much more significant in South Asia and East Asia"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" over recent decades; in both cases, the majority of households now have access."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/access-to-clean-fuels-and-technologies-for-cooking"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Use of solid fuels for cooking is still high, but it is falling"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The burning of solid fuels fills the homes in poorer countries with smoke that kills the world’s poor by causing pneumonia, stroke, heart disease, chronic obstructive pulmonary disease, and lung cancer. The solid fuels responsible for this include wood, crop residues, dung, charcoal, and coal. The solution to this problem is straightforward: shift from solid fuels to modern energy sources."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows that the world is making progress in this direction. In 1980, almost two-thirds of the world’s population used solid fuels for their cooking. Thirty years later, this is down to around 40%. The chart also shows that it is a problem associated with poverty: In richer Europe and North America, the share is much lower than in the rest of the world, and in the high-income countries of the world, the use of solid fuels is entirely a thing of the past."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The use of solid fuels is going down in all of the world’s regions. However, the success of rapidly developing South East Asia is particularly impressive: here, the share fell from 95% to 61%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/population-using-solid-fuels-for-cooking"", ""type"": ""chart"", ""parseErrors"": []}, {""top"": [], ""type"": ""all-charts"", ""heading"": ""Interactive charts on indoor air pollution"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""0f73691bde8a4cd58e67b6fdbb59907b77d46eff"": {""id"": ""0f73691bde8a4cd58e67b6fdbb59907b77d46eff"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""WHO (2014) – Fact sheet N°292 – Household air pollution and health. Updated March 2014. 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"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30752-2/fulltext"", ""children"": [{""text"": ""Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""The Lancet"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""396"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(10258), 1223-1249."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9800b3a477bc284daebb3db4a6e56e61f079c252"": {""id"": ""9800b3a477bc284daebb3db4a6e56e61f079c252"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""url"": ""https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health"", ""children"": [{""text"": ""WHO (2023) Household air pollution"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""linear-topic-page"", ""title"": ""Indoor Air Pollution"", ""authors"": [""Hannah Ritchie"", ""Max Roser""], ""excerpt"": ""Indoor air pollution – caused by the burning of firewood, crop waste, and dung for cooking and heating – is a major health risk of the world's poorest."", ""dateline"": ""This article was first published in November 2014; last revised in March 2024."", ""subtitle"": ""Indoor air pollution – caused by the burning of firewood, crop waste, and dung for cooking and heating – is a major health risk for the world's poorest."", ""sidebar-toc"": true, ""featured-image"": ""indoor-air-pollution-thumbnail.png""}",1,2023-11-10 15:33:57,2022-01-01 15:34:59,2024-03-15 12:37:53,unlisted,ALBJ4Lurjgfy4GKbQOAfClIJ-pLO_f4-VBbPD0B3wK5CleJywL9tcHqc3fCyXhH8UudPRVcoSCC5XF29fGf2ZA,,"Indoor air pollution is caused by burning solid fuel sources – such as firewood, crop waste, and dung – for cooking and heating. The burning of such fuels, particularly in poor households, results in air pollution that leads to respiratory diseases which can result in premature death. The WHO calls indoor air pollution ""the world's largest single environmental health risk.""1 **Related topics** ### undefined undefined https://docs.google.com/document/d/1I8h6_nfCxMv5mPSc0NXekQPyX2pjmuZeTjqDMAnq2to/edit ### undefined undefined https://docs.google.com/document/d/1fYmFTD2zdvt3nPyEvOc0x7wdEylg_LC-JwoXGjvIfTs/edit ### undefined undefined https://docs.google.com/document/d/1IxPaKLVC-rm0doT1JYDB2uFGR2TKNQ7Bq3d6riHCBuU/edit ### undefined undefined https://docs.google.com/document/d/1GRYE-mYiSEnes8rXjK6dCNi4KULLJB8KW03AOPci-b8/edit **[See all interactive charts on indoor air pollution ↓](#all-charts)** --- # Indoor air pollution is one of the leading risk factors for premature death --- ## Indoor air pollution is a leading risk factor for premature death in poor countries Indoor air pollution is one of the world's largest environmental problems – particularly for the [poorest in the world](https://ourworldindata.org/extreme-poverty) who often do not have access to clean fuels for cooking. The _Global Burden of Disease_ is a major global study on the causes and risk factors for death and disease published in the medical journal _The Lancet_.2 These estimates of the annual number of deaths attributed to a wide range of risk factors are shown here. This chart is shown for the global total, but can be explored for any country or region using the ""change country"" toggle. Indoor air pollution is a risk factor for several of the world's leading causes of death, including heart disease, pneumonia, stroke, diabetes and lung cancer.3 In the chart we see that it is one of the leading risk factors for death globally. According to the _Global Burden of Disease_ study 2,313,991 deaths were attributed to indoor pollution in the latest year. Because the IHME data is more recent we rely mostly on IHME data in our work on indoor air pollution. But it's worth noting that the WHO publishes a substantially larger number of indoor air pollution deaths. In 2018 (the latest available data) the WHO estimated 3.8 million deaths.4 The health impact of indoor air pollution is especially high in low-income countries. If we look at the breakdown for countries with a low sociodemographic index – 'Low SDI' on the interactive chart – we see that indoor air pollution is among the worst risk factors. --- # The global distribution of deaths from indoor air pollution --- ## 4% of global deaths are attributed to indoor air pollution Indoor air pollution was attributed to an estimated 2.3 million deaths in the latest year. This means that indoor air pollution was responsible for 4% of global deaths. In the map here we see the share of annual deaths attributed to indoor air pollution across the world. When we compare the share of deaths attributed to indoor air pollution either over time or between countries, we are not only comparing the extent of indoor air pollution, but its severity _in the context_ of other risk factors for death. Indoor air pollution's share does not only depend on how many die prematurely from it, but what else people are dying from and how this is changing. When we look at the share dying from indoor air pollution, figures are high across the lowest-income countries in Sub-Saharan Africa, but not markedly different from countries across Asia or Latin America. There, the severity of indoor air pollution – expressed as the share of deaths – has been masked by the role of other risk factors at low-incomes, such as low access to [safe water](https://ourworldindata.org/water-access), poor [sanitation](https://ourworldindata.org/sanitation) and unsafe sex which is a risk factor for [HIV/AIDS](https://ourworldindata.org/hiv-aids). ## Death rates are highest across low income countries Death rates from indoor air pollution give us an accurate comparison of differences in its mortality impacts between countries and over time. In contrast to the share of deaths that we studied before, death rates are not influenced by how other causes or risk factors for death are changing. In this map we see death rates from indoor air pollution across the world. Death rates measure the number of deaths per 100,000 people in a given country or region. What becomes clear is the large differences in death rates between countries: rates are high in lower-income countries, particularly across Sub-Saharan Africa and Asia. Compare these rates with those across high-income countries: across North America rates are below 0.1 deaths per 100,000. That’s a greater than 1000-fold difference. The issue of indoor air pollution therefore has a clear economic split: it is a problem that has almost been entirely eliminated across high-income countries, but remains a large environmental and health problem at lower incomes. We see this relationship clearly when we plot death rates versus income, as shown **[here](https://ourworldindata.org/grapher/death-rates-from-indoor-air-pollution-vs-gdp-per-capita)**. There is a strong negative relationship: death rates decline as countries get richer. This is also true when [make this comparison](https://ourworldindata.org/grapher/death-rates-from-indoor-air-pollution-vs-share-of-population-in-absolute-poverty) between extreme poverty rates and pollution effects. --- # How has mortality from indoor air pollution changed over time? --- ## Annual deaths from indoor air pollution have declined globally Whilst indoor air pollution is still one of the leading risk factors for mortality, and the largest risk factor at low incomes, the world has also made significant progress in recent decades. Globally, the number of annual deaths from indoor air pollution has fallen substantially since 1990. We see this in the visualization, which shows the annual number of deaths attributed to indoor air pollution globally. This means that despite continued [population growth](https://ourworldindata.org/world-population-growth) in recent decades, the _total_ number of deaths from indoor air pollution has still declined. ## Indoor air pollution deaths have declined in most countries We have seen this progress across a large number of countries. In the scatterplot you see the comparison of the number of deaths from indoor air pollution in 1990 (shown on the y-axis) versus the number in the latest year (on the x-axis). The grey line here marks where the number of deaths would be equal in both years; countries which lie above the line had a higher number of deaths in 1990; and the opposite is true for countries below the line. Most countries lie above the grey line, meaning most have seen a decline in the number of deaths from indoor air pollution in recent decades. You can explore data on the number of deaths from indoor air pollution across the world **[here](https://ourworldindata.org/grapher/absolute-number-of-deaths-from-household-air-pollution)**. ## Death rates have declined in almost all countries in the world Which countries in the world have made progress on tackling indoor air pollution in recent decades? Almost all countries have. In the scatterplot here we see the comparison between the death rate from indoor air pollution in 1990 (shown on the y-axis) versus the death rate in the latest year (on the x-axis). The grey line here marks where the number of deaths would be equal in both years; countries which lie above the line had a higher number of deaths in 1990; and the opposite is true for countries below the line. Almost all countries lie above the grey line. This means progress has been made almost everywhere in the world in recent decades. ## Deaths by age This visualization shows the breakdown of deaths from indoor air pollution by age group. --- # How to make progress against indoor air pollution? --- ## Indoor air pollution results from poor access to clean cooking fuels Indoor air pollution results from the burning of solid fuels such as crop waste, dung, charcoal and coal for cooking and heating in households. Burning these fuels produces particulate matter – a major health risk, particularly for respiratory diseases. The burning of such fuels in enclosed spaces such as small households is a major risk factor for exacerbation of these diseases. Low-income households tend to rely on solid fuels for cooking because cleaner fuels are either unavailable or too expensive. We therefore see a strong link between death rates from indoor air pollution and access to clean fuels for cooking. This is shown in the visualization: here we see that countries with the highest death rate from indoor air pollution are those with very low access to clean fuels (i.e. have a high dependence on solid fuels instead). As access to clean fuels and technologies increases, death rates from household air pollution begin and continue to fall. ## Poorer households have a higher dependence on solid fuels Income is a strong determinant of energy access and types of fuel sources. In the figure we see the World Health Organization (WHO)'s depiction of the 'energy ladder'. It shows how the dominant source of energy changes depending on the level of income. At low income levels households rely mostly on solid traditional fuel sources such as crop waste, dung, and firewood. As incomes rise, this energy mix tends to transition towards charcoal and coal. Only at higher income levels do households shift from the harmful solid fuels to cleaner non-solid fuels such as ethanol and natural gas. Electricity is only available for households at a high income level. The latest data on this relationship between fuel type and income is shown in the scatterplot. It shows the percentage of households in countries around the world with access to clean fuels and technologies for cooking (on the y-axis) versus the average income in the country. The share of households with access to clean energy in countries below a GDP per capita level of $2,000 per year is typically less than 10%. As countries begin to bridge that gap between low and middle incomes, this share begins to increase until a final transition towards high-income where the majority of households have clean fuels and technologies for cooking. ## Access to clean fuels for cooking ### Only 60% of the world has access to clean cooking fuels The obvious way to avoid indoor air pollution from solid fuel burning is for households to transition from traditional ways of cooking and heating towards more modern, cleaner methods. This can, for example, be in the form of transitioning towards non-solid fuels such as natural gas, ethanol or even electric technologies. 'Clean fuels' are defined by emission rate targets and recommendations for and against particular fuel use in the World Health Organization's guidelines for indoor air quality: household fuel combustion.5 The WHO recommends against the use of solid fuels, unprocessed coal and kerosene for indoor cooking since these fuels exceed its emission rate targets. The 'clean fuels' is recommends include biogas, ethanol, LPG, natural gas and electricity. Solar cook stoves can also be an important solution where conditions are suitable. The map here shows the percentage of households with access to clean fuels and technologies for cooking. Globally, access has been increasing: in 2000, 49% of households had access; by 2016 this was 60%. Still, this means access across the world remains low. Less than two-thirds of households have access to clean cooking fuels. This share has been increasing for most countries at low-to-middle incomes, however, rates of increase vary by country and region. Access is lowest across Sub-Saharan Africa where only 14% of households in 2016 had access. Progress has been much more significant in South Asia and East Asia over the last decade, with 18 percent and 16% of additional households gaining access, respectively. ### Use of solid fuels for cooking is still high, but it is falling The burning of solid fuels fills the houses and huts in poorer countries with smoke that kills the world’s poor by causing pneumonia, stroke, heart disease, chronic obstructive pulmonary disease, and lung cancer. The solid fuels responsible for this include wood, crop residues, dung, charcoal, and coal. The solution for this problem is straightforward: shift from solid fuels to modern energy sources. The chart shows that the world is making progress in this direction. In 1980 almost two thirds of the world’s population used solid fuels for their cooking. 30 years later this is down to 41%. The chart also shows that it is a problem associated with poverty: In richer Europe and North America the share is much lower than in the rest of the world; and in the high income countries of the world the use of solid fuels is entirely a thing of the past. The use of solid fuels is going down in all of the world’s regions. But the success rapidly developing South East Asia is particularly impressive: here the share fell from 95% to 61%. --- # Methodology --- ## How are deaths caused by pollution estimated? Indoor air pollution has a wide range of negative health impacts, which can lead to morbidity but also in many cases, mortality. The table features summary data from the World Health Organization (WHO) on the extent of proven links between indoor air pollution and potential health outcomes. These health outcomes range from respiratory infections to chronic obstruction pulmonary disease (COPD) to lung cancer and have varying effects on the population depending on factors such as age and sex. Health impacts vary in terms of the strength of evidence linking outcomes with indoor air pollution. The WHO define 'strong evidence' based on results from a range of studies on solid fuel using in developing countries with biochemical and laboratory evidence of health impacts; 'moderate I' has at least three studies showing strong evidence for specific age and sex groups; and 'moderate II' has at least three studies showing potential links but with more limited evidence. The WHO suggests that the deaths caused by indoor air pollution break down as follows:6 * 27% are due to pneumonia (acute lower respiratory tract infection) * 18% from stroke * 27% from ischaemic heart disease * 20% from chronic obstructive pulmonary disease (COPD) * 8% from lung cancer. ##### Health impacts of indoor air pollution – WHO (2006)7 |**Health outcome**|**Evidence**|**Population**|**Relative risk**|**Relative risk (95% confidence interval)**|**Sufficient or insufficient evidence?**| |Acute infections of the lower respiratory tract|Strong|Children aged 0-4 years|2.3|1.9-2.7|Sufficient| |Chronic obstructive pulmonary disease|Strong|Women aged ≥ 30 years|3.2|2.3-4.8|Sufficient| ||Moderate I|Men aged ≥ 30 years|1.8|1.0-3.2|Sufficient| |Lung cancer (coal)|Strong|Women aged ≥ 30 years|1.9|1.1-3.5|Sufficient| ||Moderate I|Men aged ≥ 30 years|1.5|1.0-2.5|Sufficient| |Lung cancer (biomass)|Moderate II|Women aged ≥ 30 years|1.5|1.0-2.1|Insufficient| |Asthma|Moderate II|Children aged 5-14 years|1.6|1.0-2.5|Insufficient| ||Moderate II|Adults aged ≥ 15 years|1.2|1.0-1.5|Insufficient| |Cataracts|Moderate II|Adults aged ≥ 15 years|1.3|1.0-1.7|Insufficient| |Tuberculosis|Moderate II|Adults aged ≥ 15 years|1.5|1.0-2.4|Insufficient| [WHO (2014) – Frequently Asked Questions – Ambient and Household Air Pollution and Health](https://www.who.int/phe/health_topics/outdoorair/databases/faqs_air_pollution.pdf). Update 2014 Murray, C. J., Aravkin, A. Y., Zheng, P., Abbafati, C., Abbas, K. M., Abbasi-Kangevari, M., ... & Borzouei, S. (2020). [Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30752-2/fulltext). _The Lancet_, _396_(10258), 1223-1249. WHO (2014) – Fact sheet N°292 – Household air pollution and health. Updated March 2014. Online [here](http://www.who.int/mediacentre/factsheets/fs292/en/). WHO (2018) – [Fact Sheet - Household air pollution and health](https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health) World Health Organization. (2014). _[WHO guidelines for indoor air quality: household fuel combustion](https://www.who.int/airpollution/publications/household-fuel-combustion/en/)_. World Health Organization. Based on WHO (2018) – [Fact Sheet - Household air pollution and health](https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health) This table is taken from WHO (2006) – Fuel for life: household energy and health. Online here. http://apps.who.int/iris/handle/10665/43421 Notes by the original source: 1. Strong evidence: Many studies of solid fuel use in developing countries, supported by evidence from studies of active and passive smoking, urban air pollution and biochemical or laboratory studies. Moderate evidence: At least three studies of solid fuel use in developing countries, supported by evidence from studies on active smoking and on animals. Moderate I: strong evidence for specific age/sex groups. Moderate II: limited evidence. 2. The relative risk indicates how many times more likely the disease is to occur in people exposed to indoor air pollution than in unexposed people. 3. The confidence interval represents an uncertainty range. Wide intervals indicate lower precision; narrow intervals indicate greater precision.",Indoor Air Pollution 1qmt0yBlkmJLezO19LXUQ2t7GmtSlIDoanX6FjpCDyUk,financing-education,article,"{""toc"": [{""slug"": ""when-did-the-provision-of-education-first-become-a-public-policy-priority"", ""text"": ""When did the provision of education first become a public policy priority?"", ""title"": ""When did the provision of education first become a public policy priority?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-did-the-us-finance-the-expansion-of-public-education"", ""text"": ""How did the US finance the expansion of public education?"", ""title"": ""How did the US finance the expansion of public education?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-did-france-finance-the-expansion-of-public-education"", ""text"": ""How did France finance the expansion of public education?"", ""title"": ""How did France finance the expansion of public education?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""in-the-us-growth-in-education-expenditure-was-characterized-by-growth-specifically-in-the-public-sector"", ""text"": ""In the US growth in education expenditure was characterized by growth specifically in the public sector"", ""title"": ""In the US growth in education expenditure was characterized by growth specifically in the public sector"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""when-did-the-expansion-of-basic-education-become-a-global-phenomenon"", ""text"": ""When did the expansion of basic education become a global phenomenon?"", ""title"": ""When did the expansion of basic education become a global phenomenon?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""education-inequality-is-falling-around-the-world"", ""text"": ""Education inequality is falling around the world"", ""title"": ""Education inequality is falling around the world"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""education-inequality-can-decline-rapidly-across-all-levels-of-education-south-korea-is-an-example"", ""text"": ""Education inequality can decline rapidly across all levels of education – South Korea is an example"", ""title"": ""Education inequality can decline rapidly across all levels of education – South Korea is an example"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""is-funding-for-education-expanding"", ""text"": ""Is funding for education expanding?"", ""title"": ""Is funding for education expanding?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""is-additional-funding-for-education-taking-resources-from-other-sectors"", ""text"": ""Is additional funding for education taking resources from other sectors?"", ""title"": ""Is additional funding for education taking resources from other sectors?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""european-countries-tend-to-assign-a-lower-share-of-public-budgets-to-education-relative-to-the-amount-of-their-income-that-is-devoted-to-education"", ""text"": ""European countries tend to assign a lower share of public budgets to education, relative to the amount of their income that is devoted to education"", ""title"": ""European countries tend to assign a lower share of public budgets to education, relative to the amount of their income that is devoted to education"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""in-european-countries-the-weight-of-primary-education-within-total-education-spending-is-lower-than-in-other-countries"", ""text"": ""In European countries the weight of primary education within total education spending is lower than in other countries"", ""title"": ""In European countries the weight of primary education within total education spending is lower than in other countries"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""in-high-income-countries-households-shoulder-a-larger-share-of-education-expenditures-at-higher-education-levels-than-at-lower-levels-but-in-low-income-countries-this-is-not-the-case"", ""text"": ""In high-income countries, households shoulder a larger share of education expenditures at higher education levels than at lower levels – but in low-income countries, this is not the case"", ""title"": ""In high-income countries, households shoulder a larger share of education expenditures at higher education levels than at lower levels – but in low-income countries, this is not the case"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""primary-education-continues-to-be-publicly-funded-in-industrialized-countries"", ""text"": ""Primary education continues to be publicly funded in industrialized countries"", ""title"": ""Primary education continues to be publicly funded in industrialized countries"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""publicly-funded-pre-primary-education-is-more-strongly-developed-in-the-european-countries-of-the-oecd"", ""text"": ""Publicly funded pre-primary education is more strongly developed in the European countries of the OECD"", ""title"": ""Publicly funded pre-primary education is more strongly developed in the European countries of the OECD"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""where-does-funding-for-education-go-to"", ""text"": ""Where does funding for education go to?"", ""title"": ""Where does funding for education go to?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""what-drives-current-expenditure-on-education"", ""text"": ""What drives current expenditure on education?"", ""title"": ""What drives current expenditure on education?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""education-financing-in-developing-countries-has-been-bolstered-by-development-assistance"", ""text"": ""Education financing in developing countries has been bolstered by development assistance"", ""title"": ""Education financing in developing countries has been bolstered by development assistance"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""the-share-of-development-assistance-for-education-going-to-sub-saharan-africa-has-decreased"", ""text"": ""The share of development assistance for education going to Sub-saharan Africa has decreased"", ""title"": ""The share of development assistance for education going to Sub-saharan Africa has decreased"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""development-assistance-priorities-have-the-ability-to-increase-or-reduce-expenditure-inequalities"", ""text"": ""Development assistance priorities have the ability to increase or reduce expenditure inequalities"", ""title"": ""Development assistance priorities have the ability to increase or reduce expenditure inequalities"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""the-big-picture"", ""text"": ""The big picture"", ""title"": ""The big picture"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""why-do-governments-finance-education"", ""text"": ""Why do governments finance education?"", ""title"": ""Why do governments finance education?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""do-countries-that-spend-more-public-resources-on-education-tend-to-have-better-education-outcomes"", ""text"": ""Do countries that spend more public resources on education tend to have better education outcomes?"", ""title"": ""Do countries that spend more public resources on education tend to have better education outcomes?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""does-cross-country-variation-in-government-education-expenditure-explain-cross-country-differences-in-education-outcomes"", ""text"": ""Does cross-country variation in government education expenditure explain cross-country differences in education outcomes?"", ""title"": ""Does cross-country variation in government education expenditure explain cross-country differences in education outcomes?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""school-inputs"", ""text"": ""School inputs"", ""title"": ""School inputs"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""each-education-system-is-different-but-improving-teacher-quality-is-often-more-effective-in-improving-learning-outcomes-than-increasing-the-number-of-teachers-per-pupil"", ""text"": ""Each education system is different, but improving teacher quality is often more effective in improving learning outcomes than increasing the number of teachers per pupil"", ""title"": ""Each education system is different, but improving teacher quality is often more effective in improving learning outcomes than increasing the number of teachers per pupil"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""remedial-teaching-can-yield-substantial-improvements-in-learning-outcomes"", ""text"": ""Remedial teaching can yield substantial improvements in learning outcomes"", ""title"": ""Remedial teaching can yield substantial improvements in learning outcomes"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""are-pay-for-performance-teacher-contracts-an-effective-instrument-to-improve-learning-outcomes"", ""text"": ""Are pay-for-performance teacher contracts an effective instrument to improve learning outcomes?"", ""title"": ""Are pay-for-performance teacher contracts an effective instrument to improve learning outcomes?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""household-inputs"", ""text"": ""Household inputs"", ""title"": ""Household inputs"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""school-attendance-and-student-effort-are-responsive-to-incentives"", ""text"": ""School attendance and student effort are responsive to incentives"", ""title"": ""School attendance and student effort are responsive to incentives"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""targeting-health-problems-can-be-a-particularly-cost-effective-way-of-increasing-school-attendance"", ""text"": ""Targeting health problems can be a particularly cost-effective way of increasing school attendance"", ""title"": ""Targeting health problems can be a particularly cost-effective way of increasing school attendance"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""how-important-are-pre-school-investments"", ""text"": ""How important are pre-school investments?"", ""title"": ""How important are pre-school investments?"", ""supertitle"": """", ""isSubheading"": true}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""In most countries basic education is nowadays perceived not only as a right, but also as a duty – governments are typically expected to ensure access to basic education, while citizens are often required by law to attain education up to a certain basic level."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This was not always the case: the advancement of these ideas began in the mid-19th century, when most of today’s industrialized countries started expanding primary education, mainly through public finances and government intervention. Data from this early period shows that government funds to finance the expansion of education came from a number of different sources, but taxes at the local level played a crucial role. The historical role of local funding for public schools is important to help us understand changes – or persistence – in regional inequalities."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The second half of the 20th century marked the beginning of education expansion as a global phenomenon. Available data shows that by 1990 government spending on education as a share of national income in many developing countries was already close to the average observed in developed countries."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This global education expansion in the 20th century resulted in a historical reduction in education inequality across the globe: in the period 1960-2010 education inequality went down every year, for all age groups and in all world regions. Recent estimates of education inequality across age groups suggest that further reductions in schooling inequality are still to be expected within developing countries."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Recent cross-country data from UNESCO tells us that the world is expanding government funding for education today, and these additional public funds for education are not necessarily at the expense of other government sectors. Yet behind these broad global trends, there is substantial cross-country – and cross-regional – heterogeneity. In high-income countries, for instance, households shoulder a larger share of education expenditures at higher education levels than at lower levels – but in low-income countries, this is not the case."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Following the agreement of the Millennium Development Goals, the first decade of the 21st century saw an important increase in international financial flows under the umbrella of development assistance. Recent estimates show that development assistance for education has stopped growing since 2010, with notable aggregate reductions in flows going to primary education. These changes in the prioritization of development assistance for education across levels and regions can have potentially large distributional effects, particularly within low-income countries that depend substantially on this source of funding for basic education."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When analyzing correlates, determinants and consequences of education consumption, the macro data indicates that national expenditure on education does not explain well cross-country differences in learning outcomes. This suggests that for any given level of expenditure, the output achieved depends crucially on the mix of many inputs."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Available evidence specifically on the importance of school inputs to produce education, suggests that learning outcomes may be more sensitive to improvements in the quality of teachers, than to improvements in class sizes. Regarding household inputs, the recent experimental evidence suggests that interventions that increase the benefits of attending school (e.g. conditional cash transfers) are particularly likely to increase student time in school; and that those that incentivize academic effort (e.g. scholarships) are likely to improve learning outcomes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Policy experiments have also shown that preschool investment in demand-side inputs leads to large positive impacts on education – and other important outcomes later in life. The environment that children are exposed to early in life, plays a crucial role in shaping their abilities, behavior, and talents."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Historical perspective on financing education"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""When did the provision of education first become a public policy priority?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Governments around the world are nowadays widely perceived to be responsible for ensuring the provision of accessible quality education. This is a recent social achievement. The advancement of the idea to provide education for more and more children only began in the mid-19th century, when most of today’s industrialized countries started expanding primary education."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization, plotting public expenditure on education as a share of Gross Domestic Product (GDP) for a number of early-industrialized countries, shows that this expansion took place mainly through public funding. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/global-education"", ""children"": [{""text"": ""Our topic page on global education"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" provides details regarding how this expansion in funding materialized in better education outcomes for these countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/total-government-expenditure-on-education-gdp?tab=chart&country=USA~GBR~ESP~FRA~DEU~NOR~SWE"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""How did the US finance the expansion of public education?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Public schools in the US educate more than 90% of all children enrolled in elementary and secondary schools."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is the result of a process of education expansion that relied heavily on public funding, particularly from local governments. The visualization shows the sources of revenues for public schools in the US over the last 120 years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As can be seen, states and localities are – and have always been – the main sources of funding for public primary education in the US. In fact, we observe three broad periods in this graph: there is first a period of stable revenues until 1920, then a period of sharp growth and decline during the interwar years, and then a period of substantial growth since the Second World War, slowing down in the 1970s. In all these periods, federal funding was always very small."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/revenues-for-public-schools-by-source-us"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Disaggregated data from the last couple of decades gives further insights into the specific sources of local revenues for schools in the US: the largest part comes from property taxes (about 80% of local revenues came from property taxes in 2013), while only a very small part comes from fees and donations (private funding for public schools, which is considered a local revenue, amounted to less than 2% of total public school revenues in 2013). This heavily decentralized system relying on property taxes has the potential to create large inequalities in education since public schools in affluent urban areas are able to raise more funding from local revenues. Indeed, a significant part of the debate on education inequalities in the US today focuses on the importance of increasing progressive federal spending to reduce inequalities in public school funding."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""How did France finance the expansion of public education?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The case of the US above shows that funding for public schools has been historically a responsibility of local governments. In other countries, such as France, the expansion of public education also took place initially with resources from local governments, but relatively quickly the fiscal burden was shifted to the national level. In France, this transition was associated with a sharp jump towards universal access and a concomitant reduction in regional inequalities."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization from Lindert (2004)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" provides evidence of the French experience. As we can see there are three distinct periods: education spending was initially low and mainly private, then in 1833 funding began growing with local resources after the introduction of a law liberating communes to raise more local taxes for schools, and finally in 1881 the national government took over most of the financial responsibility after the introduction of a new law that abolished all fees and tuition charges in public elementary schools. In the source book, Lindert (2004) provides further evidence of how this transition towards centrally funded public education reduced north-south inequalities in France."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": ""Lindert (2004) France_EarlyEducation_Breakdown"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Sources of funds for France’s public primary schools, 1820–1913 – Figure 5.5 in Lindert (2004)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Lindert-2004-France_EarlyEducation_Breakdown.png"", ""parseErrors"": []}, {""text"": [{""text"": ""In the US growth in education expenditure was characterized by growth specifically in the public sector"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A comparison of expenditure between public and private education institutions is helpful to contextualize the role the public sector played in the process of education expansion in industrialized countries. The following graph does this using data from the National Center for Education Statistics in the US."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It shows that during the years 1950-1970 – a period of substantial growth in education expenditure in the US – expenditure grew "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""specifically"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" in the public sector."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/us-education-expenditure-as-share-of-gdp-public-and-private"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""When did the expansion of basic education become a global phenomenon?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The second half of the 20th century marked the beginning of education expansion as a global phenomenon. The visualization shows government expenditure on education as a share of national income for a selection of low and middle-income countries, together with the corresponding average for high-income countries, for more than the last half-century. As can be seen, spending on education in many developing countries has become similar to the average observed in developed countries in recent decades."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/total-government-expenditure-on-education-gdp?tab=chart&country=High-income+countries~IND~CHN~BRA~MEX~IDN~COD~ETH"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is important to point out that the remark above makes reference to convergence in expenditure relative to "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""income"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". To the extent that low-income countries remain poorer than high-income countries, gaps in levels of expenditure per pupil are persistently large. Indeed, cross-country heterogeneity in education expenditure per pupil is currently much higher than heterogeneity in expenditure as a share of GDP."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" One factor contributing to the slower convergence of expenditure per pupil in real terms is the fact that teachers' salaries – the main component of education expenditure, as discussed below – are much higher in high-income countries because labor has a higher opportunity cost in these countries. In general, the opportunity cost of labor is a key variable that governments in developing countries should factor in when deciding whether to expand education now, rather than later."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Education inequality is falling around the world"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""An important consequence of the global education expansion is a reduction in education inequality across the globe. The following visualization shows this through a series of graphs plotting changes in the Gini coefficient of the distribution of years of schooling across different world regions. The Gini coefficient is a measure of inequality and higher values indicate higher inequality – you can read about the definition and estimation of Gini coefficients "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/what-is-the-gini-coefficient"", ""children"": [{""text"": ""in our related article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The time-series chart shows inequality by age group."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It can be seen that as inequality is falling over time, the level of inequality is higher for older generations than it is for younger generations. We can also see that in the reference period education inequality went down every year, for all age groups and in all world regions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Have gains from historical education expansion fully materialized? The breakdown by age gives us a view into the future: as inequality is lower among today's younger generations, we can expect the decline of inequality to continue in the future. Thus, further reductions in education inequality are still to be expected within developing countries; and if the expansion of global education can be continued, we can speed up this important process of global convergence."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": ""Cuaresma etal(2013) edu_gini_1960_2010"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Education Gini coefficients by world region for selected age groups, 1960- 2010 – Figure 4 in Crespo Cuaresma et al. (2013)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Cuaresma-etal2013-edu_gini_1960_2010-.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Education inequality can decline rapidly across all levels of education – South Korea is an example"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The experience of South Korea shows that it is possible to reduce education inequality rapidly across all levels of education."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization shows two graphs comparing the concentration of years of education in South Korea between the years 1970 and 2010. To be precise, each of these graphs shows an education Lorenz curve: a plot showing the cumulative percentage of the schooling years across all levels of education on the vertical axis, and the cumulative percentage of the population on the horizontal axis."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As can be seen, in 2010 education was much less concentrated than in 1970, not only because there was a smaller share of individuals without schooling (shown at the bottom of the chart), but also because there was a smaller share of individuals concentrating large proportions of school-years at higher levels of education. Indeed, in only 40 years South Korea was able to double the mean years of schooling (from 6 to 12 years) and at the same time get remarkably close to the 45-degree line marking the hypothetical scenario of perfect equality of schooling."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": ""inequality-of-education-south-korea-lorenz-curves"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Inequality of Educational Attainment in South Korea 1970 and 2010"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Inequality-of-Education-South-Korea-Lorenz-Curves.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Financing of education across the world"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Is funding for education expanding?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The last two decades have not a clear trend in the share of income that countries devote to education."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following chart plots trends in public expenditure on education as a share of GDP. We can see an upward trend in some countries, but a downward trend in others."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, as incomes – measured by GDP per capita – are generally increasing around the world, this means that the total amount of global resources spent on education is increasing in absolute terms."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/total-government-expenditure-on-education-gdp?tab=chart&time=2000..latest"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Is additional funding for education taking resources from other sectors?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization shows government expenditure on education as a share of total government expenditure. The available data also does not suggest a discernible global pattern here."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The data does suggest, however, that there is large and persistent cross-country heterogeneity in the relative importance of education vis-a-vis other sectors, even within developing countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-education-in-government-expenditure?tab=chart&time=2000..latest"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""European countries tend to assign a lower share of public budgets to education, relative to the amount of their income that is devoted to education"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Generally speaking, countries that spend a large share of their income on education also tend to prioritize education highly within their budgets."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization presents a snapshot of government spending on education around the world. Specifically, this graph plots government expenditure on education as a share of GDP on the horizontal axis, and government expenditure on education as a share of total government expenditure on the vertical axis."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we can see, there is a positive correlation, but regional differences are stark: for almost every level of spending as a share of GDP along the horizontal axis, countries in Europe spend a smaller budget share on education."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-edu-spending-gdp-vs-share-edu-total-spending"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""In European countries the weight of primary education within total education spending is lower than in other countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In comparison to countries where education started expanding later, European countries tend to assign relatively more of their government education budgets to the secondary and tertiary levels, while at the same time devoting relatively less of their general government budgets to education as a whole."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This can be appreciated in the following visualization, where the prioritization of primary education (i.e. the share of primary education within the education budget) is plotted against the overall prioritization of education (i.e. the share of education within the entire government budget)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It can be seen that European countries are mostly located in the upper left. There is a weak positive correlation between the variables, both across all countries and across European countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/primary-edu-spending-vs-overall-edu-spending"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""In high-income countries, households shoulder a larger share of education expenditures at higher education levels than at lower levels – but in low-income countries, this is not the case"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization shows the percentage of total education expenditures contributed directly by households in 15 high-income countries and 15 low or middle-income countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The top chart in this figure, corresponding to high-income countries, shows a very clear pattern: households contribute the largest share of expenses in tertiary education, and the smallest share in primary education. Roughly speaking, this pattern tends to be progressive, since students from wealthier households are more likely to attend tertiary education, and those individuals who attend tertiary education are likely to perceive large private benefits."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In contrast, the bottom chart shows a very different picture: in several low-income countries households contribute proportionally more to primary education than to higher levels. Such distribution of private household contributions to education is regressive."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": ""UNICEF Private Education Expenditure Levels"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Percentage of total education expenditures contributed directly by households in 30 countries, grouped by country income – Figure 32 in "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.unicef.org/publications/files/Investment_Case_for_Education_and_Equity_FINAL.pdf"", ""children"": [{""text"": ""The Investment Case for Education and Equity (UNICEF - 2015)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""filename"": ""UNICEF-Private-Education-Expenditure-Levels.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Recent funding structures in OECD countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Primary education continues to be publicly funded in industrialized countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We have already mentioned that those countries that pioneered the expansion of primary education in the 19th century – all of which are current OECD member states – relied heavily on public funding to do so. Today, public resources still dominate funding for the primary, secondary, and post-secondary non-tertiary education levels in these countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The visualization presents OECD-average expenditure on education institutions by source of funds."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/average-oecd-education-expenditure-by-source-of-funding-gdp"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Publicly funded pre-primary education is more strongly developed in the European countries of the OECD"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""High-income countries tend to have better-developed pre-primary education systems than lower-income countries. However, within high-income countries, there is substantial heterogeneity in the extent to which pre-primary education is publicly financed."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The visualization presents expenditure on pre-primary educational institutions as a share of GDP across the OECD."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As can be seen, publicly funded pre-primary education tends to be more strongly developed in Europe than in the non-European countries of the OECD."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": ""OECD_Expenditure_Pre-primary"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Expenditure on pre-primary educational institutions (% of GDP), OECD, 2012 – Figure C2.4 in "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.keepeek.com/Digital-Asset-Management/oecd/education/education-at-a-glance-2015_eag-2015-en#page1"", ""children"": [{""text"": ""Education at a Glance (2015)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""filename"": ""OECD_Expenditure_Pre-primary.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Where does funding for education go to?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The largest part of funding devoted to education in OECD countries goes to finance current expenditures, mainly compensation of staff – specifically, teachers. The following two charts, taken from the OECD's report "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.keepeek.com/Digital-Asset-Management/oecd/education/education-at-a-glance-2015_eag-2015-en#page1"", ""children"": [{""text"": ""Education at a Glance (2015)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", highlight the labor-intensive nature of education. In the lower levels of education (i.e. primary, secondary, and post-secondary non-tertiary) the share of current expenditure is very large and exhibits little cross-country variation – between 90 and 97 percent of total expenditure corresponds to current expenditure across all of the OECD countries. In higher levels of education (i.e. tertiary) there is more cross-country variation, but current expenditure still dominates by a large margin across all countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": ""OECD_Expenditure_Education_Resources"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Distribution of current and capital expenditure on educational institutions – Figure B6.2 in "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.keepeek.com/Digital-Asset-Management/oecd/education/education-at-a-glance-2015_eag-2015-en#page1"", ""children"": [{""text"": ""Education at a Glance (2015)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""filename"": ""OECD_Expenditure_Education_Resources-.png"", ""parseErrors"": []}, {""text"": [{""text"": ""What drives current expenditure on education?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the figures above we noted the importance of current expenditure in the production of education. The following table provides further details regarding the type of expenditures that comprise current spending. Specifically, this chart shows a breakdown of expenditure for tertiary-level institutions in the US (public and private), during the period 1980-1997. It shows that instruction accounts for almost half of expenditure; and while there are some small differences across sectors, there is a fair amount of stability in expenditures across time. This serves as a benchmark for lower education levels, where instruction takes an even larger share of expenditure."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-15"", ""children"": [{""children"": [{""text"": ""15"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""alt"": ""US_CurrentExpenditure_Types"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Percent distribution of college and university current expenditures in the US, by control over time – Table 8 in Welch and Hanushek (2006)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-16"", ""children"": [{""children"": [{""text"": ""16"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""US_CurrentExpenditure_Types.png"", ""parseErrors"": []}, {""text"": [{""text"": ""International financing flows"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Education financing in developing countries has been bolstered by development assistance"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Following the agreement of the Millennium Development Goals, the first decade of the 21st century saw an important increase in international financial flows under the umbrella of development assistance (often also called development aid, or simply 'aid')."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following chart shows total OECD development assistance flows for education by level, in constant 2013 US dollars, for the period 2002-2013. As it can be seen, there are two distinct periods: in 2003-2010 flows for education increased substantially, more than doubling in real terms across all levels of education; and in the years 2010-2013 funding for basic education "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""decreased"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", while funding for secondary and post-secondary education remained relatively constant. For many low-income countries, where development assistance contributes a substantial share of funding for education, this marked change in trends is important. As a reference, in 2012 development assistance accounted for more than 20 percent of all domestic spending on basic education in recipient low-income countries."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-17"", ""children"": [{""children"": [{""text"": ""17"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""alt"": ""EducationAidWatch_ODA_Education"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Total development assistance for education by level, 2003-2013 – Figure 5 in the report "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.campaignforeducation.org/docs/reports/Education%20Aid%20Watch_2015_EN_WEB.pdf"", ""children"": [{""text"": ""Education Aid Watch 2015"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""filename"": ""EducationAidWatch_ODA_Education.png"", ""parseErrors"": []}, {""text"": [{""text"": ""The share of development assistance for education going to Sub-saharan Africa has decreased"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The reductions in development assistance funds for primary education have been coupled with important changes in regional priorities. Specifically, the share of development assistance for primary education going to sub-Saharan Africa has been decreasing sharply since the agreement of the Millennium Development Goals."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following chart shows this: sub-Saharan Africa’s share in total aid to primary education declined from 52 percent in 2002 to 30 percent in 2013, while the continent’s share in the total number of out-of-school children rose from 46 percent to 57 percent."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": ""Brookings_ODA_EduAfrica"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Share of primary education disbursements from development assistance going to Sub-Saharan Africa, 2002-2013 – Figure 2.6 in Steer and Smith (2015)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-17"", ""children"": [{""children"": [{""text"": ""17"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Brookings_ODA_EduAfrica.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This pattern is something specific to the education sector within the broader development assistance landscape: in the healthcare sector, the overall slowdown of flows started a couple of years later, was less abrupt, and affected proportionally less the sub-Saharan countries."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-18"", ""children"": [{""children"": [{""text"": ""18"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Indeed, recent studies further highlight that development assistance for education is significantly different from assistance for healthcare in other ways: the education sector attracts less earmarked funding through multilaterals, and includes a smaller proportion of resources that developing governments can directly control for programming."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-19"", ""children"": [{""children"": [{""text"": ""19"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can read more about development assistance for healthcare in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/financing-healthcare/#recent-developments-flows-of-global-health-financing"", ""children"": [{""text"": ""our article on healthcare spending"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Development assistance priorities have the ability to increase or reduce expenditure inequalities"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We mentioned above that public spending on education has translated, in the long run, into lower inequality in education outcomes across most of the world. But for any given country, with a given income distribution and demographic structure, the extent to which public spending on education contributes to reducing inequality depends crucially on the way in which spending is focused across education levels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The recent UNICEF report "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.unicef.org/publications/files/Investment_Case_for_Education_and_Equity_FINAL.pdf"", ""children"": [{""text"": ""The Investment Case for Education and Equity"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" shows that in low-income countries, on average 46 percent of public resources are allocated to the 10 percent of students who are most educated – while this figure goes down to 26 and 13 percent in lower-middle and upper-middle income countries respectively."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization shows further details on the concentration of public spending across different countries. The vertical axis shows the percentage of public education resources going to the 10% most educated or 10% least educated students – as we can see expenditure is heavily concentrated at the top in many low-income countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The earlier remarks about trends in international education financing flows (namely that aid is very important in low-income countries, and that a relatively low and shrinking share of aid is going to primary levels), suggest that inequality in public spending may worsen in low-income countries. Yet development assistance priorities have the ability to change this."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-20"", ""children"": [{""children"": [{""text"": ""20"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""alt"": ""UNICEF Education Expenditure Concentration"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Percentage of public education resources going to the 10% most educated or 10% least educated students – Figure 29 in "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.unicef.org/publications/files/Investment_Case_for_Education_and_Equity_FINAL.pdf"", ""children"": [{""text"": ""The Investment Case for Education and Equity (UNICEF - 2015)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""filename"": ""UNICEF-Education-Expenditure-Concentration.png"", ""parseErrors"": []}, {""text"": [{""text"": ""What determines educational finance?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""The big picture"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Why do governments finance education?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One of the reasons to justify government intervention in the market for education, is that education generates positive externalities."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-21"", ""children"": [{""children"": [{""text"": ""21"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This essentially means that investing in education yields both private and social returns. Private returns to education include higher wages and better employment prospects. Social returns include pro-social behavior (e.g. volunteering, political participation) and interpersonal "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/trust/"", ""children"": [{""text"": ""trust"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following chart uses OECD results from the Survey of Adult Skills to show how self-reported trust in others correlates with educational attainment. More precisely, this chart plots the percentage-point difference in the likelihood of reporting to trust others, by education level of respondents. Those individuals with upper secondary or post-secondary non-tertiary education are taken as the reference group, so the percentage point difference is expressed in relation to this group."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we can see, in all countries those individuals with tertiary education were by far the group most likely to report trusting others. And in almost every country, those with post-secondary non-tertiary education were more likely to trust others than those with primary or lower secondary education. The OECD's report "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.keepeek.com/Digital-Asset-Management/oecd/education/education-at-a-glance-2015_eag-2015-en#page1"", ""children"": [{""text"": ""Education at a Glance (2015)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" provides similar descriptive evidence for other social outcomes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The conclusion is that adults with higher qualifications are more likely to report desirable social outcomes, including good or excellent health, participation in volunteer activities, interpersonal trust, and political efficacy. These results hold after controlling for literacy, gender, age, and monthly earnings."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": ""OECD_Education_Trust"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Likelihood of reporting to trust others, by educational attainment, OECD 2012 – Figure A8.4 in "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.keepeek.com/Digital-Asset-Management/oecd/education/education-at-a-glance-2015_eag-2015-en#page1"", ""children"": [{""text"": ""Education at a Glance (2015)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""url"": ""#note-22"", ""children"": [{""children"": [{""text"": ""22"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""OECD_Education_Trust.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Do countries that spend more public resources on education tend to have better education outcomes?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Education outcomes are "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/quality-of-education"", ""children"": [{""text"": ""typically measured"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" via 'quantity' output (e.g. years of schooling) and 'quality' output (e.g. learning outcomes, such as test scores from the Programme for International Student Assessment – PISA)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization presents three scatter plots using 2010 data to show the cross-country correlation between (i) education expenditure (as a share of GDP), (ii) mean years of schooling, and (iii) mean PISA test scores."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At a cross-sectional level, expenditure on education correlates positively with both quantity and quality measures; and not surprisingly, the quality and quantity measures also correlate positively with each other."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But obviously correlation does not imply causation: there are many factors that simultaneously affect education spending and outcomes. Indeed, these scatterplots show that despite the broad positive correlation, there is substantial dispersion away from the trend line – in other words, there is substantial variation in outcomes that does not seem to be captured by differences in expenditure."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": ""Edu_OutcomesVsExpenditure"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Correlation between education outcomes and education expenditure (2010 data)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-23"", ""children"": [{""children"": [{""text"": ""23"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Edu_OutcomesVsExpenditure.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Does cross-country variation in government education expenditure explain cross-country differences in education outcomes?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization presents the relationship between PISA reading outcomes and average education spending per student, splitting the sample of countries by income levels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It shows that income is an important factor that affects both expenditure on education and education outcomes: we can see that above a certain national income level, the relationship between PISA scores and education expenditure per pupil becomes virtually nonexistent."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": ""Average reading performance in PISA and average spending per student"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Average reading performance in PISA and average spending per student from the age of 6 to 15 - Figure 1 in OECD (2012)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-24"", ""children"": [{""children"": [{""text"": ""24"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""ourworldindata_average-reading-performance-in-pisa-and-average-spending-per-student.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Several studies with more sophisticated econometric models corroborate the fact that expenditure on education does not explain well cross-country differences in learning outcomes."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-25"", ""children"": [{""children"": [{""text"": ""25"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""School inputs"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""Each education system is different, but improving teacher quality is often more effective in improving learning outcomes than increasing the number of teachers per pupil"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A vast number of studies have tried to estimate the impact of classroom resources on learning outcomes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following table summarizes results from the systematic review in Hanushek (2006)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-26"", ""children"": [{""children"": [{""text"": ""26"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In this table, the left-hand side summarizes results from econometric studies focusing on developing countries, while the right-hand side presents evidence from the US (where studies have concentrated extensively)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We can see that for all listed inputs and across all countries, the share of studies that have found a positive effect is small – in fact, the majority of studies find either no effect or a negative effect. This clearly does not mean that these classroom resources are not important, but rather that it is very difficult to know with confidence when and where they are a binding constraint to improve learning outcomes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A first conclusion, therefore, seems to be that context and input mix are fundamental to improving outcomes – even in developing countries where the expected returns to additional resources is large across the board."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Taking the ratio of positive to negative effects detected in the literature as a proxy for what tends to work best, we can derive a second conclusion from the table: spending more resources on better teachers (i.e. improving teacher experience and teacher education) tends to work better to improve learning outcomes than simply increasing the number of teachers per pupil. This seems to be true both in developed and developing countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This last conclusion is consistent with the main message from the OECD's report "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.oecd.org/pisa/pisaproducts/pisainfocus/49685503.pdf"", ""children"": [{""text"": ""Does money buy strong performance in PISA?"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", which points out that countries that prioritized the quality of teachers over class sizes performed better in PISA tests."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-27"", ""children"": [{""children"": [{""text"": ""27"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is is also consistent with a recent high-quality study on the impact of teacher quality on test scores using data from the US, which suggests that improvements in teacher quality can causally raise students’ test scores."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-28"", ""children"": [{""children"": [{""text"": ""28"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""alt"": ""Hanushek_Supply_Interventions"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Percentage distribution of the estimated effect of selected key resources on student performance – based on Tables 3 and 6 in Hanushek (2006)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-29"", ""children"": [{""children"": [{""text"": ""29"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Hanushek_Supply_Interventions.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Remedial teaching can yield substantial improvements in learning outcomes"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Education in low-income countries is particularly difficult because there is substantial heterogeneity in the degree of preparation that children have when they enter school – much more so than in high-income countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Evidence from policy 'experiments' in developing countries suggests remedial teaching, in the form of assistants teaching targeted lessons to the bottom of the class, can yield substantial improvements in learning outcomes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization summarizes the effects of four different policy treatments within the so-called Teacher Community Assistant Initiative (TCAI) in Ghana – this is an initiative that evaluated four different such remedial teaching interventions."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-30"", ""children"": [{""children"": [{""text"": ""30"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The units in this figure are standard deviations of test results. The first two sets of estimates correspond to the test-score impacts of enabling community assistants to provide remedial instruction specifically to low-performing children, either during school or after school. The third set of estimates corresponds to test-score impacts of providing a community assistant and reducing class size, without targeting instruction to low-performing pupils. The last set of results corresponds to testing the effect of training teachers to provide small-group instruction targeted at pupils’ actual learning levels."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we can see, while all interventions had a positive effect, the lowest impacts – across all tests – come from the non-targeted 'normal curriculum' intervention that reduced class sizes, and from the intervention that provided training to teachers on how to engage in targeted remedial teaching themselves. This suggests that the improvements in outcomes were caused by the combination of targeted instruction and TCAs who, unlike teachers, were specifically dedicated to this purpose. These results are consistent with findings from across Africa, suggesting that teaching at the right level causes better learning outcomes in a cost-effective way."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-31"", ""children"": [{""children"": [{""text"": ""31"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""alt"": ""TCAI_RemedialTeaching_JPAL"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Summary of treatment effects from the Teacher Community Assistant Initiative (TCAI) in Ghana (estimates by test subject in standard deviations) – Page 2 in Innovations for Poverty Action (2014)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-32"", ""children"": [{""children"": [{""text"": ""32"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""TCAI_RemedialTeaching_JPAL.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Are pay-for-performance teacher contracts an effective instrument to improve learning outcomes?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We have already made the point that the bulk of education expenditure goes specifically towards financing teachers. We have also pointed out that improving teacher quality may be a particularly good instrument to improve teaching outcomes. This leads to a natural question: are pay-for-performance teacher contracts an effective instrument to improve learning outcomes? A growing body of literature in the economics of education has started using randomized control trials (i.e. policy 'experiments') to answer this question. Glewwe and Muralidharan (2016) provide the following account of the available evidence:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""\""Results suggest that even modest changes to compensation structures to reward teachers on the basis of objective measures of performance (such as attendance or increases in student test scores) can generate substantial improvements in learning outcomes at a fraction of the cost of a \""business as usual\"" expansion in education spending. However, not all performance pay programs are likely to be effective, so it is quite important to design the bonus formulae well and to make sure that these designs reflect insights from economic theory.\"""", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" "", ""spanType"": ""span-simple-text""}, {""url"": ""#note-33"", ""children"": [{""children"": [{""text"": ""33"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""type"": ""blockquote"", ""citation"": """", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The conclusion is that well-designed pay-for-performance contracts are a cost-effective instrument to boost test scores; but this does not mean that they are necessarily effective at achieving other – perhaps equally important – objectives of time spent in school. In simple words, it is possible that pay-for-performance yields 'teaching to the test'."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Other incentive mechanisms, such as community-based monitoring of teachers, have been proposed as an alternative. Glewwe and Muralidharan (2016) also provide a review of the – somewhat limited – available evidence on such alternative incentive mechanisms."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-34"", ""children"": [{""children"": [{""text"": ""34"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""Household inputs"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""School attendance and student effort are responsive to incentives"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Demand-side inputs are as important as supply-side inputs to produce education. Attending school and exerting effort are perhaps the most obvious examples: without these inputs, even the best-endowed schools will fail to deliver good outcomes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The table summarizes information on different demand-side investments that have been shown to successfully improve quality and quantity outcomes. More precisely, this table gathers evidence from randomized control trials in developing countries, as per the review in Glewwe and Muralidharan (2016). The reported figures correspond to positive/negative significant/insignificant estimates across a set of available experimental studies (bear in mind some studies estimate more than one effect – e.g. by measuring outcomes at several points in time)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we can see, the evidence suggests interventions that increase the benefits of attending school – such as conditional cash transfers – are likely to increase student time in school. And those that increase the benefits of higher effort and better academic performance – such as merit scholarships – are likely to improve learning outcomes."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-35"", ""children"": [{""children"": [{""text"": ""35"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": ""Glewwe2016_DemandInterventions_RCTs"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Summary of impacts for selected demand-side interventions on education outcomes in developing countries – based on Tables 4 and 5 from Glewwe and Muralidharan (2016)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-36"", ""children"": [{""children"": [{""text"": ""36"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Glewwe2016_DemandInterventions_RCTs-1.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Targeting health problems can be a particularly cost-effective way of increasing school attendance"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In many low-income countries, health problems are an important factor preventing children from attending school."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization presents a comparison of the impact that a number of different health interventions have achieved in different countries – together with some non-health-related interventions that serve as references. The height of each bar in this graph reflects the additional school years achieved per hundred dollars spent on the corresponding intervention; so these estimates can be interpreted as a measure of how cost-effective the different interventions are."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-37"", ""children"": [{""children"": [{""text"": ""37"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see that treating children for intestinal worms (labeled 'deworming' in the chart) led to an additional 13.9 years of education for every $100 spent in Kenya; while a program targeting anemia (labeled 'iron fortification') led to 2.7 additional years per $100 in India. These interventions seem to be much more cost-effective in improving test scores than conditional cash transfers, free school uniforms, or merit scholarships."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-38"", ""children"": [{""children"": [{""text"": ""38"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Of course, ranking these interventions is not trivial since most programs achieve multiple outcomes – indeed, we have already discussed that remedial teaching is generally effective to increase test-scores, although here we see a particular instance where it had no impact on school attendance."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Nevertheless, health interventions seem to be particularly interesting, since they lead to substantial achievements in both education "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""and"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" health outcomes."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-39"", ""children"": [{""children"": [{""text"": ""39"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""alt"": ""CEA_SchoolParticipationRCTs_JPAL"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Impact of selected demand-side interventions on school participation in developing countries (Additional years of student participation per $100) – Figure 8.1 in Dhaliwal et al. (2012)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-40"", ""children"": [{""children"": [{""text"": ""40"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""CEA_SchoolParticipationRCTs_JPAL-1.png"", ""parseErrors"": []}, {""text"": [{""text"": ""How important are pre-school investments?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The environment that children are exposed to early in life plays a crucial role in shaping their abilities, behavior, and talents. To a great extent, this is what drives large and remarkably persistent gaps in education achievement between individuals in the same country, but in different socioeconomic environments. Cunha et al. (2006) provide a detailed account of the theory and evidence behind this claim and discuss its implications for the design of education policies."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, we see the impacts of the Perry Preschool Program – a flagship experimental intervention study, designed to test the impact of preschool education on subsequent education outcomes."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-41"", ""children"": [{""children"": [{""text"": ""41"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows disadvantaged children participating in the preschool program (the 'treatment group') had higher grades and were more likely to graduate from high school than the reference control group. Moreover, they spent substantially less time in special education. Other programs have similarly shown evidence of very large and persistent returns to early education interventions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""alt"": ""Cunha2006_Preschool_Impact"", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Educational effects from participating in the Perry Preschool Program – Figure 14B in Cunha et al (2006)"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-42"", ""children"": [{""children"": [{""text"": ""42"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Cunha2006_Preschool_Impact.png"", ""parseErrors"": []}, {""top"": [], ""type"": ""all-charts"", ""heading"": ""Interactive Charts on Education Spending"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""015fffce5e12d52a8e66e7f030028206296848df"": {""id"": ""015fffce5e12d52a8e66e7f030028206296848df"", ""index"": 17, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The share of development assistance going to sub-Saharan Africa has decreased as a whole – from 55 percent in 2002 to 40 percent in 2013 –, but as we note the drop specifically for primary education has been steeper."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""04da056729e281b29f3e4fa373aa3e075ebbca0d"": {""id"": ""04da056729e281b29f3e4fa373aa3e075ebbca0d"", ""index"": 33, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""They conclude that \""evidence on the impact of monitoring on time in school is scarce and not encouraging...[while] the evidence of the impact of monitoring on student learning is only somewhat more encouraging\"""", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""0661a5f4af2160f5a4343c74f780a1fcf8c5a636"": {""id"": ""0661a5f4af2160f5a4343c74f780a1fcf8c5a636"", ""index"": 14, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This is a stylized fact of OECD education spending. In all the OECD countries, the share of spending devoted to the compensation of teachers is by far the largest component of current expenditure. Moreover, expenditure on teachers' compensation is larger at the combined primary, secondary, and post-secondary non-tertiary levels of education than at the tertiary level. 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(2016) "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.sciencedirect.com/science/article/pii/B9780444634597000105"", ""children"": [{""text"": ""Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Handbook of the Economics of Education, Volume 5. Elsevier."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""20ab52ded501ba8ff7ca399201804f4c912a38dd"": {""id"": ""20ab52ded501ba8ff7ca399201804f4c912a38dd"", ""index"": 19, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The conclusion from these figures is that, while public spending does reduce education inequality in low-income countries, remaining inequalities could be further reduced by shifting resources towards lower levels of education. This evidently does not mean that resources "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""should"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" be shifted – low-income countries and aid donors may have other objectives apart from reducing inequality. But the case for reducing inequality at the bottom is very strong, and some studies suggest that returns to education at the primary level might be higher than at post-primary levels in low-income countries (for a discussion of the vast literature on returns to education, and the ongoing debate on the validity of estimates, see Heckman, J. J., Lochner, L. J., & Todd, P. E. (2006). Earnings functions, rates of return and treatment effects: The Mincer equation and beyond. 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"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Growing Public: Volume 1, the story: Social spending and economic growth since the eighteenth century"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". Vol. 1. Cambridge University Press, 2004."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3600281e01557b7d548b0f1384689b000272a1dd"": {""id"": ""3600281e01557b7d548b0f1384689b000272a1dd"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""As per estimates of Gini coefficients for the distribution of school years in Crespo Cuaresma, J., KC, S., & Sauer, P. (2013). "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.ecineq.org/ecineq_bari13/FILESxBari13/CR2/p100.pdf"", ""children"": [{""text"": ""Age-specific education inequality, education mobility and income growth"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (No. 6). WWWforEurope."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""375d69ab0d2114806865f3fdee87c0ed4d6025bd"": {""id"": ""375d69ab0d2114806865f3fdee87c0ed4d6025bd"", ""index"": 20, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""That positive externalities justify government intervention in the provision of education is essentially an efficiency argument. The logic is that individuals may not spend enough on education because they fail to internalize the positive effect that their education has on other people. But there are, of course, also equity arguments to justify government intervention in the provision of education – for instance, reducing inequality in education may be of intrinsic value, or may be instrumental in reducing inequalities in other outcomes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3f9646123f25732c311363d7f97639fb4b5c1692"": {""id"": ""3f9646123f25732c311363d7f97639fb4b5c1692"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Bear in mind that the estimates from the National Center for Education Statistics are not broken down by source of funds. 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A. (2006). Handbook of the Economics of Education, Two Volumes. North Holland."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""4a9ac413743a1f3d3059bbdde0fb869a85f95067"": {""id"": ""4a9ac413743a1f3d3059bbdde0fb869a85f95067"", ""index"": 29, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Further details in Innovations for Poverty Action, 2014. I"", ""spanType"": ""span-simple-text""}, {""url"": ""https://poverty-action.org/sites/default/files/publications/TCAI_Final%20Results_040115.pdf"", ""children"": [{""text"": ""mplementation Lessons: The Teacher Community Assistant Initiative (TCAI)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""54a7c7d68aa30f17c7b1337aa5d1cf038fa36e19"": {""id"": ""54a7c7d68aa30f17c7b1337aa5d1cf038fa36e19"", ""index"": 38, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For an analysis of the literature on the impacts of mass deworming see: Croke, Kevin, Joan Hamory Hicks, Eric Hsu, Michel Kremer, and Edward Miguel. 2016. “"", ""spanType"": ""span-simple-text""}, {""url"": ""http://scholar.harvard.edu/files/kremer/files/deworming-nber_2016-06-30.pdf"", ""children"": [{""text"": ""Does Mass Deworming Affect Child Nutrition? Meta-analysis, Cost-effectiveness, and Statistical Power"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "".” Working Paper."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""5971ba940ceed017fe133a833aa2dd413fa6e29f"": {""id"": ""5971ba940ceed017fe133a833aa2dd413fa6e29f"", ""index"": 16, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Steer L. and K. Smith (2015), "", ""spanType"": ""span-simple-text""}, {""url"": ""http://files.eric.ed.gov/fulltext/ED568940.pdf"", ""children"": [{""text"": ""Financing education: Opportunities for global action"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Center for Universal Education. Available Online from the Brookings Institution"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""70878c881d9923bc72c3b30e7e978fbac2c23b8d"": {""id"": ""70878c881d9923bc72c3b30e7e978fbac2c23b8d"", ""index"": 37, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Further details on all interventions available in: Dhaliwal, I., Duflo, E., Glennerster, R., & Tulloch, C. (2013). "", ""spanType"": ""span-simple-text""}, {""url"": ""http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.570.2412&rep=rep1&type=pdf"", ""children"": [{""text"": ""Comparative cost-effectiveness analysis to inform policy in developing countries: a general framework with applications for education"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Education Policy in Developing Countries, 285-338."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7435b1fd8a5ba8c53144321de9a7a769bc2fd812"": {""id"": ""7435b1fd8a5ba8c53144321de9a7a769bc2fd812"", ""index"": 36, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Bear in mind that the reported gains in school years are a measure of the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""total"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" impact of the program across the treated population, rather than impact per treated student. Further information on cost-effectiveness analysis is available from the source of the graph."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7f56319e8dfe9a714839984d7becda97062a973c"": {""id"": ""7f56319e8dfe9a714839984d7becda97062a973c"", ""index"": 26, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This claim is clearly only descriptive since there are many underlying variables that simultaneously drive teacher characteristics and student outcomes in any particular country. Indeed, most of the available evidence on whether teacher quality and quantity matters is difficult to interpret causally, as it is hard to find instances where teacher quality/quantity varies exogenously. A recent study concludes on the topic: "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" \""teachers vary in many ways, but we found no high-quality studies that have examined the impact of teacher characteristics on student learning or time in school\"" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(source: page 696, Glewwe, P. and Muralidharan, K. (2016) "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.sciencedirect.com/science/article/pii/B9780444634597000105"", ""children"": [{""text"": ""Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Handbook of the Economics of Education, Volume 5. )"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7fad6178a3c8eea6c3542931c247718da1289a76"": {""id"": ""7fad6178a3c8eea6c3542931c247718da1289a76"", ""index"": 40, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""More specifically, the Perry preschool 'experiment' consisted of enrolling 65 randomly selected black children in a pre-school program, and comparing their outcomes later in life against those achieved by a control group of roughly the same size. The treatment consisted of a daily 2.5-hour classroom session on weekday mornings and a weekly 90-minute home visit by the teacher on weekday afternoons to involve the mother in the child's educational process. More information and details on the intervention are available in Cunha et al. (2006)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""7faef3dac751dbe9537e36fde7eb68e896aded0d"": {""id"": ""7faef3dac751dbe9537e36fde7eb68e896aded0d"", ""index"": 31, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Innovations for Poverty Action (2014). "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.poverty-action.org/sites/default/files/publications/TCAI_Final%20Results_040115.pdf"", ""children"": [{""text"": ""Implementation Lessons: The Teacher Community Assistant Initiative (TCAI)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""85d07e35303a0309ea87f3df88f8d12fab573d14"": {""id"": ""85d07e35303a0309ea87f3df88f8d12fab573d14"", ""index"": 27, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Chetty, Raj, John N. Friedman, and Jonah E. Rockoff. 2014. “Measuring the Impacts of Teachers I: Evaluating Bias in Teacher Value-Added Estimates.” American Economic Review, 104(9): 2593-26"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""85e90b1202994b49e7d67e5fedd92b3a37f045ac"": {""id"": ""85e90b1202994b49e7d67e5fedd92b3a37f045ac"", ""index"": 11, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Data from Petra Sauer (2016) – "", ""spanType"": ""span-simple-text""}, {""url"": ""http://epub.wu.ac.at/5186/"", ""children"": [{""text"": ""The Role of Age and Gender in Education Expansion"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Working Paper."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""89c559c8e397dff68f296d44dbd75809f654807d"": {""id"": ""89c559c8e397dff68f296d44dbd75809f654807d"", ""index"": 10, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Jesus Crespo Cuaresma, Samir K.C., and Petra Sauer (2013) – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20170811085131/http://www.foreurope.eu/fileadmin/documents/pdf/Workingpapers/WWWforEurope_WPS_no006_MS15.pdf"", ""children"": [{""text"": ""Age-Specific Education Inequality, Education Mobility and Income Growth"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". WWWforEurope working paper; Working Paper no 6."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""8ef0335acdc0ee754c9108bdd28ea9b064bbd338"": {""id"": ""8ef0335acdc0ee754c9108bdd28ea9b064bbd338"", ""index"": 22, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Data on expenditure corresponds to 2010 total government education expenditure across all levels, as a share of GDP (source: World Bank Education Statistics). Data on PISA scores corresponds to 2010 mean average test scores across categories – mathematics, reading, and science (source: OECD PISA). Data on years of schooling corresponds to 2010 mean years of schooling for the population aged 15 and over (source: Barro Lee Education dataset)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""8f8ad34bd4c950f011994e87776f257e2c671d3e"": {""id"": ""8f8ad34bd4c950f011994e87776f257e2c671d3e"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""As per "", ""spanType"": ""span-simple-text""}, {""url"": ""https://nces.ed.gov/programs/digest/d15/tables/dt15_105.20.asp"", ""children"": [{""text"": ""2015 enrolment estimates from the NCES."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""994dd5ec8dbc82bc97b4615b959871b9bde6a63f"": {""id"": ""994dd5ec8dbc82bc97b4615b959871b9bde6a63f"", ""index"": 9, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""In 2010, high-income countries spent 6721 US PPP dollars per primary school pupil. Low-income countries, in contrast, spent 115 US PPP dollars per pupil (UNESCO EFA Global Monitoring Report 2014)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""aa45ce8b497860ef5af913095437f4ef9637c4f0"": {""id"": ""aa45ce8b497860ef5af913095437f4ef9637c4f0"", ""index"": 21, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""As per the source notes: \""Percentage-point difference reflects the relative change of reporting to trust others compared to the reference category. For example, in Norway, the percentage of individuals with tertiary education reporting to trust others increases by 20 percentage points compared to someone who has upper secondary or post-secondary non-tertiary education. Similarly, after accounting for literacy proficiency, the percentage of individuals with tertiary education increases by 16 percentage points compared to someone who has upper secondary or post-secondary non-tertiary education.\"""", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""bc351b6f52770f33968fea615ca8ef24e480f328"": {""id"": ""bc351b6f52770f33968fea615ca8ef24e480f328"", ""index"": 41, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Cunha, F., Heckman, J. J., Lochner, L., & Masterov, D. V. (2006). "", ""spanType"": ""span-simple-text""}, {""url"": ""http://discovery.ucl.ac.uk/2559/1/2559.pdf"", ""children"": [{""text"": ""Interpreting the evidence on life cycle skill formation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Handbook of the Economics of Education, 1, 697-812."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""be5f97697f74ad37e3d515715dfa5126bf41609d"": {""id"": ""be5f97697f74ad37e3d515715dfa5126bf41609d"", ""index"": 24, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For a discussion of the evidence supporting this claim, see Hanushek, E. A., (2006). School Resources. Handbook of the Economics of Education, 2."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c150d0e727f8f32009930c61a653713d29672d83"": {""id"": ""c150d0e727f8f32009930c61a653713d29672d83"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""An article "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.huffingtonpost.com/2015/03/13/arne-duncan-school-funding-disparities_n_6864866.html"", ""children"": [{""text"": ""from the Huffington Post"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" highlights this point, including interesting visualizations documenting the important role that federal funding plays in reducing expenditure inequalities."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c1ef1cbf498e6e8e21a4917411b3d383988e1820"": {""id"": ""c1ef1cbf498e6e8e21a4917411b3d383988e1820"", ""index"": 35, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Glewwe, P. and Muralidharan, K. (2016) "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.sciencedirect.com/science/article/pii/B9780444634597000105"", ""children"": [{""text"": ""Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Handbook of the Economics of Education, Volume 5. Elsevier. (Link only to working paper)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c9415834eeb91f743c8e7ec43ce7ccb96c40af06"": {""id"": ""c9415834eeb91f743c8e7ec43ce7ccb96c40af06"", ""index"": 13, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The OECD provides country-specific figures. However, there is relatively little variation across OECD countries in this respect. This is explained by near-universal enrolment rates at these levels of education and the demographic structure of the population."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""cf03c576942b4a845975f4f60765c2341860a95e"": {""id"": ""cf03c576942b4a845975f4f60765c2341860a95e"", ""index"": 30, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For further details, see: Glewwe, P. and Muralidharan, K. (2016) "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.sciencedirect.com/science/article/pii/B9780444634597000105"", ""children"": [{""text"": ""Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Handbook of the Economics of Education, Volume 5. Elsevier. (Link to working paper)"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""da69cb9aa158396c5eaf2ac162c2a4f0ac5ca086"": {""id"": ""da69cb9aa158396c5eaf2ac162c2a4f0ac5ca086"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Lindert, Peter H. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Growing public: Volume 1, the story: Social spending and economic growth since the eighteenth century"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". Vol. 1. Cambridge University Press, 2004."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""dbcdd0638f1e87faba63fdd65c28e16d16996849"": {""id"": ""dbcdd0638f1e87faba63fdd65c28e16d16996849"", ""index"": 34, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See Glewwe and Muralidharan 2016 for further details on the underlying policy interventions, plus further evidence and discussion of results"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""dc47ef558cbc2007c913f7c45c34adaa4b08fbbe"": {""id"": ""dc47ef558cbc2007c913f7c45c34adaa4b08fbbe"", ""index"": 23, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Does money buy strong performance in PISA? - OECD. Available online "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.oecd.org/pisa/pisaproducts/pisainfocus/49685503.pdf"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e1ca890fc61b3688c8c729a261607b6ae7ee882b"": {""id"": ""e1ca890fc61b3688c8c729a261607b6ae7ee882b"", ""index"": 18, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Steer L. and K. Smith (2015), "", ""spanType"": ""span-simple-text""}, {""url"": ""http://files.eric.ed.gov/fulltext/ED568940.pdf"", ""children"": [{""text"": ""Financing education: Opportunities for global action"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Center for Universal Education."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e49fa3088c4be693499499544584aba0fffc9dc0"": {""id"": ""e49fa3088c4be693499499544584aba0fffc9dc0"", ""index"": 39, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Dhaliwal, I., Duflo, E., Glennerster, R., & Tulloch, C. (2013). "", ""spanType"": ""span-simple-text""}, {""url"": ""http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.570.2412&rep=rep1&type=pdf"", ""children"": [{""text"": ""Comparative cost-effectiveness analysis to inform policy in developing countries: a general framework with applications for education"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Education Policy in Developing Countries, 285-338."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Education Spending"", ""authors"": [""Max Roser"", ""Esteban Ortiz-Ospina""], ""excerpt"": ""How is education financed? How much do we spend on it? What are the returns?"", ""subtitle"": ""How is education financed? How much do we spend on it? What are the returns?"", ""featured-image"": ""financing-education-thumbnail.png""}",1,2024-03-11 09:06:02,2016-06-22 16:47:34,2024-03-12 17:57:51,listed,ALBJ4LuG2qeviSSq9fCE9bUzRbXrk26jJ8cKAxEYJ0UzF4MNs1V6wtbLPJk12YZ9fo7KkSHZDw1ERM--NYrlxw,,"In most countries basic education is nowadays perceived not only as a right, but also as a duty – governments are typically expected to ensure access to basic education, while citizens are often required by law to attain education up to a certain basic level.1 This was not always the case: the advancement of these ideas began in the mid-19th century, when most of today’s industrialized countries started expanding primary education, mainly through public finances and government intervention. Data from this early period shows that government funds to finance the expansion of education came from a number of different sources, but taxes at the local level played a crucial role. The historical role of local funding for public schools is important to help us understand changes – or persistence – in regional inequalities. The second half of the 20th century marked the beginning of education expansion as a global phenomenon. Available data shows that by 1990 government spending on education as a share of national income in many developing countries was already close to the average observed in developed countries.2 This global education expansion in the 20th century resulted in a historical reduction in education inequality across the globe: in the period 1960-2010 education inequality went down every year, for all age groups and in all world regions. Recent estimates of education inequality across age groups suggest that further reductions in schooling inequality are still to be expected within developing countries.3 Recent cross-country data from UNESCO tells us that the world is expanding government funding for education today, and these additional public funds for education are not necessarily at the expense of other government sectors. Yet behind these broad global trends, there is substantial cross-country – and cross-regional – heterogeneity. In high-income countries, for instance, households shoulder a larger share of education expenditures at higher education levels than at lower levels – but in low-income countries, this is not the case. Following the agreement of the Millennium Development Goals, the first decade of the 21st century saw an important increase in international financial flows under the umbrella of development assistance. Recent estimates show that development assistance for education has stopped growing since 2010, with notable aggregate reductions in flows going to primary education. These changes in the prioritization of development assistance for education across levels and regions can have potentially large distributional effects, particularly within low-income countries that depend substantially on this source of funding for basic education.4 When analyzing correlates, determinants and consequences of education consumption, the macro data indicates that national expenditure on education does not explain well cross-country differences in learning outcomes. This suggests that for any given level of expenditure, the output achieved depends crucially on the mix of many inputs. Available evidence specifically on the importance of school inputs to produce education, suggests that learning outcomes may be more sensitive to improvements in the quality of teachers, than to improvements in class sizes. Regarding household inputs, the recent experimental evidence suggests that interventions that increase the benefits of attending school (e.g. conditional cash transfers) are particularly likely to increase student time in school; and that those that incentivize academic effort (e.g. scholarships) are likely to improve learning outcomes. Policy experiments have also shown that preschool investment in demand-side inputs leads to large positive impacts on education – and other important outcomes later in life. The environment that children are exposed to early in life, plays a crucial role in shaping their abilities, behavior, and talents. # Historical perspective on financing education ## When did the provision of education first become a public policy priority? Governments around the world are nowadays widely perceived to be responsible for ensuring the provision of accessible quality education. This is a recent social achievement. The advancement of the idea to provide education for more and more children only began in the mid-19th century, when most of today’s industrialized countries started expanding primary education. The following visualization, plotting public expenditure on education as a share of Gross Domestic Product (GDP) for a number of early-industrialized countries, shows that this expansion took place mainly through public funding. [Our topic page on global education](https://ourworldindata.org/global-education) provides details regarding how this expansion in funding materialized in better education outcomes for these countries. ## How did the US finance the expansion of public education? Public schools in the US educate more than 90% of all children enrolled in elementary and secondary schools.5 This is the result of a process of education expansion that relied heavily on public funding, particularly from local governments. The visualization shows the sources of revenues for public schools in the US over the last 120 years. As can be seen, states and localities are – and have always been – the main sources of funding for public primary education in the US. In fact, we observe three broad periods in this graph: there is first a period of stable revenues until 1920, then a period of sharp growth and decline during the interwar years, and then a period of substantial growth since the Second World War, slowing down in the 1970s. In all these periods, federal funding was always very small. Disaggregated data from the last couple of decades gives further insights into the specific sources of local revenues for schools in the US: the largest part comes from property taxes (about 80% of local revenues came from property taxes in 2013), while only a very small part comes from fees and donations (private funding for public schools, which is considered a local revenue, amounted to less than 2% of total public school revenues in 2013). This heavily decentralized system relying on property taxes has the potential to create large inequalities in education since public schools in affluent urban areas are able to raise more funding from local revenues. Indeed, a significant part of the debate on education inequalities in the US today focuses on the importance of increasing progressive federal spending to reduce inequalities in public school funding.6 ## How did France finance the expansion of public education? The case of the US above shows that funding for public schools has been historically a responsibility of local governments. In other countries, such as France, the expansion of public education also took place initially with resources from local governments, but relatively quickly the fiscal burden was shifted to the national level. In France, this transition was associated with a sharp jump towards universal access and a concomitant reduction in regional inequalities. The following visualization from Lindert (2004)7 provides evidence of the French experience. As we can see there are three distinct periods: education spending was initially low and mainly private, then in 1833 funding began growing with local resources after the introduction of a law liberating communes to raise more local taxes for schools, and finally in 1881 the national government took over most of the financial responsibility after the introduction of a new law that abolished all fees and tuition charges in public elementary schools. In the source book, Lindert (2004) provides further evidence of how this transition towards centrally funded public education reduced north-south inequalities in France. ## In the US growth in education expenditure was characterized by growth specifically in the public sector A comparison of expenditure between public and private education institutions is helpful to contextualize the role the public sector played in the process of education expansion in industrialized countries. The following graph does this using data from the National Center for Education Statistics in the US. It shows that during the years 1950-1970 – a period of substantial growth in education expenditure in the US – expenditure grew _specifically_ in the public sector.9 ## When did the expansion of basic education become a global phenomenon? The second half of the 20th century marked the beginning of education expansion as a global phenomenon. The visualization shows government expenditure on education as a share of national income for a selection of low and middle-income countries, together with the corresponding average for high-income countries, for more than the last half-century. As can be seen, spending on education in many developing countries has become similar to the average observed in developed countries in recent decades. It is important to point out that the remark above makes reference to convergence in expenditure relative to _income_. To the extent that low-income countries remain poorer than high-income countries, gaps in levels of expenditure per pupil are persistently large. Indeed, cross-country heterogeneity in education expenditure per pupil is currently much higher than heterogeneity in expenditure as a share of GDP.10 One factor contributing to the slower convergence of expenditure per pupil in real terms is the fact that teachers' salaries – the main component of education expenditure, as discussed below – are much higher in high-income countries because labor has a higher opportunity cost in these countries. In general, the opportunity cost of labor is a key variable that governments in developing countries should factor in when deciding whether to expand education now, rather than later. ## Education inequality is falling around the world An important consequence of the global education expansion is a reduction in education inequality across the globe. The following visualization shows this through a series of graphs plotting changes in the Gini coefficient of the distribution of years of schooling across different world regions. The Gini coefficient is a measure of inequality and higher values indicate higher inequality – you can read about the definition and estimation of Gini coefficients [in our related article](https://ourworldindata.org/what-is-the-gini-coefficient). The time-series chart shows inequality by age group. It can be seen that as inequality is falling over time, the level of inequality is higher for older generations than it is for younger generations. We can also see that in the reference period education inequality went down every year, for all age groups and in all world regions. Have gains from historical education expansion fully materialized? The breakdown by age gives us a view into the future: as inequality is lower among today's younger generations, we can expect the decline of inequality to continue in the future. Thus, further reductions in education inequality are still to be expected within developing countries; and if the expansion of global education can be continued, we can speed up this important process of global convergence. ## Education inequality can decline rapidly across all levels of education – South Korea is an example The experience of South Korea shows that it is possible to reduce education inequality rapidly across all levels of education. The following visualization shows two graphs comparing the concentration of years of education in South Korea between the years 1970 and 2010. To be precise, each of these graphs shows an education Lorenz curve: a plot showing the cumulative percentage of the schooling years across all levels of education on the vertical axis, and the cumulative percentage of the population on the horizontal axis. As can be seen, in 2010 education was much less concentrated than in 1970, not only because there was a smaller share of individuals without schooling (shown at the bottom of the chart), but also because there was a smaller share of individuals concentrating large proportions of school-years at higher levels of education. Indeed, in only 40 years South Korea was able to double the mean years of schooling (from 6 to 12 years) and at the same time get remarkably close to the 45-degree line marking the hypothetical scenario of perfect equality of schooling. # Financing of education across the world ## Is funding for education expanding? The last two decades have not a clear trend in the share of income that countries devote to education. The following chart plots trends in public expenditure on education as a share of GDP. We can see an upward trend in some countries, but a downward trend in others. However, as incomes – measured by GDP per capita – are generally increasing around the world, this means that the total amount of global resources spent on education is increasing in absolute terms. ## Is additional funding for education taking resources from other sectors? The following visualization shows government expenditure on education as a share of total government expenditure. The available data also does not suggest a discernible global pattern here. The data does suggest, however, that there is large and persistent cross-country heterogeneity in the relative importance of education vis-a-vis other sectors, even within developing countries. ## European countries tend to assign a lower share of public budgets to education, relative to the amount of their income that is devoted to education Generally speaking, countries that spend a large share of their income on education also tend to prioritize education highly within their budgets. The following visualization presents a snapshot of government spending on education around the world. Specifically, this graph plots government expenditure on education as a share of GDP on the horizontal axis, and government expenditure on education as a share of total government expenditure on the vertical axis. As we can see, there is a positive correlation, but regional differences are stark: for almost every level of spending as a share of GDP along the horizontal axis, countries in Europe spend a smaller budget share on education. ## In European countries the weight of primary education within total education spending is lower than in other countries In comparison to countries where education started expanding later, European countries tend to assign relatively more of their government education budgets to the secondary and tertiary levels, while at the same time devoting relatively less of their general government budgets to education as a whole. This can be appreciated in the following visualization, where the prioritization of primary education (i.e. the share of primary education within the education budget) is plotted against the overall prioritization of education (i.e. the share of education within the entire government budget). It can be seen that European countries are mostly located in the upper left. There is a weak positive correlation between the variables, both across all countries and across European countries. ## In high-income countries, households shoulder a larger share of education expenditures at higher education levels than at lower levels – but in low-income countries, this is not the case The following visualization shows the percentage of total education expenditures contributed directly by households in 15 high-income countries and 15 low or middle-income countries. The top chart in this figure, corresponding to high-income countries, shows a very clear pattern: households contribute the largest share of expenses in tertiary education, and the smallest share in primary education. Roughly speaking, this pattern tends to be progressive, since students from wealthier households are more likely to attend tertiary education, and those individuals who attend tertiary education are likely to perceive large private benefits.13 In contrast, the bottom chart shows a very different picture: in several low-income countries households contribute proportionally more to primary education than to higher levels. Such distribution of private household contributions to education is regressive. # Recent funding structures in OECD countries ## Primary education continues to be publicly funded in industrialized countries We have already mentioned that those countries that pioneered the expansion of primary education in the 19th century – all of which are current OECD member states – relied heavily on public funding to do so. Today, public resources still dominate funding for the primary, secondary, and post-secondary non-tertiary education levels in these countries. The visualization presents OECD-average expenditure on education institutions by source of funds.14 ## Publicly funded pre-primary education is more strongly developed in the European countries of the OECD High-income countries tend to have better-developed pre-primary education systems than lower-income countries. However, within high-income countries, there is substantial heterogeneity in the extent to which pre-primary education is publicly financed. The visualization presents expenditure on pre-primary educational institutions as a share of GDP across the OECD. As can be seen, publicly funded pre-primary education tends to be more strongly developed in Europe than in the non-European countries of the OECD. ## Where does funding for education go to? The largest part of funding devoted to education in OECD countries goes to finance current expenditures, mainly compensation of staff – specifically, teachers. The following two charts, taken from the OECD's report [Education at a Glance (2015)](http://www.keepeek.com/Digital-Asset-Management/oecd/education/education-at-a-glance-2015_eag-2015-en#page1), highlight the labor-intensive nature of education. In the lower levels of education (i.e. primary, secondary, and post-secondary non-tertiary) the share of current expenditure is very large and exhibits little cross-country variation – between 90 and 97 percent of total expenditure corresponds to current expenditure across all of the OECD countries. In higher levels of education (i.e. tertiary) there is more cross-country variation, but current expenditure still dominates by a large margin across all countries. ## What drives current expenditure on education? In the figures above we noted the importance of current expenditure in the production of education. The following table provides further details regarding the type of expenditures that comprise current spending. Specifically, this chart shows a breakdown of expenditure for tertiary-level institutions in the US (public and private), during the period 1980-1997. It shows that instruction accounts for almost half of expenditure; and while there are some small differences across sectors, there is a fair amount of stability in expenditures across time. This serves as a benchmark for lower education levels, where instruction takes an even larger share of expenditure.15 # International financing flows ## Education financing in developing countries has been bolstered by development assistance Following the agreement of the Millennium Development Goals, the first decade of the 21st century saw an important increase in international financial flows under the umbrella of development assistance (often also called development aid, or simply 'aid'). The following chart shows total OECD development assistance flows for education by level, in constant 2013 US dollars, for the period 2002-2013. As it can be seen, there are two distinct periods: in 2003-2010 flows for education increased substantially, more than doubling in real terms across all levels of education; and in the years 2010-2013 funding for basic education _decreased_, while funding for secondary and post-secondary education remained relatively constant. For many low-income countries, where development assistance contributes a substantial share of funding for education, this marked change in trends is important. As a reference, in 2012 development assistance accounted for more than 20 percent of all domestic spending on basic education in recipient low-income countries.17 ## The share of development assistance for education going to Sub-saharan Africa has decreased The reductions in development assistance funds for primary education have been coupled with important changes in regional priorities. Specifically, the share of development assistance for primary education going to sub-Saharan Africa has been decreasing sharply since the agreement of the Millennium Development Goals. The following chart shows this: sub-Saharan Africa’s share in total aid to primary education declined from 52 percent in 2002 to 30 percent in 2013, while the continent’s share in the total number of out-of-school children rose from 46 percent to 57 percent. This pattern is something specific to the education sector within the broader development assistance landscape: in the healthcare sector, the overall slowdown of flows started a couple of years later, was less abrupt, and affected proportionally less the sub-Saharan countries.18 Indeed, recent studies further highlight that development assistance for education is significantly different from assistance for healthcare in other ways: the education sector attracts less earmarked funding through multilaterals, and includes a smaller proportion of resources that developing governments can directly control for programming.19 You can read more about development assistance for healthcare in [our article on healthcare spending](https://ourworldindata.org/financing-healthcare/#recent-developments-flows-of-global-health-financing). ## Development assistance priorities have the ability to increase or reduce expenditure inequalities We mentioned above that public spending on education has translated, in the long run, into lower inequality in education outcomes across most of the world. But for any given country, with a given income distribution and demographic structure, the extent to which public spending on education contributes to reducing inequality depends crucially on the way in which spending is focused across education levels. The recent UNICEF report [The Investment Case for Education and Equity](http://www.unicef.org/publications/files/Investment_Case_for_Education_and_Equity_FINAL.pdf) shows that in low-income countries, on average 46 percent of public resources are allocated to the 10 percent of students who are most educated – while this figure goes down to 26 and 13 percent in lower-middle and upper-middle income countries respectively. The following visualization shows further details on the concentration of public spending across different countries. The vertical axis shows the percentage of public education resources going to the 10% most educated or 10% least educated students – as we can see expenditure is heavily concentrated at the top in many low-income countries. The earlier remarks about trends in international education financing flows (namely that aid is very important in low-income countries, and that a relatively low and shrinking share of aid is going to primary levels), suggest that inequality in public spending may worsen in low-income countries. Yet development assistance priorities have the ability to change this.20 # What determines educational finance? ## The big picture ### Why do governments finance education? One of the reasons to justify government intervention in the market for education, is that education generates positive externalities.21 This essentially means that investing in education yields both private and social returns. Private returns to education include higher wages and better employment prospects. Social returns include pro-social behavior (e.g. volunteering, political participation) and interpersonal [trust](https://ourworldindata.org/trust/). The following chart uses OECD results from the Survey of Adult Skills to show how self-reported trust in others correlates with educational attainment. More precisely, this chart plots the percentage-point difference in the likelihood of reporting to trust others, by education level of respondents. Those individuals with upper secondary or post-secondary non-tertiary education are taken as the reference group, so the percentage point difference is expressed in relation to this group. As we can see, in all countries those individuals with tertiary education were by far the group most likely to report trusting others. And in almost every country, those with post-secondary non-tertiary education were more likely to trust others than those with primary or lower secondary education. The OECD's report [Education at a Glance (2015)](http://www.keepeek.com/Digital-Asset-Management/oecd/education/education-at-a-glance-2015_eag-2015-en#page1) provides similar descriptive evidence for other social outcomes. The conclusion is that adults with higher qualifications are more likely to report desirable social outcomes, including good or excellent health, participation in volunteer activities, interpersonal trust, and political efficacy. These results hold after controlling for literacy, gender, age, and monthly earnings. ### Do countries that spend more public resources on education tend to have better education outcomes? Education outcomes are [typically measured](https://ourworldindata.org/quality-of-education) via 'quantity' output (e.g. years of schooling) and 'quality' output (e.g. learning outcomes, such as test scores from the Programme for International Student Assessment – PISA). The following visualization presents three scatter plots using 2010 data to show the cross-country correlation between (i) education expenditure (as a share of GDP), (ii) mean years of schooling, and (iii) mean PISA test scores. At a cross-sectional level, expenditure on education correlates positively with both quantity and quality measures; and not surprisingly, the quality and quantity measures also correlate positively with each other. But obviously correlation does not imply causation: there are many factors that simultaneously affect education spending and outcomes. Indeed, these scatterplots show that despite the broad positive correlation, there is substantial dispersion away from the trend line – in other words, there is substantial variation in outcomes that does not seem to be captured by differences in expenditure. ### Does cross-country variation in government education expenditure explain cross-country differences in education outcomes? The following visualization presents the relationship between PISA reading outcomes and average education spending per student, splitting the sample of countries by income levels. It shows that income is an important factor that affects both expenditure on education and education outcomes: we can see that above a certain national income level, the relationship between PISA scores and education expenditure per pupil becomes virtually nonexistent. Several studies with more sophisticated econometric models corroborate the fact that expenditure on education does not explain well cross-country differences in learning outcomes.25 ## School inputs ### Each education system is different, but improving teacher quality is often more effective in improving learning outcomes than increasing the number of teachers per pupil A vast number of studies have tried to estimate the impact of classroom resources on learning outcomes. The following table summarizes results from the systematic review in Hanushek (2006).26 In this table, the left-hand side summarizes results from econometric studies focusing on developing countries, while the right-hand side presents evidence from the US (where studies have concentrated extensively). We can see that for all listed inputs and across all countries, the share of studies that have found a positive effect is small – in fact, the majority of studies find either no effect or a negative effect. This clearly does not mean that these classroom resources are not important, but rather that it is very difficult to know with confidence when and where they are a binding constraint to improve learning outcomes. A first conclusion, therefore, seems to be that context and input mix are fundamental to improving outcomes – even in developing countries where the expected returns to additional resources is large across the board. Taking the ratio of positive to negative effects detected in the literature as a proxy for what tends to work best, we can derive a second conclusion from the table: spending more resources on better teachers (i.e. improving teacher experience and teacher education) tends to work better to improve learning outcomes than simply increasing the number of teachers per pupil. This seems to be true both in developed and developing countries. This last conclusion is consistent with the main message from the OECD's report [Does money buy strong performance in PISA?](http://www.oecd.org/pisa/pisaproducts/pisainfocus/49685503.pdf), which points out that countries that prioritized the quality of teachers over class sizes performed better in PISA tests.27 This is is also consistent with a recent high-quality study on the impact of teacher quality on test scores using data from the US, which suggests that improvements in teacher quality can causally raise students’ test scores.28 ### Remedial teaching can yield substantial improvements in learning outcomes Education in low-income countries is particularly difficult because there is substantial heterogeneity in the degree of preparation that children have when they enter school – much more so than in high-income countries. Evidence from policy 'experiments' in developing countries suggests remedial teaching, in the form of assistants teaching targeted lessons to the bottom of the class, can yield substantial improvements in learning outcomes. The following visualization summarizes the effects of four different policy treatments within the so-called Teacher Community Assistant Initiative (TCAI) in Ghana – this is an initiative that evaluated four different such remedial teaching interventions.30 The units in this figure are standard deviations of test results. The first two sets of estimates correspond to the test-score impacts of enabling community assistants to provide remedial instruction specifically to low-performing children, either during school or after school. The third set of estimates corresponds to test-score impacts of providing a community assistant and reducing class size, without targeting instruction to low-performing pupils. The last set of results corresponds to testing the effect of training teachers to provide small-group instruction targeted at pupils’ actual learning levels. As we can see, while all interventions had a positive effect, the lowest impacts – across all tests – come from the non-targeted 'normal curriculum' intervention that reduced class sizes, and from the intervention that provided training to teachers on how to engage in targeted remedial teaching themselves. This suggests that the improvements in outcomes were caused by the combination of targeted instruction and TCAs who, unlike teachers, were specifically dedicated to this purpose. These results are consistent with findings from across Africa, suggesting that teaching at the right level causes better learning outcomes in a cost-effective way.31 ### Are pay-for-performance teacher contracts an effective instrument to improve learning outcomes? We have already made the point that the bulk of education expenditure goes specifically towards financing teachers. We have also pointed out that improving teacher quality may be a particularly good instrument to improve teaching outcomes. This leads to a natural question: are pay-for-performance teacher contracts an effective instrument to improve learning outcomes? A growing body of literature in the economics of education has started using randomized control trials (i.e. policy 'experiments') to answer this question. Glewwe and Muralidharan (2016) provide the following account of the available evidence: -- The conclusion is that well-designed pay-for-performance contracts are a cost-effective instrument to boost test scores; but this does not mean that they are necessarily effective at achieving other – perhaps equally important – objectives of time spent in school. In simple words, it is possible that pay-for-performance yields 'teaching to the test'. Other incentive mechanisms, such as community-based monitoring of teachers, have been proposed as an alternative. Glewwe and Muralidharan (2016) also provide a review of the – somewhat limited – available evidence on such alternative incentive mechanisms.34 ## Household inputs ### School attendance and student effort are responsive to incentives Demand-side inputs are as important as supply-side inputs to produce education. Attending school and exerting effort are perhaps the most obvious examples: without these inputs, even the best-endowed schools will fail to deliver good outcomes. The table summarizes information on different demand-side investments that have been shown to successfully improve quality and quantity outcomes. More precisely, this table gathers evidence from randomized control trials in developing countries, as per the review in Glewwe and Muralidharan (2016). The reported figures correspond to positive/negative significant/insignificant estimates across a set of available experimental studies (bear in mind some studies estimate more than one effect – e.g. by measuring outcomes at several points in time). As we can see, the evidence suggests interventions that increase the benefits of attending school – such as conditional cash transfers – are likely to increase student time in school. And those that increase the benefits of higher effort and better academic performance – such as merit scholarships – are likely to improve learning outcomes.35. ### Targeting health problems can be a particularly cost-effective way of increasing school attendance In many low-income countries, health problems are an important factor preventing children from attending school. The following visualization presents a comparison of the impact that a number of different health interventions have achieved in different countries – together with some non-health-related interventions that serve as references. The height of each bar in this graph reflects the additional school years achieved per hundred dollars spent on the corresponding intervention; so these estimates can be interpreted as a measure of how cost-effective the different interventions are.37 We see that treating children for intestinal worms (labeled 'deworming' in the chart) led to an additional 13.9 years of education for every $100 spent in Kenya; while a program targeting anemia (labeled 'iron fortification') led to 2.7 additional years per $100 in India. These interventions seem to be much more cost-effective in improving test scores than conditional cash transfers, free school uniforms, or merit scholarships.38 Of course, ranking these interventions is not trivial since most programs achieve multiple outcomes – indeed, we have already discussed that remedial teaching is generally effective to increase test-scores, although here we see a particular instance where it had no impact on school attendance. Nevertheless, health interventions seem to be particularly interesting, since they lead to substantial achievements in both education _and_ health outcomes.39 ### How important are pre-school investments? The environment that children are exposed to early in life plays a crucial role in shaping their abilities, behavior, and talents. To a great extent, this is what drives large and remarkably persistent gaps in education achievement between individuals in the same country, but in different socioeconomic environments. Cunha et al. (2006) provide a detailed account of the theory and evidence behind this claim and discuss its implications for the design of education policies. In the chart, we see the impacts of the Perry Preschool Program – a flagship experimental intervention study, designed to test the impact of preschool education on subsequent education outcomes.41 The chart shows disadvantaged children participating in the preschool program (the 'treatment group') had higher grades and were more likely to graduate from high school than the reference control group. Moreover, they spent substantially less time in special education. Other programs have similarly shown evidence of very large and persistent returns to early education interventions. See the Wikipedia entry on [compulsory education](https://en.wikipedia.org/wiki/Compulsory_education) for a table of the ages of compulsory schooling around the world. As per estimates from Adam Szirmai, (2015) [The Dynamics of Socio-Economic Development](https://web.archive.org/web/20191223132520/http://www.dynamicsofdevelopment.com:80/). As per estimates of Gini coefficients for the distribution of school years in Crespo Cuaresma, J., KC, S., & Sauer, P. (2013). [Age-specific education inequality, education mobility and income growth](http://www.ecineq.org/ecineq_bari13/FILESxBari13/CR2/p100.pdf) (No. 6). WWWforEurope. As per estimates reported in Steer L. and K. Smith (2015), [Financing education: Opportunities for global action](http://files.eric.ed.gov/fulltext/ED568940.pdf). Center for Universal Education. As per [2015 enrolment estimates from the NCES.](https://nces.ed.gov/programs/digest/d15/tables/dt15_105.20.asp) An article [from the Huffington Post](http://www.huffingtonpost.com/2015/03/13/arne-duncan-school-funding-disparities_n_6864866.html) highlights this point, including interesting visualizations documenting the important role that federal funding plays in reducing expenditure inequalities. Lindert, Peter H. _Growing public: Volume 1, the story: Social spending and economic growth since the eighteenth century_. Vol. 1. Cambridge University Press, 2004. Lindert, Peter H. _Growing Public: Volume 1, the story: Social spending and economic growth since the eighteenth century_. Vol. 1. Cambridge University Press, 2004. Bear in mind that the estimates from the National Center for Education Statistics are not broken down by source of funds. Rather, they show expenditure by type of institution – which is not equivalent, since public institutions may spend private resources, and vice versa. In 2010, high-income countries spent 6721 US PPP dollars per primary school pupil. Low-income countries, in contrast, spent 115 US PPP dollars per pupil (UNESCO EFA Global Monitoring Report 2014). Jesus Crespo Cuaresma, Samir K.C., and Petra Sauer (2013) – [Age-Specific Education Inequality, Education Mobility and Income Growth](https://web.archive.org/web/20170811085131/http://www.foreurope.eu/fileadmin/documents/pdf/Workingpapers/WWWforEurope_WPS_no006_MS15.pdf). WWWforEurope working paper; Working Paper no 6. Data from Petra Sauer (2016) – [The Role of Age and Gender in Education Expansion](http://epub.wu.ac.at/5186/). Working Paper. Strictly speaking, for this spending pattern to be truly progressive there must be subsidies or income-contingent loans to guarantee that low-income students can also access tertiary education and reap the private benefits from this type of investment. The OECD provides country-specific figures. However, there is relatively little variation across OECD countries in this respect. This is explained by near-universal enrolment rates at these levels of education and the demographic structure of the population. This is a stylized fact of OECD education spending. In all the OECD countries, the share of spending devoted to the compensation of teachers is by far the largest component of current expenditure. Moreover, expenditure on teachers' compensation is larger at the combined primary, secondary, and post-secondary non-tertiary levels of education than at the tertiary level. See Table B6.2 in [Education at a Glance (2015)](http://www.keepeek.com/Digital-Asset-Management/oecd/education/education-at-a-glance-2015_eag-2015-en#page1) for details on the breakdown of current expenditure across all OECD countries by education level. Welch, F., & Hanushek, E. A. (2006). Handbook of the Economics of Education, Two Volumes. North Holland. Steer L. and K. Smith (2015), [Financing education: Opportunities for global action](http://files.eric.ed.gov/fulltext/ED568940.pdf). Center for Universal Education. Available Online from the Brookings Institution The share of development assistance going to sub-Saharan Africa has decreased as a whole – from 55 percent in 2002 to 40 percent in 2013 –, but as we note the drop specifically for primary education has been steeper. Steer L. and K. Smith (2015), [Financing education: Opportunities for global action](http://files.eric.ed.gov/fulltext/ED568940.pdf). Center for Universal Education. The conclusion from these figures is that, while public spending does reduce education inequality in low-income countries, remaining inequalities could be further reduced by shifting resources towards lower levels of education. This evidently does not mean that resources _should_ be shifted – low-income countries and aid donors may have other objectives apart from reducing inequality. But the case for reducing inequality at the bottom is very strong, and some studies suggest that returns to education at the primary level might be higher than at post-primary levels in low-income countries (for a discussion of the vast literature on returns to education, and the ongoing debate on the validity of estimates, see Heckman, J. J., Lochner, L. J., & Todd, P. E. (2006). Earnings functions, rates of return and treatment effects: The Mincer equation and beyond. Handbook of the Economics of Education, 1, 307-458. ). That positive externalities justify government intervention in the provision of education is essentially an efficiency argument. The logic is that individuals may not spend enough on education because they fail to internalize the positive effect that their education has on other people. But there are, of course, also equity arguments to justify government intervention in the provision of education – for instance, reducing inequality in education may be of intrinsic value, or may be instrumental in reducing inequalities in other outcomes. As per the source notes: ""Percentage-point difference reflects the relative change of reporting to trust others compared to the reference category. For example, in Norway, the percentage of individuals with tertiary education reporting to trust others increases by 20 percentage points compared to someone who has upper secondary or post-secondary non-tertiary education. Similarly, after accounting for literacy proficiency, the percentage of individuals with tertiary education increases by 16 percentage points compared to someone who has upper secondary or post-secondary non-tertiary education."" Data on expenditure corresponds to 2010 total government education expenditure across all levels, as a share of GDP (source: World Bank Education Statistics). Data on PISA scores corresponds to 2010 mean average test scores across categories – mathematics, reading, and science (source: OECD PISA). Data on years of schooling corresponds to 2010 mean years of schooling for the population aged 15 and over (source: Barro Lee Education dataset) Does money buy strong performance in PISA? - OECD. Available online [here](http://www.oecd.org/pisa/pisaproducts/pisainfocus/49685503.pdf). For a discussion of the evidence supporting this claim, see Hanushek, E. A., (2006). School Resources. Handbook of the Economics of Education, 2. Hanushek, E. A., (2006). School Resources. Handbook of the Economics of Education, Volume 2. Elsevier. This claim is clearly only descriptive since there are many underlying variables that simultaneously drive teacher characteristics and student outcomes in any particular country. Indeed, most of the available evidence on whether teacher quality and quantity matters is difficult to interpret causally, as it is hard to find instances where teacher quality/quantity varies exogenously. A recent study concludes on the topic: _ ""teachers vary in many ways, but we found no high-quality studies that have examined the impact of teacher characteristics on student learning or time in school"" _(source: page 696, Glewwe, P. and Muralidharan, K. (2016) [Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications](https://www.sciencedirect.com/science/article/pii/B9780444634597000105). Handbook of the Economics of Education, Volume 5. ) Chetty, Raj, John N. Friedman, and Jonah E. Rockoff. 2014. “Measuring the Impacts of Teachers I: Evaluating Bias in Teacher Value-Added Estimates.” American Economic Review, 104(9): 2593-26 Hanushek, E. A., (2006). School Resources. Handbook of the Economics of Education, 2. Further details in Innovations for Poverty Action, 2014. I[mplementation Lessons: The Teacher Community Assistant Initiative (TCAI)](https://poverty-action.org/sites/default/files/publications/TCAI_Final%20Results_040115.pdf). For further details, see: Glewwe, P. and Muralidharan, K. (2016) [Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications](https://www.sciencedirect.com/science/article/pii/B9780444634597000105). Handbook of the Economics of Education, Volume 5. Elsevier. (Link to working paper) Innovations for Poverty Action (2014). [Implementation Lessons: The Teacher Community Assistant Initiative (TCAI)](http://www.poverty-action.org/sites/default/files/publications/TCAI_Final%20Results_040115.pdf). Glewwe, P. and Muralidharan, K. (2016) [Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications](https://www.sciencedirect.com/science/article/pii/B9780444634597000105). Handbook of the Economics of Education, Volume 5. Elsevier. They conclude that ""evidence on the impact of monitoring on time in school is scarce and not encouraging...[while] the evidence of the impact of monitoring on student learning is only somewhat more encouraging"" See Glewwe and Muralidharan 2016 for further details on the underlying policy interventions, plus further evidence and discussion of results Glewwe, P. and Muralidharan, K. (2016) [Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications](https://www.sciencedirect.com/science/article/pii/B9780444634597000105). Handbook of the Economics of Education, Volume 5. Elsevier. (Link only to working paper) Bear in mind that the reported gains in school years are a measure of the _total_ impact of the program across the treated population, rather than impact per treated student. Further information on cost-effectiveness analysis is available from the source of the graph. Further details on all interventions available in: Dhaliwal, I., Duflo, E., Glennerster, R., & Tulloch, C. (2013). [Comparative cost-effectiveness analysis to inform policy in developing countries: a general framework with applications for education](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.570.2412&rep=rep1&type=pdf). Education Policy in Developing Countries, 285-338. For an analysis of the literature on the impacts of mass deworming see: Croke, Kevin, Joan Hamory Hicks, Eric Hsu, Michel Kremer, and Edward Miguel. 2016. “[Does Mass Deworming Affect Child Nutrition? Meta-analysis, Cost-effectiveness, and Statistical Power](http://scholar.harvard.edu/files/kremer/files/deworming-nber_2016-06-30.pdf).” Working Paper. Dhaliwal, I., Duflo, E., Glennerster, R., & Tulloch, C. (2013). [Comparative cost-effectiveness analysis to inform policy in developing countries: a general framework with applications for education](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.570.2412&rep=rep1&type=pdf). Education Policy in Developing Countries, 285-338. More specifically, the Perry preschool 'experiment' consisted of enrolling 65 randomly selected black children in a pre-school program, and comparing their outcomes later in life against those achieved by a control group of roughly the same size. The treatment consisted of a daily 2.5-hour classroom session on weekday mornings and a weekly 90-minute home visit by the teacher on weekday afternoons to involve the mother in the child's educational process. More information and details on the intervention are available in Cunha et al. (2006). Cunha, F., Heckman, J. J., Lochner, L., & Masterov, D. V. (2006). [Interpreting the evidence on life cycle skill formation](http://discovery.ucl.ac.uk/2559/1/2559.pdf). Handbook of the Economics of Education, 1, 697-812.",Education Spending 1qXuRnsozpmvOQymlHztn2-KPcGUJFPPd5J7jOamzQ7A,time-use,linear-topic-page,"{""toc"": [{""slug"": ""time-use-from-the-perspective-of-an-average-day"", ""text"": ""Time use from the perspective of an average day"", ""title"": ""Time use from the perspective of an average day"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-do-people-across-the-world-spend-their-time-and-what-does-this-tell-us-about-living-conditions"", ""text"": ""How do people across the world spend their time and what does this tell us about living conditions?"", ""title"": ""How do people across the world spend their time and what does this tell us about living conditions?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""time-use-from-the-perspective-of-the-life-cycle"", ""text"": ""Time use from the perspective of the life cycle"", ""title"": ""Time use from the perspective of the life cycle"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""who-do-we-spend-time-with-across-our-lifetime"", ""text"": ""Who do we spend time with across our lifetime?"", ""title"": ""Who do we spend time with across our lifetime?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""additional-information"", ""text"": ""Additional information"", ""title"": ""Additional information"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""all-charts"", ""text"": ""Interactive charts on time use"", ""title"": ""Interactive charts on time use"", ""isSubheading"": false}, {""slug"": ""article-endnotes"", ""text"": ""Endnotes"", ""title"": ""Endnotes"", ""isSubheading"": false}, {""slug"": ""article-citation"", ""text"": ""Citation"", ""title"": ""Citation"", ""isSubheading"": false}, {""slug"": ""article-licence"", ""text"": ""Licence"", ""title"": ""Licence"", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Time is the ultimate limited resource. Every one of us has the same “time budget” — 24 hours per day, 365 days per year, giving a total of 8,760 hours — "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/life-expectancy"", ""children"": [{""text"": ""each year of our lives"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How do we spend our time? There are many commonalities across the world: we all sleep, work, eat, and enjoy leisure time. But there are also important differences in the freedom people have to spend time on the things they value most."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Studying how people across the world spend their time provides an important perspective for understanding living conditions, economic opportunities, and general well-being."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Here we present the data on time use. We explore how it differs across countries and over time and how these differences matter for people’s lives."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On our related topic page on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/working-hours"", ""children"": [{""text"": ""Working Hours"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", you can read more about people’s time spent working and how this varies around the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""url"": ""#all-charts"", ""children"": [{""text"": ""See all interactive charts on time use ↓"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""text"": [{""text"": ""Related topics"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1H0uMTbbF9NlaX2vyHdKHRxEBwJbQpeuoQcF6Fhue7dE/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1fZviDn01DnCXZo2Hw_FkfyZo4S5UZMzey_zof3s-iPE/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""text"": [{""text"": ""Time use from the perspective of an average day"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""How do people across the world spend their time and what does this tell us about living conditions?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Sleep, work, eat, leisure — at a high level most of us spend time on similar activities. But just how similar are the daily activities of people across the world?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is something worth considering, not just to serve our curiosity but because differences in the way we spend time give us meaningful perspectives on living conditions, economic opportunities, and general well-being."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Here we take a look at the data on time use. We explore some of the key patterns that emerge from cross-country time use surveys, and then dig deeper to understand how these differences matter for people’s well-being."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Daily activities: similarities and differences across countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart below we compare the average time spent across several common activities."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The data comes from the OECD and brings together estimates from time diaries where respondents are asked to record the sequence of what they did over a specific day, as well as from general questionnaires where respondents are asked to recall the amount of time spent on different activities on a specific day in the previous week."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""How do people spend their time?"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" "", ""spanType"": ""span-simple-text""}, {""url"": ""https://docs.google.com/spreadsheets/d/1Cmav9S-bNUd-54HHXPRD1x-k3nDCceJPH_QXwQb1beU/"", ""children"": [{""text"": ""Download the underlying data for this chart (Google Sheets)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""filename"": ""Time-Use-by-Country-OECD.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The first thing that jumps out from this chart is that there are indeed many similarities across countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is not surprising — most of us try to split our days into “work, rest, and fun”, and so there are some predictable patterns. We spend the most time working and sleeping. Together, paid work, housework, leisure, eating, and sleeping take 80–90% of all the 1440 minutes in a day."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Work is an important activity where we see large differences. Countries are sorted by paid work hours in the chart — from highest to lowest. On an average day, people in China and Mexico spend almost twice as much time on paid work as people in Italy and France do. This is a general pattern: "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/working-hours"", ""children"": [{""text"": ""people in richer countries can afford to work less"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Keep in mind that this chart shows the average for all people in the working age bracket, from 15 to 64 years, whether or not they are employed."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Read more on our page on working hours:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1H0uMTbbF9NlaX2vyHdKHRxEBwJbQpeuoQcF6Fhue7dE/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Differences in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/age-structure"", ""children"": [{""text"": ""demographics"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/global-education"", ""children"": [{""text"": ""education"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/economic-growth"", ""children"": [{""text"": ""economic prosperity"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" all contribute to these inequalities in work and time use."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But what’s clear in the chart here is that some differences in time use are not well explained by economic or demographic differences. In the UK, for example, people spend more time working than in France, but in both countries, people report spending a similar amount of time on leisure activities."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Cultural differences are likely to play a role here. The French seem to spend much more time eating than the British — and in this respect, the data goes in line with stereotypes about food culture. People in France, Greece, Italy, and Spain report spending more time eating than people in most other European countries. The country where people spend the least time eating and drinking is the USA."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Going beyond averages: The gender gap in leisure time"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Going beyond national averages reveals important inequalities within countries. The gender gap in leisure time, for example, is a key dimension along which large inequalities exist."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart here relies on the same time-use data described above but it shows total leisure time for men and women separately."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Time for men is shown on the horizontal axis, while time for women appears on the vertical axis. The dotted diagonal line denotes \""gender parity\"", so the further away a country is from the diagonal line, the larger the difference between men and women."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/minutes-spent-on-leisure?tab=chart&stackMode=absolute&time=latest&country=®ion=World"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we can see, in all countries the average leisure time for men is higher than for women — all bubbles are below the diagonal line — but in some countries, the gaps are much larger. In Norway, the difference is very small, while in Portugal men report much more leisure time than women."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A key factor driving these differences in leisure time is the gender gap in unpaid work. As we explain in detail on our page on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/female-labor-supply"", ""children"": [{""text"": ""women’s employment"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", women are responsible for a disproportionate amount of unpaid work and have less leisure as a result."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1lxxDxbccgq8wSQyrkO8EnI9iSFayaGrWvTkfSFJ0z-c/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""text"": [{""text"": ""Why should we care about differences in time use?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Every single one of us has the same “time budget”: 24 hours per day and 365 days per year. But of course, not all of us can choose to spend time on the activities that we enjoy most. Differences in our freedom to allocate time to the things we enjoy are the main reasons why time-use data is important for studying living conditions."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the UK, researchers from the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.timeuse.org/"", ""children"": [{""text"": ""Centre for Time Use Research"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" linked time-use diaries with the respondents’ assessments of enjoyment, on a scale from 1 to 7, to better understand the connection between time use and well-being."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart here, which we’ve adapted from the book \""What We Really Do All Day’"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "", "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""by professors Jonathan Gershuny and Oriel Sullivan, shows the results. The estimates correspond to average reported levels of enjoyment for each activity, with confidence intervals."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""How do people rate the enjoyment of different activities?"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Enjoyment-Level-of-Time-Use.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see that the most-enjoyed activities involve rest or leisure activities — such as eating out, sleeping, going to sports events, playing computer games, or attending cultural performances. The activities receiving the lowest ratings include doing school homework, looking for a job, or doing housework."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The activity where people show the greatest variation in enjoyment is working a “Second Job”. This likely reflects the difference between people who work a second job because they want to, and those who work a second job because they "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""have to"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""So what do we learn from this?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""First, we learn that the enjoyment of activities is, at least to some degree, predictable and stable. This means we can take activity groups and make meaningful comparisons across groups of people. Economists, for example, often classify any activity with an enjoyment level below work as a “non-leisure activity”, to measure trends in leisure across people and time."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But beyond this, and more importantly, this confirms that time-use is informative about well-being."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The fact that there is a very clear and predictable pattern in the enjoyment of activities suggests that differences in time use do, indeed, give us meaningful perspectives on living conditions and economic opportunities. In countries where people do more paid and unpaid work, and have less time for leisure, their enjoyment — and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/happiness-and-life-satisfaction"", ""children"": [{""text"": ""happiness and life satisfaction"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" — levels are likely to be lower."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Time use from the perspective of the life cycle"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Who do we spend time with across our lifetime?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we go through life we build personal relationships with different people — family, friends, coworkers, partners."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These relationships, which are "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/social-connections-and-loneliness#social-connections"", ""children"": [{""text"": ""deeply important to all of us"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", evolve with time. As we grow older we build new relationships while others transform or fade, and towards the end of life, many of us spend a lot of time alone."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Taking the big picture over the entire life course: Who do we actually spend our time with?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""From adolescence to old age: who do we spend our time with?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To understand how social connections evolve throughout our lives we can look at survey data on "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""how much"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" time people spend with others, and "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""who"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" that time is spent with."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart here shows the amount of time that people in the US report spending in the company of others, based on their age."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The data comes from time-use surveys, where people are asked to list all the activities that they perform over a full day, and the people who were there during each activity. We currently only have data with this granularity for the US — time-use surveys are common across many countries, but what is special about the US is that respondents of the American Time Use Survey are asked to list "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""everyone"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" who was present during each activity."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/time-spent-with-relationships-by-age-us?tab=chart&stackMode=absolute®ion=World"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The numbers in this chart are based on averages for a cross-section of American society — people are only interviewed once, but we have brought together a decade of surveys, tabulating the average amount of time that survey respondents of different ages report spending with other people"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""Who we spend our time with changes a lot over the course of life"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When we’re young — particularly in our teens — we spend a lot of our time with friends, parents, siblings, and extended family."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we enter our 20s, time with friends, siblings, and parents starts to drop off quickly. Instead, we start spending an increasing amount of time with partners and children. The chart shows an average across Americans, so for those that have children the time spent with children is even higher, since the average is pulled down by those without children."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As the chart shows, this continues throughout our 30s, 40s, and 50s — over this period of their life, Americans spend much of their time with partners, children, and, unsurprisingly, co-workers."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For those 60 and older, we see a significant drop-off in time spent with co-workers. This makes sense, considering many people in the US "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/average-effective-retirement-men"", ""children"": [{""text"": ""enter retirement in their mid-60s"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". We see that this time is partly displaced by more time with partners."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How about the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""number"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of people we interact with? This chart suggests that the number of people with whom we interact is highest around 40, but then things change substantially after that. And this is perhaps the most conspicuous trend in the chart: above 40, people spend an increasing amount of time alone."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Older people spend a lot of time alone"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Older people spend a large amount of time alone and it is understandable why — time spent alone increases with age because this is when health typically deteriorates and people lose relatives and friends."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Indeed, many people who are older than 60 live alone as this chart shows clearly: living alone is particularly common for older adults."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another interesting point here is that the share of people across all age groups who live alone has been rising over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is part of a more general global trend — if you want to read more about the global \""rise of living alone\"", we provide a detailed account of this trend across countries in this article:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/19H4I3tfW-m1skufkSCiVn4mQtU-H2Bw0eD7Wrb1ebYg/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/percentage-of-americans-living-alone-by-age"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Older people spend more time alone, but this doesn’t necessarily mean they’re lonely"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The data shows that as we become older we tend to spend more and more time alone. What’s more, the data also shows that older people today spend more time alone than older people did in the past."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We might think older people are therefore more lonely — but this is not necessarily the case."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Spending time alone is not the same as feeling lonely. This is a point that is well recognized by researchers, and one which has been confirmed empirically across countries. Surveys that ask people about living arrangements, time use, and feelings of loneliness find that solitude, by itself, is not a good predictor of loneliness."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can read our overview of the evidence in our article:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1o4VRXc0grrTvoZ4IfJ6i9Cne93E_-81w9KcPnErDr7I/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""So, what about loneliness? If we focus on self-reported loneliness, there is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/loneliness-epidemic#loneliness-trends-over-time-are-people-lonelier-today-than-in-the-past"", ""children"": [{""text"": ""little evidence"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of an upward trend over time in the US, and importantly, it’s "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/social-connections-and-loneliness#do-we-become-lonelier-as-we-get-older"", ""children"": [{""text"": ""not the case"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that loneliness keeps going up as we become older."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In fact, a recent study based on surveys that track the same individuals over time found that after age 50 — which is the earliest age of participants in the analysis — loneliness tended to decrease, until about 75, after which it began to increase again."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Taking the evidence together, the message is not that we should be sad about the prospect of aging, but rather that we should recognize the fact that social connections are complex."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We often look at the amount of time spent with others as a marker of social well-being — but the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""quality"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of time spent with others, and our expectations, matter even more for our feelings of connection and loneliness."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""gray-section"", ""items"": [{""text"": [{""text"": ""Additional information"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""expandable-paragraph"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""In the chart where we plot the amount of time that people in the US report spending in the company of others, it’s important to keep in mind that we are taking a look at a cross-section of society. This means that we are actually seeing the result of two underlying trends."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On one hand, we see the effect of aging on social connections (we relate to different people and reallocate time as we go through different stages of life), but we also see the effect of cohort trends (compared to people in the past, today’s older generations in the US tend to be "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/life-expectancy?tab=chart&country=JPN~GBR~IND~ETH~OWID_WRL~KOR~ZAF~USA®ion=World"", ""children"": [{""text"": ""healthier"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/average-real-gdp-per-capita-across-countries-and-regions?tab=chart&country=Western%20Offshoots~Western%20Europe~Western%20Asia~Eastern%20Europe~OWID_WRL~Latin%20America~East%20Asia~Africa~USA"", ""children"": [{""text"": ""richer"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and might also have different expectations, preferences, and opportunities)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Disentangling these two effects is difficult, so it is important to keep in mind that at least some of the age gradients we observe might be partly explained by cohort changes, rather than life-cycle trends."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is why it’s important to rely not only on cross-sectional data but also on surveys that track the same individuals over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}], ""parseErrors"": []}, {""top"": [], ""type"": ""all-charts"", ""heading"": ""Interactive charts on time use"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""241c9b912ac589b46510fc8ede99ed4a4cbea1ca"": {""id"": ""241c9b912ac589b46510fc8ede99ed4a4cbea1ca"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""When interpreting this chart it’s important to bear in mind that the relationships used to categorize people are not exhaustive (i.e., survey respondents could also list being with people who didn’t fit any of the listed categories, or for whom a relationship was unclear or unknown — we do not count these instances in the estimates). Additionally, time spent with multiple people can be counted more than once; so attending a party with friends and your spouse, for example, would show up for both “friends” and “partner” in our estimates. The implication is that companion categories cannot be stacked to add up the total time spent in the company of others."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""2dd335156ec32b6749c016e2415d27694e1fee3e"": {""id"": ""2dd335156ec32b6749c016e2415d27694e1fee3e"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""OECD (2020) "", ""spanType"": ""span-simple-text""}, {""url"": ""https://stats.oecd.org/Index.aspx?DataSetCode=TIME_USE"", ""children"": [{""text"": ""Time Use Database."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3efd81351a18d9817bf7b5cf4ba8279ce01338ce"": {""id"": ""3efd81351a18d9817bf7b5cf4ba8279ce01338ce"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""If you want to dig deeper you can explore gender differences across all other activities directly from our source, via the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://stats.oecd.org/Index.aspx?DataSetCode=TIME_USE"", ""children"": [{""text"": ""OECD Data Portal"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". You can read more about within-country inequalities in time use along other dimensions, such as income and education, in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://scholar.google.com/scholar?hl=es&as_sdt=0%2C5&q=The+Middle+Class+Time+Squeeze%2C+Economic+Studies+at+Brookings%2C+Sawhill+and+Guyot+%282020%29&btnG="", ""children"": [{""text"": ""this Brookings Paper"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", where the authors focus on the \""middle-class time squeeze\"" in the US."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Sawhill, I. V., & Guyot, K. (2020). The Middle Class Time Squeeze. Economic Studies at Brookings. Brookings Institution."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3f6005788b2035e0b9f9262fac8a55d226f2c160"": {""id"": ""3f6005788b2035e0b9f9262fac8a55d226f2c160"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The \""time-diary method\"" is generally more reliable and allows a richer analysis of routines, because it measures not only aggregate times but also sequences and clock-times. Time-diary data is less common, but it is available for some countries from the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.timeuse.org/mtus"", ""children"": [{""text"": ""Multinational Time Use Study"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". We explore time-use \""tempograms\"" from the MTUS in a forthcoming companion post."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""5b6273e76d4a210a821626e74f840b1de8e01d72"": {""id"": ""5b6273e76d4a210a821626e74f840b1de8e01d72"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Because these estimates include people who are not employed, they are much lower than the estimates of working hours "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""per worker"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""url"": ""https://ourworldindata.org/time-use#time-spent-working"", ""children"": [{""text"": "" present elsewhere"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The estimates also differ because of differences in the sources: time-use surveys compared to labor force surveys and national accounts data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""bb7eae1503107430422c03656fbc4d882199ce1e"": {""id"": ""bb7eae1503107430422c03656fbc4d882199ce1e"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Hawkley, L. C., Wroblewski, K., Kaiser, T., Luhmann, M., & Schumm, L. P. (2019). Are US older adults getting lonelier? Age, period, and cohort differences. Psychology and Aging, 34(8), 1144."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c6cae9150c7af70ec1a49aa2b98abfd580e72ae0"": {""id"": ""c6cae9150c7af70ec1a49aa2b98abfd580e72ae0"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The underlying data comes from time-use diaries where respondents are asked to record the sequence of what they do over a specific day, and how much they enjoy each \""episode\"" (i.e. what they do) on a scale from 1 to 7. All episodes reported are then coded and grouped into similar activities. To arrive at the mean enjoyment scores, the authors multiply the duration of each episode where the activity category concerned is the primary activity recorded, by the enjoyment level to arrive at the total enjoyment score for that episode. Then they sum these total enjoyment scores for each category of activity across the day, and finally divide these daily enjoyment total scores for each activity by the amount of time devoted to the activity. In this way, they arrive at an appropriately weighted mean enjoyment level for each activity across all those who engage in it. For more details see Gershuny, J., & Sullivan, O. (2019). What We Really Do All Day: Insights from the Centre for Time Use Research. Penguin UK."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""db82871982fdeaa5834ebad3345a59b9222a0918"": {""id"": ""db82871982fdeaa5834ebad3345a59b9222a0918"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Gershuny, J., & Sullivan, O. (2019). What We Really Do All Day: Insights from the Centre for Time Use Research. Penguin UK."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""e45e490c5d2341c42a58a2277a34e0a270bc58fc"": {""id"": ""e45e490c5d2341c42a58a2277a34e0a270bc58fc"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""You can find a very clear and complete explanation of this in Ramey, V. A., & Francis, N. (2009). A century of work and leisure. American Economic Journal: Macroeconomics, 1(2), 189-224."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""linear-topic-page"", ""title"": ""Time Use"", ""authors"": [""Esteban Ortiz-Ospina"", ""Charlie Giattino"", ""Max Roser""], ""excerpt"": ""How do people across the world spend their time? How do daily activities differ across countries, and how do these differences matter for people’s lives? Explore data and research on time use."", ""dateline"": ""This page was first published in November 2020, and last revised in February 2024."", ""subtitle"": ""How do people across the world spend their time? How do daily activities differ across countries, and how do these differences matter for people’s lives? Explore data and research on time use."", ""sidebar-toc"": true, ""featured-image"": ""time-use-thumbnail.png""}",1,2023-11-09 14:57:22,2020-11-29 11:10:48,2024-02-29 11:14:23,unlisted,ALBJ4LuR81ZUeOxUKA-RlJ0MdzNJ5zT5zNqfCiyycVrNTccUO_ONJTL0pSSPK4iRe4tMlcVTVdGtUHwNF7M1XA,,"Time is the ultimate limited resource. Every one of us has the same “time budget” — 24 hours per day, 365 days per year, giving a total of 8,760 hours — [each year of our lives](https://ourworldindata.org/life-expectancy). How do we spend our time? There are many commonalities across the world: we all sleep, work, eat, and enjoy leisure time. But there are also important differences in the freedom people have to spend time on the things they value most. Studying how people across the world spend their time provides an important perspective for understanding living conditions, economic opportunities, and general well-being. Here we present the data on time use. We explore how it differs across countries and over time and how these differences matter for people’s lives. On our related topic page on [Working Hours](https://ourworldindata.org/working-hours), you can read more about people’s time spent working and how this varies around the world. **[See all interactive charts on time use ↓](#all-charts)** ### Related topics ### undefined undefined https://docs.google.com/document/d/1H0uMTbbF9NlaX2vyHdKHRxEBwJbQpeuoQcF6Fhue7dE/edit ### undefined undefined https://docs.google.com/document/d/1fZviDn01DnCXZo2Hw_FkfyZo4S5UZMzey_zof3s-iPE/edit # Time use from the perspective of an average day ## How do people across the world spend their time and what does this tell us about living conditions? Sleep, work, eat, leisure — at a high level most of us spend time on similar activities. But just how similar are the daily activities of people across the world? This is something worth considering, not just to serve our curiosity but because differences in the way we spend time give us meaningful perspectives on living conditions, economic opportunities, and general well-being. Here we take a look at the data on time use. We explore some of the key patterns that emerge from cross-country time use surveys, and then dig deeper to understand how these differences matter for people’s well-being. ### Daily activities: similarities and differences across countries In the chart below we compare the average time spent across several common activities. The data comes from the OECD and brings together estimates from time diaries where respondents are asked to record the sequence of what they did over a specific day, as well as from general questionnaires where respondents are asked to recall the amount of time spent on different activities on a specific day in the previous week.1 The first thing that jumps out from this chart is that there are indeed many similarities across countries. This is not surprising — most of us try to split our days into “work, rest, and fun”, and so there are some predictable patterns. We spend the most time working and sleeping. Together, paid work, housework, leisure, eating, and sleeping take 80–90% of all the 1440 minutes in a day. Work is an important activity where we see large differences. Countries are sorted by paid work hours in the chart — from highest to lowest. On an average day, people in China and Mexico spend almost twice as much time on paid work as people in Italy and France do. This is a general pattern: [people in richer countries can afford to work less](https://ourworldindata.org/working-hours). Keep in mind that this chart shows the average for all people in the working age bracket, from 15 to 64 years, whether or not they are employed.3 Read more on our page on working hours: ### undefined undefined https://docs.google.com/document/d/1H0uMTbbF9NlaX2vyHdKHRxEBwJbQpeuoQcF6Fhue7dE/edit Differences in [demographics](https://ourworldindata.org/age-structure), [education](https://ourworldindata.org/global-education), and [economic prosperity](https://ourworldindata.org/economic-growth) all contribute to these inequalities in work and time use. But what’s clear in the chart here is that some differences in time use are not well explained by economic or demographic differences. In the UK, for example, people spend more time working than in France, but in both countries, people report spending a similar amount of time on leisure activities. Cultural differences are likely to play a role here. The French seem to spend much more time eating than the British — and in this respect, the data goes in line with stereotypes about food culture. People in France, Greece, Italy, and Spain report spending more time eating than people in most other European countries. The country where people spend the least time eating and drinking is the USA. ### Going beyond averages: The gender gap in leisure time Going beyond national averages reveals important inequalities within countries. The gender gap in leisure time, for example, is a key dimension along which large inequalities exist. The chart here relies on the same time-use data described above but it shows total leisure time for men and women separately. Time for men is shown on the horizontal axis, while time for women appears on the vertical axis. The dotted diagonal line denotes ""gender parity"", so the further away a country is from the diagonal line, the larger the difference between men and women. As we can see, in all countries the average leisure time for men is higher than for women — all bubbles are below the diagonal line — but in some countries, the gaps are much larger. In Norway, the difference is very small, while in Portugal men report much more leisure time than women. A key factor driving these differences in leisure time is the gender gap in unpaid work. As we explain in detail on our page on [women’s employment](https://ourworldindata.org/female-labor-supply), women are responsible for a disproportionate amount of unpaid work and have less leisure as a result.4 ### undefined undefined https://docs.google.com/document/d/1lxxDxbccgq8wSQyrkO8EnI9iSFayaGrWvTkfSFJ0z-c/edit ### Why should we care about differences in time use? Every single one of us has the same “time budget”: 24 hours per day and 365 days per year. But of course, not all of us can choose to spend time on the activities that we enjoy most. Differences in our freedom to allocate time to the things we enjoy are the main reasons why time-use data is important for studying living conditions. In the UK, researchers from the [Centre for Time Use Research](https://www.timeuse.org/) linked time-use diaries with the respondents’ assessments of enjoyment, on a scale from 1 to 7, to better understand the connection between time use and well-being. The chart here, which we’ve adapted from the book ""What We Really Do All Day’_, _by professors Jonathan Gershuny and Oriel Sullivan, shows the results. The estimates correspond to average reported levels of enjoyment for each activity, with confidence intervals.5 We see that the most-enjoyed activities involve rest or leisure activities — such as eating out, sleeping, going to sports events, playing computer games, or attending cultural performances. The activities receiving the lowest ratings include doing school homework, looking for a job, or doing housework. The activity where people show the greatest variation in enjoyment is working a “Second Job”. This likely reflects the difference between people who work a second job because they want to, and those who work a second job because they _have to_. So what do we learn from this? First, we learn that the enjoyment of activities is, at least to some degree, predictable and stable. This means we can take activity groups and make meaningful comparisons across groups of people. Economists, for example, often classify any activity with an enjoyment level below work as a “non-leisure activity”, to measure trends in leisure across people and time.7 But beyond this, and more importantly, this confirms that time-use is informative about well-being. The fact that there is a very clear and predictable pattern in the enjoyment of activities suggests that differences in time use do, indeed, give us meaningful perspectives on living conditions and economic opportunities. In countries where people do more paid and unpaid work, and have less time for leisure, their enjoyment — and [happiness and life satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction) — levels are likely to be lower. # Time use from the perspective of the life cycle ## Who do we spend time with across our lifetime? As we go through life we build personal relationships with different people — family, friends, coworkers, partners. These relationships, which are [deeply important to all of us](https://ourworldindata.org/social-connections-and-loneliness#social-connections), evolve with time. As we grow older we build new relationships while others transform or fade, and towards the end of life, many of us spend a lot of time alone. Taking the big picture over the entire life course: Who do we actually spend our time with? ### From adolescence to old age: who do we spend our time with? To understand how social connections evolve throughout our lives we can look at survey data on _how much_ time people spend with others, and _who_ that time is spent with. The chart here shows the amount of time that people in the US report spending in the company of others, based on their age. The data comes from time-use surveys, where people are asked to list all the activities that they perform over a full day, and the people who were there during each activity. We currently only have data with this granularity for the US — time-use surveys are common across many countries, but what is special about the US is that respondents of the American Time Use Survey are asked to list _everyone_ who was present during each activity. The numbers in this chart are based on averages for a cross-section of American society — people are only interviewed once, but we have brought together a decade of surveys, tabulating the average amount of time that survey respondents of different ages report spending with other people_._8 ### Who we spend our time with changes a lot over the course of life When we’re young — particularly in our teens — we spend a lot of our time with friends, parents, siblings, and extended family. As we enter our 20s, time with friends, siblings, and parents starts to drop off quickly. Instead, we start spending an increasing amount of time with partners and children. The chart shows an average across Americans, so for those that have children the time spent with children is even higher, since the average is pulled down by those without children. As the chart shows, this continues throughout our 30s, 40s, and 50s — over this period of their life, Americans spend much of their time with partners, children, and, unsurprisingly, co-workers. For those 60 and older, we see a significant drop-off in time spent with co-workers. This makes sense, considering many people in the US [enter retirement in their mid-60s](https://ourworldindata.org/grapher/average-effective-retirement-men). We see that this time is partly displaced by more time with partners. How about the _number_ of people we interact with? This chart suggests that the number of people with whom we interact is highest around 40, but then things change substantially after that. And this is perhaps the most conspicuous trend in the chart: above 40, people spend an increasing amount of time alone. ### Older people spend a lot of time alone Older people spend a large amount of time alone and it is understandable why — time spent alone increases with age because this is when health typically deteriorates and people lose relatives and friends. Indeed, many people who are older than 60 live alone as this chart shows clearly: living alone is particularly common for older adults. Another interesting point here is that the share of people across all age groups who live alone has been rising over time. This is part of a more general global trend — if you want to read more about the global ""rise of living alone"", we provide a detailed account of this trend across countries in this article: ### undefined undefined https://docs.google.com/document/d/19H4I3tfW-m1skufkSCiVn4mQtU-H2Bw0eD7Wrb1ebYg/edit ### Older people spend more time alone, but this doesn’t necessarily mean they’re lonely The data shows that as we become older we tend to spend more and more time alone. What’s more, the data also shows that older people today spend more time alone than older people did in the past. We might think older people are therefore more lonely — but this is not necessarily the case. Spending time alone is not the same as feeling lonely. This is a point that is well recognized by researchers, and one which has been confirmed empirically across countries. Surveys that ask people about living arrangements, time use, and feelings of loneliness find that solitude, by itself, is not a good predictor of loneliness. You can read our overview of the evidence in our article: ### undefined undefined https://docs.google.com/document/d/1o4VRXc0grrTvoZ4IfJ6i9Cne93E_-81w9KcPnErDr7I/edit So, what about loneliness? If we focus on self-reported loneliness, there is [little evidence](https://ourworldindata.org/loneliness-epidemic#loneliness-trends-over-time-are-people-lonelier-today-than-in-the-past) of an upward trend over time in the US, and importantly, it’s [not the case](https://ourworldindata.org/social-connections-and-loneliness#do-we-become-lonelier-as-we-get-older) that loneliness keeps going up as we become older. In fact, a recent study based on surveys that track the same individuals over time found that after age 50 — which is the earliest age of participants in the analysis — loneliness tended to decrease, until about 75, after which it began to increase again.9 Taking the evidence together, the message is not that we should be sad about the prospect of aging, but rather that we should recognize the fact that social connections are complex. We often look at the amount of time spent with others as a marker of social well-being — but the _quality_ of time spent with others, and our expectations, matter even more for our feelings of connection and loneliness. ## Additional information In the chart where we plot the amount of time that people in the US report spending in the company of others, it’s important to keep in mind that we are taking a look at a cross-section of society. This means that we are actually seeing the result of two underlying trends. On one hand, we see the effect of aging on social connections (we relate to different people and reallocate time as we go through different stages of life), but we also see the effect of cohort trends (compared to people in the past, today’s older generations in the US tend to be [healthier](https://ourworldindata.org/grapher/life-expectancy?tab=chart&country=JPN~GBR~IND~ETH~OWID_WRL~KOR~ZAF~USA®ion=World) and [richer](https://ourworldindata.org/grapher/average-real-gdp-per-capita-across-countries-and-regions?tab=chart&country=Western%20Offshoots~Western%20Europe~Western%20Asia~Eastern%20Europe~OWID_WRL~Latin%20America~East%20Asia~Africa~USA), and might also have different expectations, preferences, and opportunities). Disentangling these two effects is difficult, so it is important to keep in mind that at least some of the age gradients we observe might be partly explained by cohort changes, rather than life-cycle trends. This is why it’s important to rely not only on cross-sectional data but also on surveys that track the same individuals over time. The ""time-diary method"" is generally more reliable and allows a richer analysis of routines, because it measures not only aggregate times but also sequences and clock-times. Time-diary data is less common, but it is available for some countries from the [Multinational Time Use Study](https://www.timeuse.org/mtus). We explore time-use ""tempograms"" from the MTUS in a forthcoming companion post. OECD (2020) [Time Use Database.](https://stats.oecd.org/Index.aspx?DataSetCode=TIME_USE) Because these estimates include people who are not employed, they are much lower than the estimates of working hours _per worker_[ present elsewhere](https://ourworldindata.org/time-use#time-spent-working). The estimates also differ because of differences in the sources: time-use surveys compared to labor force surveys and national accounts data. If you want to dig deeper you can explore gender differences across all other activities directly from our source, via the [OECD Data Portal](https://stats.oecd.org/Index.aspx?DataSetCode=TIME_USE). You can read more about within-country inequalities in time use along other dimensions, such as income and education, in [this Brookings Paper](https://scholar.google.com/scholar?hl=es&as_sdt=0%2C5&q=The+Middle+Class+Time+Squeeze%2C+Economic+Studies+at+Brookings%2C+Sawhill+and+Guyot+%282020%29&btnG=), where the authors focus on the ""middle-class time squeeze"" in the US. Sawhill, I. V., & Guyot, K. (2020). The Middle Class Time Squeeze. Economic Studies at Brookings. Brookings Institution. The underlying data comes from time-use diaries where respondents are asked to record the sequence of what they do over a specific day, and how much they enjoy each ""episode"" (i.e. what they do) on a scale from 1 to 7. All episodes reported are then coded and grouped into similar activities. To arrive at the mean enjoyment scores, the authors multiply the duration of each episode where the activity category concerned is the primary activity recorded, by the enjoyment level to arrive at the total enjoyment score for that episode. Then they sum these total enjoyment scores for each category of activity across the day, and finally divide these daily enjoyment total scores for each activity by the amount of time devoted to the activity. In this way, they arrive at an appropriately weighted mean enjoyment level for each activity across all those who engage in it. For more details see Gershuny, J., & Sullivan, O. (2019). What We Really Do All Day: Insights from the Centre for Time Use Research. Penguin UK. Gershuny, J., & Sullivan, O. (2019). What We Really Do All Day: Insights from the Centre for Time Use Research. Penguin UK. You can find a very clear and complete explanation of this in Ramey, V. A., & Francis, N. (2009). A century of work and leisure. American Economic Journal: Macroeconomics, 1(2), 189-224. When interpreting this chart it’s important to bear in mind that the relationships used to categorize people are not exhaustive (i.e., survey respondents could also list being with people who didn’t fit any of the listed categories, or for whom a relationship was unclear or unknown — we do not count these instances in the estimates). Additionally, time spent with multiple people can be counted more than once; so attending a party with friends and your spouse, for example, would show up for both “friends” and “partner” in our estimates. The implication is that companion categories cannot be stacked to add up the total time spent in the company of others. Hawkley, L. C., Wroblewski, K., Kaiser, T., Luhmann, M., & Schumm, L. P. (2019). Are US older adults getting lonelier? Age, period, and cohort differences. Psychology and Aging, 34(8), 1144.",Time Use 1qW5ok-Jo_asj_7A7WWfHHR_PpXhoUrD3OyssHfyWHtM,wildfires,linear-topic-page,"{""toc"": [{""slug"": ""tracking-the-extent-of-wildfires-across-the-world"", ""text"": ""Tracking the extent of wildfires across the world"", ""title"": ""Tracking the extent of wildfires across the world"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""weekly-burned-area"", ""text"": ""Weekly burned area"", ""title"": ""Weekly burned area"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""burned-area-by-year"", ""text"": ""Burned area by year"", ""title"": ""Burned area by year"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""share-of-land-area-burned-by-wildfires"", ""text"": ""Share of land area burned by wildfires"", ""title"": ""Share of land area burned by wildfires"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""land-area-burned-per-wildfire"", ""text"": ""Land area burned per wildfire"", ""title"": ""Land area burned per wildfire"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""burned-area-by-land-type"", ""text"": ""Burned area by land type"", ""title"": ""Burned area by land type"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""carbon-emissions-from-wildfires"", ""text"": ""Carbon emissions from wildfires"", ""title"": ""Carbon emissions from wildfires"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""air-pollution-from-wildfires"", ""text"": ""Air pollution from wildfires"", ""title"": ""Air pollution from wildfires"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""is-the-area-burnt-by-wildfires-increasing-or-decreasing-globally"", ""text"": ""Is the area burnt by wildfires increasing or decreasing globally?"", ""title"": ""Is the area burnt by wildfires increasing or decreasing globally?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""all-charts"", ""text"": ""Interactive charts on wildfires"", ""title"": ""Interactive charts on wildfires"", ""isSubheading"": false}, {""slug"": ""article-endnotes"", ""text"": ""Endnotes"", ""title"": ""Endnotes"", ""isSubheading"": false}, {""slug"": ""article-citation"", ""text"": ""Citation"", ""title"": ""Citation"", ""isSubheading"": false}, {""slug"": ""article-licence"", ""text"": ""Licence"", ""title"": ""Licence"", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""A wildfire is an uncontrolled burn of vegetation, which includes the burning of "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/forests-and-deforestation"", ""children"": [{""text"": ""forests"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", shrublands and grasslands, savannas, and croplands."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Wildfires can be caused by "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.science.org/content/article/human-sparked-wildfires-are-more-destructive-those-caused-nature"", ""children"": [{""text"": ""human activity"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" — such as arson, unattended fires, or the loss of control of planned burns — and natural causes, such as lightning."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""spread"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of wildfires, once ignited, is determined by a range of factors, such as the amount and types of dry vegetation in the surrounding area, wind direction and speed, moisture levels, and heat. The amount of area burned by wildfires — and the impacts on ecosystems — is driven by a combination of weather patterns, human activity, the management of vegetation and landscapes, and responses to suppress their spread."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Humans are affected by wildfires both directly and indirectly. In most years, "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/natural-disasters?time=2000..latest&facet=none&Disaster+Type=Wildfires&Impact=Deaths&Timespan=Annual&Per+capita=false&country=~OWID_WRL"", ""children"": [{""text"": ""several hundred people"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" die directly from the fire. Far more — typically "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/natural-disasters?time=2000..latest&facet=none&Disaster+Type=Wildfires&Impact=Total+affected&Timespan=Annual&Per+capita=false&country=~OWID_WRL"", ""children"": [{""text"": ""tens or hundreds of thousands"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" are evacuated, or in some cases, permanently displaced from their homes. Wildfires also emit particulates and local air pollutants "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.who.int/news-room/feature-stories/detail/air-pollution--the-invisible-health-threat"", ""children"": [{""text"": ""that are damaging"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" to human health. These fires also emit carbon dioxide — a drive of climate change — and can disrupt or damage ecosystems."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""On this page, we look at data on wildfires' extent and how they are changing over time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""This data is updated frequently, including some charts on a weekly basis."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""url"": ""#all-charts"", ""children"": [{""text"": ""See all interactive charts on wildfires ↓"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}], ""parseErrors"": []}, {""text"": [{""text"": ""Tracking the extent of wildfires across the world"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To track the progression of wildfires over the course of the current year and understand the historical trends in these fires, we’ve published a range of charts that we will update frequently."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most of this data is sourced from the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://gwis.jrc.ec.europa.eu/apps/gwis.statistics/seasonaltrend"", ""children"": [{""text"": ""Global Wildfire Information System"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (GWIS). This is a joint initiative of the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://earthobservations.org/"", ""children"": [{""text"": ""Group on Earth Observation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (GEO) and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.copernicus.eu/en/copernicus-services/emergency"", ""children"": [{""text"": ""Copernicus"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", the Earth observation component of the European Union’s space program."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""GWIS detects wildfires through the use of satellite imagery"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" and provides excellent, up-to-date reports on wildfire extent, emissions, and pollution at a very high resolution. Here, we visualize its national, regional, and global summaries. Detailed mappings of active wildfires are also available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://gwis.jrc.ec.europa.eu/apps/gwis_current_situation/index.html"", ""children"": [{""text"": ""on its website"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Weekly burned area"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A useful way of tracking the evolution of wildfires across the year is to look at their week-by-week progression. This allows us to compare the extent of wildfires at a given time "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""this "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""year to the same period in a previous year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It lets us see whether wildfires have started earlier or later than in previous years and whether they’re tracking above or below what we might expect from historical records."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart below, you can see the cumulative area of land burned by wildfires by week. The horizontal axis starts on week 1 (January 1st) and continues to 52 (the last week of December). Each line represents one year and shows the cumulative area burnt by any given week in that year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can explore this data for any country in the interactive chart. What you’ll find is that wildfires start at different times depending on the region. Peak wildfire season in Europe, for example, tends to be from June to August. In Southern Australia, it tends to be later in the year, coinciding with the South Hemisphere’s spring and summer months. You can also explore this data expressed "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/weekly-cumulative-share-of-the-area-burnt-by-wildfires-each-year"", ""children"": [{""text"": ""as a share of the total land area"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/cumulative-area-burnt-by-wildfires-by-week"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Burned area by year"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How much area is burned by wildfires each year?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart below, you can see the annual totals since 2012."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The data for the current year is also included to allow you to put this year’s current burn figures into context. It will be updated frequently, with the date of the latest published figures shown on the chart. Obviously, this means the final year of data is incomplete until the end of the fire season in each respective country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/annual-area-burnt-by-wildfires"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Share of land area burned by wildfires"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It’s useful to put the total extent of wildfires in the context of total land area: what "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""share"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of land is burned each year?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart below, you can explore this data by country and region."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Africa tends to be the region with the largest share of area burned — typically ranging from 6% to 8% each year. Figures tend to be lower in other regions but can vary a lot from year to year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It’s important to note that the same land area can burn multiple times over successive years. So, a rate of 5% burn per year doesn’t mean that 50% of a country is burned over a decade. Some areas will burn multiple times over that period, while others will never be exposed."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-the-total-land-area-burnt-by-wildfires-each-year"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Land area burned per wildfire"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The spread of a wildfire, once ignited, will be influenced by many factors. Natural phenomena such as the type of vegetation, weather conditions, and the intensity of heat will influence how well the fire will be contained. Certain types of vegetation, for example, burn more easily than others. The effectiveness of the local fire containment management strategies will also play a role. This means some wildfires end up being significantly larger than others, and the impact can vary across different regions and countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart below, you can explore how the average land area burnt per wildfire varies across different countries and regions. As before, the current year’s data is included but will remain incomplete until the end of the fire season. You can further explore "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/annual-area-burnt-per-wildfire-vs-number-of-fires"", ""children"": [{""text"": ""in this scatterplot"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" how the same number of wildfires can lead to significantly different amounts of land being burnt per wildfire."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/annual-area-burnt-per-wildfire"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Burned area by land type"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When “wildfires” are mentioned, people often picture forest fires. But lots of other ecosystem types can burn. Much larger areas of grasslands and savannas are burned each year than forests."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart below, you can see the area burned in any given year by the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""type"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" of land cover."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This can vary a lot depending on a country’s landscape. Savannas, for example, are much more dominant in Africa and South America America. In Oceania, it’s shrubs and grasslands. In Europe and North America, large areas of croplands are often burned."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/annual-burned-area-by-landcover"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Carbon emissions from wildfires"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Wildfires can lead to large emissions of carbon dioxide (CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": ""), as the carbon stored in the vegetation — trees, grasslands, or crops — is released into the atmosphere when burned."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How much carbon is emitted from wildfires depends on a range of factors: the amount of material burned, as well as the type. A hectare of carbon-rich forest might emit more CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" than a hectare of cropland."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Calculating the amount of carbon released from wildfires is difficult. Researchers can use several methods to estimate it. They can, for example, use a combination of satellite measurements and atmospheric models. Or measure the amount of vegetation before and after a fire to estimate how much has been lost. Finally, they might use more direct measurements of the heat released from a fire to estimate the amount of vegetation that burned; this allows them to estimate how much carbon was released. Carbon Brief provides "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.carbonbrief.org/qa-how-scientists-tackle-the-challenges-of-estimating-wildfire-co2-emissions/"", ""children"": [{""text"": ""a detailed explanation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of how scientists estimate these figures."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" emissions from wildfires — as estimated by the Global Wildfire Information System — are shown in the chart below."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Wildfires globally add around 5 to 8 billion tonnes of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" each year. For context, globally, we emit around "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/co2?facet=none&country=~OWID_WRL&Gas+or+Warming=CO%E2%82%82&Accounting=Territorial&Fuel+or+Land+Use+Change=All+fossil+emissions&Count=Per+country"", ""children"": [{""text"": ""37 billion tonnes of CO"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""children"": [{""url"": ""https://ourworldindata.org/explorers/co2?facet=none&country=~OWID_WRL&Gas+or+Warming=CO%E2%82%82&Accounting=Territorial&Fuel+or+Land+Use+Change=All+fossil+emissions&Count=Per+country"", ""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-subscript""}, {""text"": "" from fossil fuels and cement yearly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While it’s tempting to compare these numbers directly, that is not always appropriate, because some emissions from wildfires can be offset in later years by the regrowth of vegetation. This is not the case for fossil fuels: most of the carbon they emit persists in the atmosphere for centuries or more. The contribution of wildfire emissions to climate change is, therefore, a more complex balance between how burning and revegetation are changing over time. You can also track the evolution of CO"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" emissions from wildfires across the year "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/cumulative-co-emissions-released-by-wildfires-by-week"", ""children"": [{""text"": ""week by week"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/annual-carbon-dioxide-emissions"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Air pollution from wildfires"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The burning of biomass "", ""spanType"": ""span-simple-text""}, {""url"": ""https://acp.copernicus.org/preprints/acp-2021-381/acp-2021-381-manuscript-version2.pdf"", ""children"": [{""text"": ""emits air pollutants"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" such as black carbon, volatile organic compounds, and nitrogen oxides (NOx). These compounds can be damaging to human health, resulting in respiratory problems, and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.who.int/health-topics/air-pollution"", ""children"": [{""text"": ""have been linked to"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" premature death from non-communicable conditions such as heart disease and strokes."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One major pollutant of concern is called PM"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2.5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "". This is fine particulate matter with a diameter of less than 2.5 micrometers. These very small particles are particularly damaging because they can penetrate deeply into the lungs."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart below, we show estimates of the amount of PM"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""2.5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-subscript""}, {""text"": "" that is emitted from wildfires across the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/annual-pm25-emissions-from-wildfires"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Is the area burnt by wildfires increasing or decreasing globally?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There is increasing concern about the impacts of global warming on wildfire frequency and severity. Factors such as increased heat, humidity, the drying effect on vegetation, and wind patterns can all affect the risk of large wildfires."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, some observers have noted that globally, the amount of area burned by wildfires each year has gone "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""down"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" over the last few decades."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you look at statistics from the Global Wildfire Information System shown in the chart "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/annual-area-burnt-by-wildfires-gwis"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", since the early 2000s, there has been a noticeable decline in the annual extent of land affected by wildfires."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To understand what’s going on, it’s useful to look at how areas burnt have changed across different landscapes. In the chart below, we see the amount of area burned by land cover. You can see that most of this decline has come from shrublands, grasslands, and croplands (with small declines in savannas). Forest fires have been relatively stable."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Much of this decline has occurred in Africa and, to a lesser extent, in Oceania. The data suggests small declines in Europe, too."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In a "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.science.org/doi/full/10.1126/science.aal4108"", ""children"": [{""text"": ""paper published"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Science"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", researchers note this same trend: “Unexpectedly, global burned area declined by ∼25% over the past 18 years, despite the influence of climate.”"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" They, too, point out that this is largely driven by a decline in burn rates in grasslands and savannas as a result of the expansion and intensification of agriculture."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This highlights the strong role that human activity and land use management play in wildfire extent, alongside weather- and climate-related factors. Both factors must be considered when trying to minimize the damage of increasing fire risk in a changing climate."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/area-burned-wildfires-by-type"", ""type"": ""chart"", ""parseErrors"": []}, {""top"": [], ""type"": ""all-charts"", ""heading"": ""Interactive charts on wildfires"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""639bd13f8eb1c0f5d451cdf8040c221bcd206142"": {""id"": ""639bd13f8eb1c0f5d451cdf8040c221bcd206142"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""GWIS uses various satellite sensors for their wildfire data analysis. The "", ""spanType"": ""span-simple-text""}, {""url"": ""https://gwis.jrc.ec.europa.eu/apps/country.profile/"", ""children"": [{""text"": ""historical estimates"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" are primarily based on the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.sciencedirect.com/science/article/pii/S0034425719305097"", ""children"": [{""text"": ""NASA MCD64 MODIS product"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", but the authors note that it can underestimate burnt areas and fire occurrences. For higher spatial resolution and enhanced accuracy, they also rely on Sentinel-2, but unfortunately, these data are only available for "", ""spanType"": ""span-simple-text""}, {""url"": ""https://effis.jrc.ec.europa.eu/apps/effis.statistics/seasonaltrend/ALB/2022/CO2"", ""children"": [{""text"": ""the European region"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Therefore, we’ve chosen to rely largely on the near-real-time"", ""spanType"": ""span-simple-text""}, {""url"": ""https://gwis.jrc.ec.europa.eu/apps/gwis.statistics/"", ""children"": [{""text"": "" data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" provided by GWIS, which is derived from "", ""spanType"": ""span-simple-text""}, {""url"": ""https://modis.gsfc.nasa.gov/about/"", ""children"": [{""text"": ""MODIS"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" & "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.earthdata.nasa.gov/learn/find-data/near-real-time/firms/viirs-i-band-375-m-active-fire-data"", ""children"": [{""text"": ""VIIRS Active Fire "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""sensors, except when discussing historical trends. The authors note that these provide a reliable estimate while acknowledging that they might still be underestimating the genuine impact of wildfires, primarily due to constraints imposed by the spatial resolution of the MODIS and VIIRS sensors."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""9346b53e9900ab13e3190f0b90881cc3b2ffeca7"": {""id"": ""9346b53e9900ab13e3190f0b90881cc3b2ffeca7"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Zheng, B., Ciais, P., Chevallier, F., Chuvieco, E., Chen, Y., & Yang, H. (2021). Increasing forest fire emissions despite the decline in global burned area. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Science Advances"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", 7(39), eabh2646."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a1536110fc497e8da256dbc5f5a7beaf12b43e6d"": {""id"": ""a1536110fc497e8da256dbc5f5a7beaf12b43e6d"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Andela, N., Morton, D. C., Giglio, L., Chen, Y., van der Werf, G. R., Kasibhatla, P. S., ... & Randerson, J. T. (2017). A human-driven decline in global burned area. Science, 356(6345), 1356-1362."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""a4bd731d2cf688067287e7988dbe3797b4b36e79"": {""id"": ""a4bd731d2cf688067287e7988dbe3797b4b36e79"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Alexeeff, S. E., Liao, N. S., Liu, X., Van Den Eeden, S. K., & Sidney, S. (2021). Long‐term PM2. 5 exposure and risks of ischemic heart disease and stroke events: review and meta‐analysis. Journal of the American Heart Association, 10(1), e016890."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Murray, C. J., Aravkin, A. Y., Zheng, P., Abbafati, C., Abbas, K. M., Abbasi-Kangevari, M., ... & Borzouei, S. (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The lancet, 396(10258), 1223-1249."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f57f956b948fb68b31c79dd9900ab1d3b32739af"": {""id"": ""f57f956b948fb68b31c79dd9900ab1d3b32739af"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Note that for these historical estimates, we rely on the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://gwis.jrc.ec.europa.eu/apps/country.profile/"", ""children"": [{""text"": ""data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that is based on the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.sciencedirect.com/science/article/pii/S0034425719305097"", ""children"": [{""text"": ""NASA MCD64 MODIS sensor"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", which according to the data provider might be underestimating burnt areas and fire occurrences. However, since the underestimation would be consistent across years due to using the same methods, the observed decline in burned areas remains valid."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""linear-topic-page"", ""title"": ""Wildfires"", ""authors"": [""Veronika Samborska"", ""Hannah Ritchie""], ""excerpt"": ""Explore global and country-level data on the extent of wildfires and how they’ve changed over time."", ""dateline"": ""First published on 2nd April, 2024. Statistics updated weekly."", ""subtitle"": ""Explore global and country-level data on the extent of wildfires and how they’ve changed over time."", ""atom-title"": ""Tracking the extent of wildfires across the world"", ""sidebar-toc"": true, ""atom-excerpt"": ""Explore global and country-level data on the extent of wildfires and how they’ve changed over time."", ""featured-image"": ""wildfires-featured-image.png""}",1,2024-03-07 14:43:42,2024-04-02 08:16:37,2024-03-15 12:10:47,listed,ALBJ4Lue7jtiwKSCeukz-XCs8A7u7gh0b7vmGdcCCvuvtO4IHnK0AALql5ZArbcdGGM3_sOUJA4Vv27aPSFx_g,,"A wildfire is an uncontrolled burn of vegetation, which includes the burning of [forests](http://ourworldindata.org/forests-and-deforestation), shrublands and grasslands, savannas, and croplands. Wildfires can be caused by [human activity](https://www.science.org/content/article/human-sparked-wildfires-are-more-destructive-those-caused-nature) — such as arson, unattended fires, or the loss of control of planned burns — and natural causes, such as lightning. The _spread_ of wildfires, once ignited, is determined by a range of factors, such as the amount and types of dry vegetation in the surrounding area, wind direction and speed, moisture levels, and heat. The amount of area burned by wildfires — and the impacts on ecosystems — is driven by a combination of weather patterns, human activity, the management of vegetation and landscapes, and responses to suppress their spread. Humans are affected by wildfires both directly and indirectly. In most years, [several hundred people](https://ourworldindata.org/explorers/natural-disasters?time=2000..latest&facet=none&Disaster+Type=Wildfires&Impact=Deaths&Timespan=Annual&Per+capita=false&country=~OWID_WRL) die directly from the fire. Far more – typically [tens or hundreds of thousands](https://ourworldindata.org/explorers/natural-disasters?time=2000..latest&facet=none&Disaster+Type=Wildfires&Impact=Total+affected&Timespan=Annual&Per+capita=false&country=~OWID_WRL) are evacuated, or in some cases, permanently displaced from their homes. Wildfires also emit particulates and local air pollutants [that are damaging](https://www.who.int/news-room/feature-stories/detail/air-pollution--the-invisible-health-threat) to human health. These fires also emit carbon dioxide – a drive of climate change – and can disrupt or damage ecosystems. On this page, we look at data on wildfires' extent and how they are changing over time. **This data is updated frequently, including some charts on a weekly basis.** **[See all interactive charts on wildfires ↓](#all-charts)** # Tracking the extent of wildfires across the world To track the progression of wildfires over the course of the current year and understand the historical trends in these fires, we’ve published a range of charts that we will update frequently. Most of this data is sourced from the [Global Wildfire Information System](https://gwis.jrc.ec.europa.eu/apps/gwis.statistics/seasonaltrend) (GWIS). This is a joint initiative of the [Group on Earth Observation](https://earthobservations.org/) (GEO) and [Copernicus](https://www.copernicus.eu/en/copernicus-services/emergency), the Earth observation component of the European Union’s space program. GWIS detects wildfires through the use of satellite imagery1 and provides excellent, up-to-date reports on wildfire extent, emissions, and pollution at a very high resolution. Here, we visualize its national, regional, and global summaries. Detailed mappings of active wildfires are also available [on its website](https://gwis.jrc.ec.europa.eu/apps/gwis_current_situation/index.html). ## Weekly burned area A useful way of tracking the evolution of wildfires across the year is to look at their week-by-week progression. This allows us to compare the extent of wildfires at a given time _this _year to the same period in a previous year. It lets us see whether wildfires have started earlier or later than in previous years and whether they’re tracking above or below what we might expect from historical records. In the chart below, you can see the cumulative area of land burned by wildfires by week. The horizontal axis starts on week 1 (January 1st) and continues to 52 (the last week of December). Each line represents one year and shows the cumulative area burnt by any given week in that year. You can explore this data for any country in the interactive chart. What you’ll find is that wildfires start at different times depending on the region. Peak wildfire season in Europe, for example, tends to be from June to August. In Southern Australia, it tends to be later in the year, coinciding with the South Hemisphere’s spring and summer months. You can also explore this data expressed [as a share of the total land area](https://ourworldindata.org/grapher/weekly-cumulative-share-of-the-area-burnt-by-wildfires-each-year). ## Burned area by year How much area is burned by wildfires each year? In the chart below, you can see the annual totals since 2012. The data for the current year is also included to allow you to put this year’s current burn figures into context. It will be updated frequently, with the date of the latest published figures shown on the chart. Obviously, this means the final year of data is incomplete until the end of the fire season in each respective country. ## Share of land area burned by wildfires It’s useful to put the total extent of wildfires in the context of total land area: what _share_ of land is burned each year? In the chart below, you can explore this data by country and region. Africa tends to be the region with the largest share of area burned — typically ranging from 6% to 8% each year. Figures tend to be lower in other regions but can vary a lot from year to year. It’s important to note that the same land area can burn multiple times over successive years. So, a rate of 5% burn per year doesn’t mean that 50% of a country is burned over a decade. Some areas will burn multiple times over that period, while others will never be exposed. ## Land area burned per wildfire The spread of a wildfire, once ignited, will be influenced by many factors. Natural phenomena such as the type of vegetation, weather conditions, and the intensity of heat will influence how well the fire will be contained. Certain types of vegetation, for example, burn more easily than others. The effectiveness of the local fire containment management strategies will also play a role. This means some wildfires end up being significantly larger than others, and the impact can vary across different regions and countries. In the chart below, you can explore how the average land area burnt per wildfire varies across different countries and regions. As before, the current year’s data is included but will remain incomplete until the end of the fire season. You can further explore [in this scatterplot](https://ourworldindata.org/grapher/annual-area-burnt-per-wildfire-vs-number-of-fires) how the same number of wildfires can lead to significantly different amounts of land being burnt per wildfire. ## Burned area by land type When “wildfires” are mentioned, people often picture forest fires. But lots of other ecosystem types can burn. Much larger areas of grasslands and savannas are burned each year than forests. In the chart below, you can see the area burned in any given year by the _type_ of land cover. This can vary a lot depending on a country’s landscape. Savannas, for example, are much more dominant in Africa and South America America. In Oceania, it’s shrubs and grasslands. In Europe and North America, large areas of croplands are often burned. # Carbon emissions from wildfires Wildfires can lead to large emissions of carbon dioxide (CO2), as the carbon stored in the vegetation — trees, grasslands, or crops — is released into the atmosphere when burned. How much carbon is emitted from wildfires depends on a range of factors: the amount of material burned, as well as the type. A hectare of carbon-rich forest might emit more CO2 than a hectare of cropland.2 Calculating the amount of carbon released from wildfires is difficult. Researchers can use several methods to estimate it. They can, for example, use a combination of satellite measurements and atmospheric models. Or measure the amount of vegetation before and after a fire to estimate how much has been lost. Finally, they might use more direct measurements of the heat released from a fire to estimate the amount of vegetation that burned; this allows them to estimate how much carbon was released. Carbon Brief provides [a detailed explanation](https://www.carbonbrief.org/qa-how-scientists-tackle-the-challenges-of-estimating-wildfire-co2-emissions/) of how scientists estimate these figures. CO2 emissions from wildfires — as estimated by the Global Wildfire Information System — are shown in the chart below. Wildfires globally add around 5 to 8 billion tonnes of CO2 each year. For context, globally, we emit around [37 billion tonnes of CO](https://ourworldindata.org/explorers/co2?facet=none&country=~OWID_WRL&Gas+or+Warming=CO%E2%82%82&Accounting=Territorial&Fuel+or+Land+Use+Change=All+fossil+emissions&Count=Per+country)[2](https://ourworldindata.org/explorers/co2?facet=none&country=~OWID_WRL&Gas+or+Warming=CO%E2%82%82&Accounting=Territorial&Fuel+or+Land+Use+Change=All+fossil+emissions&Count=Per+country) from fossil fuels and cement yearly. While it’s tempting to compare these numbers directly, that is not always appropriate, because some emissions from wildfires can be offset in later years by the regrowth of vegetation. This is not the case for fossil fuels: most of the carbon they emit persists in the atmosphere for centuries or more. The contribution of wildfire emissions to climate change is, therefore, a more complex balance between how burning and revegetation are changing over time. You can also track the evolution of CO2 emissions from wildfires across the year [week by week](https://ourworldindata.org/grapher/cumulative-co-emissions-released-by-wildfires-by-week). # Air pollution from wildfires The burning of biomass [emits air pollutants](https://acp.copernicus.org/preprints/acp-2021-381/acp-2021-381-manuscript-version2.pdf) such as black carbon, volatile organic compounds, and nitrogen oxides (NOx). These compounds can be damaging to human health, resulting in respiratory problems, and [have been linked to](https://www.who.int/health-topics/air-pollution) premature death from non-communicable conditions such as heart disease and strokes.3 One major pollutant of concern is called PM2.5. This is fine particulate matter with a diameter of less than 2.5 micrometers. These very small particles are particularly damaging because they can penetrate deeply into the lungs. In the chart below, we show estimates of the amount of PM2.5 that is emitted from wildfires across the world. # Is the area burnt by wildfires increasing or decreasing globally? There is increasing concern about the impacts of global warming on wildfire frequency and severity. Factors such as increased heat, humidity, the drying effect on vegetation, and wind patterns can all affect the risk of large wildfires. However, some observers have noted that globally, the amount of area burned by wildfires each year has gone _down_ over the last few decades. If you look at statistics from the Global Wildfire Information System shown in the chart [here](https://ourworldindata.org/grapher/annual-area-burnt-by-wildfires-gwis), since the early 2000s, there has been a noticeable decline in the annual extent of land affected by wildfires.4 To understand what’s going on, it’s useful to look at how areas burnt have changed across different landscapes. In the chart below, we see the amount of area burned by land cover. You can see that most of this decline has come from shrublands, grasslands, and croplands (with small declines in savannas). Forest fires have been relatively stable. Much of this decline has occurred in Africa and, to a lesser extent, in Oceania. The data suggests small declines in Europe, too. In a [paper published](https://www.science.org/doi/full/10.1126/science.aal4108) in _Science_, researchers note this same trend: “Unexpectedly, global burned area declined by ∼25% over the past 18 years, despite the influence of climate.”5 They, too, point out that this is largely driven by a decline in burn rates in grasslands and savannas as a result of the expansion and intensification of agriculture. This highlights the strong role that human activity and land use management play in wildfire extent, alongside weather- and climate-related factors. Both factors must be considered when trying to minimize the damage of increasing fire risk in a changing climate. GWIS uses various satellite sensors for their wildfire data analysis. The [historical estimates](https://gwis.jrc.ec.europa.eu/apps/country.profile/) are primarily based on the [NASA MCD64 MODIS product](https://www.sciencedirect.com/science/article/pii/S0034425719305097), but the authors note that it can underestimate burnt areas and fire occurrences. For higher spatial resolution and enhanced accuracy, they also rely on Sentinel-2, but unfortunately, these data are only available for [the European region](https://effis.jrc.ec.europa.eu/apps/effis.statistics/seasonaltrend/ALB/2022/CO2). Therefore, we’ve chosen to rely largely on the near-real-time[ data](https://gwis.jrc.ec.europa.eu/apps/gwis.statistics/) provided by GWIS, which is derived from [MODIS](https://modis.gsfc.nasa.gov/about/) & [VIIRS Active Fire ](https://www.earthdata.nasa.gov/learn/find-data/near-real-time/firms/viirs-i-band-375-m-active-fire-data)sensors, except when discussing historical trends. The authors note that these provide a reliable estimate while acknowledging that they might still be underestimating the genuine impact of wildfires, primarily due to constraints imposed by the spatial resolution of the MODIS and VIIRS sensors. Zheng, B., Ciais, P., Chevallier, F., Chuvieco, E., Chen, Y., & Yang, H. (2021). Increasing forest fire emissions despite the decline in global burned area. _Science Advances_, 7(39), eabh2646. Alexeeff, S. E., Liao, N. S., Liu, X., Van Den Eeden, S. K., & Sidney, S. (2021). Long‐term PM2. 5 exposure and risks of ischemic heart disease and stroke events: review and meta‐analysis. Journal of the American Heart Association, 10(1), e016890. Murray, C. J., Aravkin, A. Y., Zheng, P., Abbafati, C., Abbas, K. M., Abbasi-Kangevari, M., ... & Borzouei, S. (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The lancet, 396(10258), 1223-1249. Note that for these historical estimates, we rely on the [data](https://gwis.jrc.ec.europa.eu/apps/country.profile/) that is based on the [NASA MCD64 MODIS sensor](https://www.sciencedirect.com/science/article/pii/S0034425719305097), which according to the data provider might be underestimating burnt areas and fire occurrences. However, since the underestimation would be consistent across years due to using the same methods, the observed decline in burned areas remains valid. Andela, N., Morton, D. C., Giglio, L., Chen, Y., van der Werf, G. R., Kasibhatla, P. S., ... & Randerson, J. T. (2017). A human-driven decline in global burned area. Science, 356(6345), 1356-1362.",Wildfires 1qT1GqoufLtV-JR98fNgWSHgAwBArwg0DjyjnSjU0HO8,soil-lifespans,article,"{""toc"": [], ""body"": [{""text"": [{""type"": ""text"", ""value"": [{""text"": ""The stark claim that the world has only 100; 60 or even 30 years of harvests left often hits the headlines. Although they continue to be repeated, there is no scientific basis to them."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While the claims are overblown, soil erosion is an important problem. Erosion rates from across the world span five orders of magnitude. Some are eroding quickly: 16% of soils are estimated to have a lifespan of less than 100 years. Others are eroding slowly: half have a lifespan greater than 1000 years; and one-third have over 5000 years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To protect our soils we must adopt better agricultural practices – such as cover cropping, minimal or no tillage, and contour cultivation. This way we can extend the lifespan of the soils that we all depend on."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Summary"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If one reads the newspaper headlines on the state of the world’s soils it is easy to be convinced that we are only decades away from global famine:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""“"", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.fao.org/soils-2015/events/detail/en/c/338738/"", ""children"": [{""text"": ""The world’s top soil could be gone within 60 years"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” says a senior UN official;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""“"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.independent.co.uk/news/uk/home-news/britain-facing-agricultural-crisis-scientists-warn-there-are-only-100-harvests-left-our-farm-soil-9806353.html"", ""children"": [{""text"": ""Britain has only 100 harvests left"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” writes the Independent newspaper;"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""“"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.theguardian.com/environment/2017/oct/24/uk-30-40-years-away-eradication-soil-fertility-warns-michael-gove"", ""children"": [{""text"": ""UK is 30 to 40 years away from the ‘eradication of soil fertility’ warns Gove"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""” [the former Environment Secretary]."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The good news is that these claims are overblown. The bad news is that this doesn’t stop them being repeated over and over. The “60 harvests left” statistic seems to be one that just won’t die."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And while the headlines are exaggerations it shouldn’t take away from the fact that many of our soils are degrading and we need to take action to restore them."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Where do these claims come from?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The honest answer is that we don’t know. Botanist and science communicator, James Wong, tried to trace these claims back to their roots for an "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.newscientist.com/article/mg24232291-100-the-idea-that-there-are-only-100-harvests-left-is-just-a-fantasy/"", ""children"": [{""text"": ""article in the New Scientist"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" We know that a senior official at a UN FAO farming conference was quoted with the “60 harvests” figure and that Michael Gove mentioned a 30 to 40 year deadline. But we don’t know what they based their assessments on."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The “100 harvests” figure seems to link back to a study in the UK conducted by  researchers at the University of Sheffield."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" I say “seems to” because there appears to be no mention of the 100-year figure in the paper. James Wong failed to find where this number came from; I also spent a lot of time digging and did no better."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In any case, this study looked at the difference in soils properties of city allotments in Leicester, a city in the UK, and soils from some surrounding farms. It concluded that the soils in city allotments had more organic matter, higher nitrogen levels and a better soil density. Not exactly informative for the larger and more urgent question on the state of the world’s soils."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""There is no single lifespan of the world’s soils"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What do we know about the state of the world’s soils?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A recent study by Daniel Evans and colleagues gave us a first assessment of the range of soil lifespans across the world."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This drew upon a database of 4285 measured soil erosion rates, from 240 studies, covering 255 unique locations across 38 countries. As shown in the map, these 255 locations span all continents of the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How would we estimate the ‘lifespan’ of a soil? There is no single metric to do so: soils are complex and have a range of properties from nutrient balance, to density, and structure. The best proxy – and the metric that Daniel Evans and his colleagues used – was net erosion rates of the crucial topsoil layer, the topmost layer that is around 30 centimeters thick "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""[in reality, this thickness varies from soil to soil, but 0.3m is the most commonly adopted figure for this upper productive layer]"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". Crops need this layer to grow: it’s where the carbon, water and nutrients get stored."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Depending on how the soil is managed, this topsoil can thin or thicken. If we know what rate it’s thinning, we can estimate how long it would take for this layer to disappear. For example, if a topsoil was thinning by 0.5 centimeters every year, it would take 60 years to lose 30 centimeters."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" If you want a more detailed understanding of soil lifespans and how they’re calculated, the lead author explains this "", ""spanType"": ""span-simple-text""}, {""children"": [{""url"": ""https://www.youtube.com/watch?v=XKeGv6z5a10"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-bold""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It’s not the only metric that determines soil productivity, but it’s a meaningful metric that tells us something valuable about the state of the world’s soils."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""caption"": [{""children"": [{""text"": ""Number and spatial distribution of plot years for the 255 unique locations in the study."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""soil-lifespans-map.jpg"", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""The lifespans of the world’s soils span five orders of magnitude"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What did this study tell us about the lifespan of our soils?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Soils from the 4285 data points in the study were grouped into three categories."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""‘"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Bare’ soils"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" are plots of land which are deliberately kept free from any crops to determine erosion rates of soils without vegetation. These are used to assess a ‘worst-case scenario’."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Conventionally managed soils "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""are those which are actively farmed, without implementing notable conservation practices. These are used to represent a ‘business-as-usual scenario’."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Conservation management soils "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""were those that had been subject to soil conservation techniques such as land use change (to forests and grasslands) or improved agricultural practices such as intercropping, no-tillage, or contour farming. We will look at the impact of these techniques later."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart here we see how the distribution of estimated soil lifespans in these three categories varied across the global dataset. On the x-axis we have the lifespan in years and on the y-axis we have the cumulative percentage of soils that were found to have that lifespan. Notice that the scale on the lifespan axis is logarithmic and stretches from 10 years to 10 million years. This further demonstrates how citing a single lifespan for the world’s soils is inaccurate and nonsensical."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Let’s focus on the ‘conventionally managed’ soils, shown in blue. These data are relevant for understanding many of the world’s farming practices. We will look at conservation techniques later."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Many of these soils are thinning; some very quickly. 16% have a lifespan of less than 100 years if they continue to erode at their current rates. This is not a local problem: there are examples of soils with lifespans shorter than a century on all continents, including the United States, Australia, Spain, Italy, Brazil and China. The longevity of these soils is concerning and we should be acting quickly to preserve them."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But the “60 harvests” claim is quite clearly false. More than 90% of conventionally managed soils had a ‘lifespan’ greater than 60 years. The median was 491 years for thinning soils. Half had a lifespan greater than 1,000 years, and 18% exceeded 10,000 years. There were also some soils that were not eroding at all. Where soil formation rates exceeded erosion rates, soils thickened.In fact, some were thickening – soil was forming quicker than it was eroding. In the bottom-right of the chart we see the rates of soil gain. 7% of conventionally managed soils were thickening."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If we were to keep our land completely bare – by removing any vegetation and preventing any natural regrowth through pesticides – our soils could erode more quickly. One-third (34%) of bare soils had lifespans less than 100 years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There is no single figure for how many harvests the world has left because there is so much variation in the types, quality, and management of our soils. It’s just implausible that they would all be degrading at exactly the same rate. As these results show: some soils are eroding quickly while others are thickening."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Soil-Lifespans.png"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/soil-lifespans"", ""type"": ""prominent-link"", ""title"": ""Explore an interactive version of this chart on soil lifespans"", ""description"": """", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""What can we do to slow erosion and restore our soils?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It’s concerning that so many of our soils are thinning. Some, very quickly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But, there are things we can do to extend the lifespan of our soils. Take a look at the ‘conservation’ curve in the previous chart. It’s shifted far to the right – even more so because the lifespan scale is logarithmic – meaning these soils are eroding much more slowly than conventionally managed soils, if at all. In fact, one-fifth were actually thickening (meaning soil was forming faster than it was eroding)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A comparison of these two groups is shown in the table. The share that had a lifespan less than 100 years was less than half that of conventionally-managed soils – 7% versus 16%. Half of the soils managed with conservation management had a lifespan greater than 5000 years; and 40% exceeded 10,000 years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This sounds promising, but what does ‘conservation’ actually mean? What practices should we put in place? There are four interventions we should consider according to Evans et al."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""We could switch from agricultural land use to forest or grassland"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "". This is the most effective way to extend soil lifespans, but it reduces the land available for farming. As the global population increases, and demands for food rise, we need to find ways of protecting our cultivable soils. If we can increase "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/crop-yields"", ""children"": [{""text"": ""crop yields"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" we have the opportunity to reduce the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/arable-land-pin"", ""children"": [{""text"": ""amount of arable land we need"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" to meet this demand."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""We can plant cover crops during the non-harvesting season"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "". If we need to use our land for farming, cover cropping can be effective in improving soil quality. None of the plots that used cover cropping in the study had a lifespan of less than 100 years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is a practice that involves growing a crop for the purpose of maintaining a vegetative cover and preventing soils from becoming bare and susceptible to erosion between growing seasons."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We know from our comparisons above that ‘bare’ soils erode much more quickly. To prevent this, you can plant a cover crop during the off-season, which maintains soil structure, fertility and enhances organic matter."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Cover crops consist of leguminous plants such as peas, beans and lentils. Cover cropping has been shown to be effective in reducing soil erosion."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""children"": [{""text"": "","", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}, {""text"": "" "", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""children"": [{""text"": "","", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""We can use minimal or zero tillage practices"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "". Conventional ‘tillage’ is a common practice where farmers will mechanically agitate and overturn the soil using ploughs."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is done to mechanically dislodge and destroy weeds; aerate the top layer of soil, and mix the nutrients evenly throughout the soil profile."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But this also creates negative impacts: it can damage the soil structure, leading to soils becoming more susceptible to erosion. Increasing soil erosion can lead to a loss of nutrients and organic matter; it may increase chemical run-off from the land; and may reduce water infiltration."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Minimal or zero-tillage practices try to manage soils without this mechanical overturning of the soil. This reduces soil erosion but has its own trade-offs: because weeds are not disturbed by ploughing, it often requires more herbicides to kill them. Some of these trade-offs can be reduced by combining it with cover cropping. Cover crops such as legumes add nitrogen to the soil, and can protect the soil from weeds and pests."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""We can implement contour cultivation or terracing on hillslopes"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "". The gradient of the land makes a big difference to the susceptibility of the soil to erosion. Soils on a steep slope erode much more quickly. 37% of soils which were cultivated up-and-down the slope had a lifespan less than 100 years. Contour cultivation – where you grow crops perpendicular to or across the slope – reduced this to 7%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Contour cultivation is not always practical on very steep gradients. In this case, terracing can be effective. Only 2% of terraced plots had a lifespan less than 100 years. The downside to terracing is that it can reduce the amount of land you have available to grow crops. In some cases the benefits of reduced soil erosion, and improved water and nutrient management will outweigh this cost."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The agricultural practices that are most effective will be location-specific. They will depend not only on the environmental qualities of the land, but also on the social and economic constraints of the farmer. 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I don’t buy it. It can be damaging in many ways."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Firstly, it forces some people towards solutions that are ineffective or counterproductive. Some "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.scientificamerican.com/article/only-60-years-of-farming-left-if-soil-degradation-continues/"", ""children"": [{""text"": ""blame the decline"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in soil fertility on the use of fertilizers and other chemical inputs. The “60 harvests” claim from the UN senior official has been used many times to argue for a switch to organic farming systems "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""[here is it "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""children"": [{""url"": ""http://www.fao.org/soils-2015/events/detail/en/c/338738/"", ""children"": [{""text"": ""being used"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""children"": [{""text"": "" at a UN International Year of Soil conference]"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". Michael Gove said the UK had only 30 to 40 years of harvests left because it was “drenching them with chemicals”. But many of the conservation techniques have nothing to do with organic farming. In fact, shifting to a no-tillage approach often requires "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""more "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""pesticides and fertilizers, not less. Since average yields "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/is-organic-agriculture-better-for-the-environment"", ""children"": [{""text"": ""tend to be lower in organic farming"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", it requires more agricultural land. This is in obvious conflict with the best way to reduce soil erosion: have as little cropland as possible. In some contexts organic farming can play a role, but it’s not the ultimate solution. 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"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Journal of Applied Ecology"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""51"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(4), 880-889."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Do we only have 60 harvests left?"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""Claims that the world has only 100, 60, or even 30 years of harvests left often hit the headlines. These claims are overblown, but soil erosion is a problem and we can do something about it."", ""dateline"": ""January 14, 2021"", ""subtitle"": ""Claims that the world has only 100, 60, or even 30 years of harvests left often hit the headlines. These claims are overblown, but soil erosion is a problem and we can do something about it."", ""sidebar-toc"": false, ""featured-image"": ""Soil-Lifespans-Thumbnail.png""}",1,2024-02-21 19:18:29,2021-01-14 11:00:00,2024-02-22 13:21:54,listed,ALBJ4LsP6vNalwkn7iMsEkOpbrUR2zHVT0V43NJOaBoyRdrhZfMzMLM_PK-rX64bAn--lw7asGlH7wXHNU-pyQ,," If one reads the newspaper headlines on the state of the world’s soils it is easy to be convinced that we are only decades away from global famine: * “[The world’s top soil could be gone within 60 years](http://www.fao.org/soils-2015/events/detail/en/c/338738/)” says a senior UN official; * “[Britain has only 100 harvests left](https://www.independent.co.uk/news/uk/home-news/britain-facing-agricultural-crisis-scientists-warn-there-are-only-100-harvests-left-our-farm-soil-9806353.html)” writes the Independent newspaper; * “[UK is 30 to 40 years away from the ‘eradication of soil fertility’ warns Gove](https://www.theguardian.com/environment/2017/oct/24/uk-30-40-years-away-eradication-soil-fertility-warns-michael-gove)” [the former Environment Secretary]. The good news is that these claims are overblown. The bad news is that this doesn’t stop them being repeated over and over. The “60 harvests left” statistic seems to be one that just won’t die. And while the headlines are exaggerations it shouldn’t take away from the fact that many of our soils are degrading and we need to take action to restore them. # Where do these claims come from? The honest answer is that we don’t know. Botanist and science communicator, James Wong, tried to trace these claims back to their roots for an [article in the New Scientist](https://www.newscientist.com/article/mg24232291-100-the-idea-that-there-are-only-100-harvests-left-is-just-a-fantasy/).1 We know that a senior official at a UN FAO farming conference was quoted with the “60 harvests” figure and that Michael Gove mentioned a 30 to 40 year deadline. But we don’t know what they based their assessments on. The “100 harvests” figure seems to link back to a study in the UK conducted by  researchers at the University of Sheffield.2 I say “seems to” because there appears to be no mention of the 100-year figure in the paper. James Wong failed to find where this number came from; I also spent a lot of time digging and did no better. In any case, this study looked at the difference in soils properties of city allotments in Leicester, a city in the UK, and soils from some surrounding farms. It concluded that the soils in city allotments had more organic matter, higher nitrogen levels and a better soil density. Not exactly informative for the larger and more urgent question on the state of the world’s soils. # There is no single lifespan of the world’s soils What do we know about the state of the world’s soils? A recent study by Daniel Evans and colleagues gave us a first assessment of the range of soil lifespans across the world.4 This drew upon a database of 4285 measured soil erosion rates, from 240 studies, covering 255 unique locations across 38 countries. As shown in the map, these 255 locations span all continents of the world. How would we estimate the ‘lifespan’ of a soil? There is no single metric to do so: soils are complex and have a range of properties from nutrient balance, to density, and structure. The best proxy – and the metric that Daniel Evans and his colleagues used – was net erosion rates of the crucial topsoil layer, the topmost layer that is around 30 centimeters thick _[in reality, this thickness varies from soil to soil, but 0.3m is the most commonly adopted figure for this upper productive layer]_. Crops need this layer to grow: it’s where the carbon, water and nutrients get stored.5 Depending on how the soil is managed, this topsoil can thin or thicken. If we know what rate it’s thinning, we can estimate how long it would take for this layer to disappear. For example, if a topsoil was thinning by 0.5 centimeters every year, it would take 60 years to lose 30 centimeters.6 If you want a more detailed understanding of soil lifespans and how they’re calculated, the lead author explains this **[here](https://www.youtube.com/watch?v=XKeGv6z5a10)**. It’s not the only metric that determines soil productivity, but it’s a meaningful metric that tells us something valuable about the state of the world’s soils. # The lifespans of the world’s soils span five orders of magnitude What did this study tell us about the lifespan of our soils? Soils from the 4285 data points in the study were grouped into three categories. ‘**Bare’ soils** are plots of land which are deliberately kept free from any crops to determine erosion rates of soils without vegetation. These are used to assess a ‘worst-case scenario’. **Conventionally managed soils **are those which are actively farmed, without implementing notable conservation practices. These are used to represent a ‘business-as-usual scenario’. **Conservation management soils **were those that had been subject to soil conservation techniques such as land use change (to forests and grasslands) or improved agricultural practices such as intercropping, no-tillage, or contour farming. We will look at the impact of these techniques later. In the chart here we see how the distribution of estimated soil lifespans in these three categories varied across the global dataset. On the x-axis we have the lifespan in years and on the y-axis we have the cumulative percentage of soils that were found to have that lifespan. Notice that the scale on the lifespan axis is logarithmic and stretches from 10 years to 10 million years. This further demonstrates how citing a single lifespan for the world’s soils is inaccurate and nonsensical. Let’s focus on the ‘conventionally managed’ soils, shown in blue. These data are relevant for understanding many of the world’s farming practices. We will look at conservation techniques later. Many of these soils are thinning; some very quickly. 16% have a lifespan of less than 100 years if they continue to erode at their current rates. This is not a local problem: there are examples of soils with lifespans shorter than a century on all continents, including the United States, Australia, Spain, Italy, Brazil and China. The longevity of these soils is concerning and we should be acting quickly to preserve them. But the “60 harvests” claim is quite clearly false. More than 90% of conventionally managed soils had a ‘lifespan’ greater than 60 years. The median was 491 years for thinning soils. Half had a lifespan greater than 1,000 years, and 18% exceeded 10,000 years. There were also some soils that were not eroding at all. Where soil formation rates exceeded erosion rates, soils thickened.In fact, some were thickening – soil was forming quicker than it was eroding. In the bottom-right of the chart we see the rates of soil gain. 7% of conventionally managed soils were thickening. If we were to keep our land completely bare – by removing any vegetation and preventing any natural regrowth through pesticides – our soils could erode more quickly. One-third (34%) of bare soils had lifespans less than 100 years. There is no single figure for how many harvests the world has left because there is so much variation in the types, quality, and management of our soils. It’s just implausible that they would all be degrading at exactly the same rate. As these results show: some soils are eroding quickly while others are thickening. ### Explore an interactive version of this chart on soil lifespans https://ourworldindata.org/grapher/soil-lifespans # What can we do to slow erosion and restore our soils? It’s concerning that so many of our soils are thinning. Some, very quickly. But, there are things we can do to extend the lifespan of our soils. Take a look at the ‘conservation’ curve in the previous chart. It’s shifted far to the right – even more so because the lifespan scale is logarithmic – meaning these soils are eroding much more slowly than conventionally managed soils, if at all. In fact, one-fifth were actually thickening (meaning soil was forming faster than it was eroding). A comparison of these two groups is shown in the table. The share that had a lifespan less than 100 years was less than half that of conventionally-managed soils – 7% versus 16%. Half of the soils managed with conservation management had a lifespan greater than 5000 years; and 40% exceeded 10,000 years. This sounds promising, but what does ‘conservation’ actually mean? What practices should we put in place? There are four interventions we should consider according to Evans et al.4 **We could switch from agricultural land use to forest or grassland**. This is the most effective way to extend soil lifespans, but it reduces the land available for farming. As the global population increases, and demands for food rise, we need to find ways of protecting our cultivable soils. If we can increase [crop yields](http://ourworldindata.org/crop-yields) we have the opportunity to reduce the [amount of arable land we need](https://ourworldindata.org/grapher/arable-land-pin) to meet this demand. **We can plant cover crops during the non-harvesting season**. If we need to use our land for farming, cover cropping can be effective in improving soil quality. None of the plots that used cover cropping in the study had a lifespan of less than 100 years. This is a practice that involves growing a crop for the purpose of maintaining a vegetative cover and preventing soils from becoming bare and susceptible to erosion between growing seasons. We know from our comparisons above that ‘bare’ soils erode much more quickly. To prevent this, you can plant a cover crop during the off-season, which maintains soil structure, fertility and enhances organic matter. Cover crops consist of leguminous plants such as peas, beans and lentils. Cover cropping has been shown to be effective in reducing soil erosion.7, 8,9 **We can use minimal or zero tillage practices**. Conventional ‘tillage’ is a common practice where farmers will mechanically agitate and overturn the soil using ploughs. This is done to mechanically dislodge and destroy weeds; aerate the top layer of soil, and mix the nutrients evenly throughout the soil profile. But this also creates negative impacts: it can damage the soil structure, leading to soils becoming more susceptible to erosion. Increasing soil erosion can lead to a loss of nutrients and organic matter; it may increase chemical run-off from the land; and may reduce water infiltration. Minimal or zero-tillage practices try to manage soils without this mechanical overturning of the soil. This reduces soil erosion but has its own trade-offs: because weeds are not disturbed by ploughing, it often requires more herbicides to kill them. Some of these trade-offs can be reduced by combining it with cover cropping. Cover crops such as legumes add nitrogen to the soil, and can protect the soil from weeds and pests. **We can implement contour cultivation or terracing on hillslopes**. The gradient of the land makes a big difference to the susceptibility of the soil to erosion. Soils on a steep slope erode much more quickly. 37% of soils which were cultivated up-and-down the slope had a lifespan less than 100 years. Contour cultivation – where you grow crops perpendicular to or across the slope – reduced this to 7%. Contour cultivation is not always practical on very steep gradients. In this case, terracing can be effective. Only 2% of terraced plots had a lifespan less than 100 years. The downside to terracing is that it can reduce the amount of land you have available to grow crops. In some cases the benefits of reduced soil erosion, and improved water and nutrient management will outweigh this cost. The agricultural practices that are most effective will be location-specific. They will depend not only on the environmental qualities of the land, but also on the social and economic constraints of the farmer. There’s no universal solution, but it is clear that there are plenty of opportunities to increase the lifespans of soils across the world. |**Soil management**|**% with lifespan** **less than 100 years**|**% with lifespan** **greater than 5000 years**|**% with lifespan** **greater than 10,000 years**| |Conventional|16%|23%|18%| |Conservation|7%|48%|39%| # Extreme headlines could do more harm than good People will often argue that while extreme headlines may be untruthful, they are worth it if they force people to take action. I don’t buy it. It can be damaging in many ways. Firstly, it forces some people towards solutions that are ineffective or counterproductive. Some [blame the decline](https://www.scientificamerican.com/article/only-60-years-of-farming-left-if-soil-degradation-continues/) in soil fertility on the use of fertilizers and other chemical inputs. The “60 harvests” claim from the UN senior official has been used many times to argue for a switch to organic farming systems _[here is it __[being used](http://www.fao.org/soils-2015/events/detail/en/c/338738/)__ at a UN International Year of Soil conference]_. Michael Gove said the UK had only 30 to 40 years of harvests left because it was “drenching them with chemicals”. But many of the conservation techniques have nothing to do with organic farming. In fact, shifting to a no-tillage approach often requires _more _pesticides and fertilizers, not less. Since average yields [tend to be lower in organic farming](https://ourworldindata.org/is-organic-agriculture-better-for-the-environment), it requires more agricultural land. This is in obvious conflict with the best way to reduce soil erosion: have as little cropland as possible. In some contexts organic farming can play a role, but it’s not the ultimate solution. Misleading headlines convince people that it is. Exaggeration also creates the opposite problem: apathy. Many people don’t take it seriously and dismiss that there’s any problem. The headlines might be overblown, but this shouldn’t detract from the fact that soil erosion _is_ a serious problem. It’s one we can’t afford to ignore and as I have shown it is a problem that we can do something against. --- James Wong interviewed a number of soil scientists and asked if they had seen a credible single figure for the number of “harvests left” in the scientific research. None of them had. Their responses to this metric ranged from “hardly useful” to “almost insulting”. Edmondson, J. L., Davies, Z. G., Gaston, K. J., & Leake, J. R. (2014). [Urban cultivation in allotments maintains soil qualities adversely affected by conventional agriculture](https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2664.12254). _Journal of Applied Ecology_, _51_(4), 880-889. Evans, D. L., Quinton, J. N., Davies, J. A. C., Zhao, J., & Govers, G. (2020).[ ](https://iopscience.iop.org/article/10.1088/1748-9326/aba2fd/pdf)[Soil lifespans and how they can be extended by land use and management change](https://iopscience.iop.org/article/10.1088/1748-9326/aba2fd/pdf). _Environmental Research Letters_, _15_(9), 0940b2. Evans, D. L., Quinton, J. N., Davies, J. A. C., Zhao, J., & Govers, G. (2020). [Soil lifespans and how they can be extended by land use and management change](https://iopscience.iop.org/article/10.1088/1748-9326/aba2fd/pdf). _Environmental Research Letters_, _15_(9), 0940b2. Power, J. F., Sandoval, F. M., Ries, R. E., & Merrill, S. D. (1981). [Effects of topsoil and subsoil thickness on soil water content and crop production on a disturbed soil](https://acsess.onlinelibrary.wiley.com/doi/abs/10.2136/sssaj1981.03615995004500010027x). _Soil Science Society of America Journal_, _45_(1), 124-129. We can calculate this as [30 centimeters / 0.5 centimeters per year = 60 years]. Gyssels, G., Poesen, J., Bochet, E., & Li, Y. (2005). [Impact of plant roots on the resistance of soils to erosion by water: a review](https://journals.sagepub.com/doi/10.1191/0309133305pp443ra). _Progress in Physical Geography_, _29_(2), 189-217. Nyakatawa, E. Z., Reddy, K. C., & Lemunyon, J. L. (2001). [Predicting soil erosion in conservation tillage cotton production systems using the revised universal soil loss equation (RUSLE)](https://www.sciencedirect.com/science/article/pii/S0167198700001781?casa_token=2hQSKi5AvzUAAAAA:pFF9aInIBs7pXDlvC4yOfCVgVKOoMFK_IkRRjeBwNmh_5FlCt-HmGcnbbGfTFuDi8Tk0E7pMN0jB). _Soil and Tillage Research_, _57_(4), 213-224. Verstraeten, G., Van Oost, K., Van Rompaey, A., Poesen, J., & Govers, G. (2002). [Evaluating an integrated approach to catchment management to reduce soil loss and sediment pollution through modelling](https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1475-2743.2002.tb00257.x). _Soil Use and Management_, _18_(4), 386-394.",Do we only have 60 harvests left? 1qQqo2roAI9agAMPd0wcgY3haOKflkKYunyERiYRKGpw,spanish-flu-largest-influenza-pandemic-in-history,article,"{""toc"": [{""slug"": ""how-many-people-died-in-the-spanish-flu-pandemic"", ""text"": ""How many people died in the Spanish flu pandemic?"", ""title"": ""How many people died in the Spanish flu pandemic?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""the-global-death-count-of-the-flu-today"", ""text"": ""The global death count of the flu today:"", ""title"": ""The global death count of the flu today:"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""global-deaths-of-the-spanish-flu"", ""text"": ""Global deaths of the Spanish flu"", ""title"": ""Global deaths of the Spanish flu"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""the-global-death-rate-of-the-spanish-flu"", ""text"": ""The global death rate of the Spanish flu"", ""title"": ""The global death rate of the Spanish flu"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""other-large-influenza-pandemics"", ""text"": ""Other large influenza pandemics"", ""title"": ""Other large influenza pandemics"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""the-impact-of-the-spanish-flu-on-different-age-groups"", ""text"": ""The impact of the Spanish flu on different age groups"", ""title"": ""The impact of the Spanish flu on different age groups"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-the-spanish-flu-differs-from-the-coronavirus-outbreak-in-2020"", ""text"": ""How the Spanish flu differs from the Coronavirus outbreak in 2020"", ""title"": ""How the Spanish flu differs from the Coronavirus outbreak in 2020"", ""supertitle"": """", ""isSubheading"": true}], ""body"": [{""text"": [{""type"": ""text"", ""value"": [{""text"": ""Parts of the article were revised in May 2023, and the chart on death tolls from flu pandemics was updated in April 2024."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the last 150 years the world has seen an unprecedented improvement in health. The visualization shows that in many countries life expectancy, which measures the average age of death, doubled from around 40 years or less to more than 80 years. This was not just an achievement across the countries shown here; life expectancy "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/life-expectancy#how-did-life-expectancy-change-over-time"", ""children"": [{""text"": ""has doubled"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in all regions of the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What also stands out is how abrupt and damning negative health events can be. Most striking is the large, sudden decline of life expectancy in 1918, caused by an unusually deadly influenza pandemic that became known as the ‘Spanish flu’."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To make sense of the fact life expectancy declined so abruptly, one has to keep in mind what it measures. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Period life expectancy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", which is the precise name for this measure, captures the mortality in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""one particular year"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". It summarizes the mortality in a particular year by calculating the average age of death of a hypothetical cohort of people for which that year’s mortality pattern would remain constant throughout their entire lifetimes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This influenza outbreak wasn’t restricted to Spain and it didn’t even originate there. Recent genetic research suggests that the strain emerged a few years earlier, around 1915, but did not take off until later on. The earliest recorded outbreak was in Kansas in the United States in 1918."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But it "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.history.com/news/why-was-it-called-the-spanish-flu"", ""children"": [{""text"": ""was named"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" as such because Spain was neutral in the First World War (1914-18), which meant it was free to report on the severity of the pandemic, while countries that were fighting tried to suppress reports on how the influenza impacted their population to maintain morale and not appear weakened in the eyes of the enemies. Since it is very valuable to speak openly about the threat of an infectious disease I think Spain should be proud that it was not like other countries at that time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The virus spread rapidly and eventually reached all parts of the world: the epidemic became a pandemic."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Even in a much less-connected world the virus eventually reached extremely remote places such as the Alaskan wilderness and Samoa in the middle of the Pacific islands. In these remote places the mortality rate was often particularly high."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/life-expectancy?tab=chart&country=FIN~NOR~ESP~SWE~CHE~USA"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""How many people died in the Spanish flu pandemic?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""text"": [{""text"": ""The global death count of the flu today:"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To have a context for the severity of influenza pandemics it might be helpful to know the death count of a typical flu season. Current estimates for the annual number of deaths from influenza are around 400,000 deaths per year. Paget et al (2019) suggest an average of 389,000 with an uncertainty range 294,000 from 518,000."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This means that in recent years the flu was responsible for the death of 0.005% of the world population."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Even in comparison to the low estimate for the death count of the Spanish flu (17.4 million) this pandemic, more than a century ago, caused a death rate that was 182-times higher than today’s baseline."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Global deaths of the Spanish flu"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Several research teams have worked on the difficult problem of reconstructing the global health impact of the Spanish flu."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The visualization here shows the available estimates from the different research publications discussed in the following. The range of published estimates for the Spanish flu is particularly wide."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The widely cited study by Johnson and Mueller (2002) arrives at a very high estimate of at least 50 million global deaths. But the authors suggest that this could be an underestimation and that the true death toll was as high as 100 million."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Patterson and Pyle (1991) estimated that between 24.7 and 39.3 million died from the pandemic."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The more recent study by Spreeuwenberg et al. (2018) concluded that earlier estimates have been too high. Their own estimate is 17.4 million deaths."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""The global death rate of the Spanish flu"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How do these estimates compare with the size of the world population at the time? How large was the share who died in the pandemic?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/world-population-growth#how-has-world-population-growth-changed-over-time"", ""children"": [{""text"": ""Estimates"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" suggest that the world population in 1918 was 1.8 billion."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Based on this, the low estimate of 17.4 million deaths by Spreeuwenberg et al. (2018) implies that the Spanish flu killed almost 1% of the world population."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The estimate of 50 million deaths published by Johnson and Mueller implies that the Spanish flu killed 2.7% of the world population. And if it was in fact higher – 100 million as these authors suggest – then the global death rate would have been 5.4%."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world population was growing by around 13 million every year in this period which suggests that the period of the Spanish flu was likely the last time in history when the world population was declining."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""influenza-pandemics-in-comparison-v9.png"", ""parseErrors"": []}, {""text"": [{""text"": ""Other large influenza pandemics"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Spanish flu pandemic was the largest, but not the only large recent influenza pandemic."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Two decades before the Spanish flu the Russian flu pandemic (1889-1894) is believed to have killed 1 million people."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Estimates for the death toll of the “Asian Flu” (1957-1958) range from 1.7 to 2.7 million according to Spreeuwenberg et al. (2018)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The same authors estimate that the “Hong Kong Flu” (1968-1969) killed between 2 and 3.8 million people."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The “Russian Flu” pandemic of 1889-1890 is believed to be caused by an H3 pandemic virus."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" According to Spreeuwenberg et al. (2018) around 3.7 to 5.1 million people died worldwide."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The “Swine flu” pandemic of 2009-2010 was caused by a new H1N1 pandemic virus. Several research groups have made estimates of the global death toll, which ranges from 130,000 to 1.87 million people worldwide."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-15"", ""children"": [{""children"": [{""text"": ""15"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What becomes clear from this overview are two things: influenza pandemics are not rare, but the Spanish flu of 1918 was by far the most devastating influenza pandemic in recorded history."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""The impact of the Spanish flu on different age groups"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This last visualization here shows the life expectancy in England and Wales by age."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The red line shows the life expectancy for a newborn, with the rainbow-colored lines above showing how long a person could expect to live once they had reached that given, older, age. The light green line, for example, represents the life expectancy for children who have reached age 10."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It shows that life expectancy increased at all ages, which means that the often-heard assertion that life expectancy ‘only’ increased because child mortality declined is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/life-expectancy#it-is-not-only-about-child-mortality-life-expectancy-by-age"", ""children"": [{""text"": ""not true"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""With respect to the impact of the Spanish flu it is striking that the visualization shows that the pandemic had little impact on older people. While the life expectancy at birth and at young ages declined by more than ten years, the life expectancy of 60- and 70-year olds saw no change. This is at odds with what one would reasonably expect: older populations tend to be most vulnerable to influenza outbreaks and respiratory infections. If we look at mortality for both "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/pneumonia-mortality-by-age?time=2017"", ""children"": [{""text"": ""lower respiratory infections (pneumonia)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/death-rate-upper-respiratory-age"", ""children"": [{""text"": ""upper respiratory infections"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" today, death rates are highest for those who are 70 years and older."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This data tells us that young people "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/age-structure"", ""children"": [{""text"": ""accounted for a large share"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of the deaths, this made this pandemic especially devastating."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Why were older people so resilient to the 1918 pandemic? The research literature suggests that this was the case because older people had lived through an earlier flu outbreak – the already discussed "", ""spanType"": ""span-simple-text""}, {""url"": ""https://en.wikipedia.org/wiki/1889%E2%80%9390_flu_pandemic"", ""children"": [{""text"": ""‘Russian flu pandemic’"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of 1889–90 – which gave those who lived through it some immunity for the later outbreak of the Spanish flu."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-16"", ""children"": [{""children"": [{""text"": ""16"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The earlier 1889-90 pandemic might have given the older population some immunity, but was a destructive event in itself. According to Smith 132,000 people died in England, Wales, and Ireland alone."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-17"", ""children"": [{""children"": [{""text"": ""17"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Life-expectancy-by-age-in-the-UK-1700-to-2013.png"", ""parseErrors"": []}, {""text"": [{""text"": ""How the Spanish flu differs from the Coronavirus outbreak in 2020"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Writing in early March 2020 it is an obvious question to ask how the ongoing outbreak of Covid-19 compares. There are a number of important differences that should be considered."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""They are not the same disease and the virus causing these diseases are very different. The virus that causes Covid-19 is a coronavirus, not an influenza virus that caused the Spanish flu and the other influenza pandemics listed above."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The age-specific mortality seems to be very different. As we’ve seen above, the Spanish flu in 1918 was especially dangerous to infants and younger people. The new coronavirus that causes Covid-19 appears to be most lethal to the elderly, based on early evidence in China."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-18"", ""children"": [{""children"": [{""text"": ""18"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We’ve also seen above that during the Spanish flu many countries tried to suppress any information about the influenza outbreak. Today the sharing of data, research, and news is certainly not perfect, but very different and much more open than in the past."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But it is true that the world today is much better connected. In 1918 it was railroads and steamships that connected the world. Today planes can carry people and viruses to many corners of the world in a very short time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Differences in health systems and infrastructure also matter. The Spanish flu hit the world in the days before antibiotics were invented; and many deaths, perhaps most, were not caused by the influenza virus itself, but by secondary "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""bacterial"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" infections. Morens et al (2008) found that during the Spanish flu “the majority of deaths … likely resulted directly from secondary bacterial pneumonia caused by common upper respiratory–tract bacteria.”"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-19"", ""children"": [{""children"": [{""text"": ""19"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""And not just health systems were different, but also the health and living conditions of the global population. The 1918 flu hit a world population of which a very large share "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/extreme-history-methods"", ""children"": [{""text"": ""was extremely poor"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – large shares of the population were undernourished, in most parts of the world the populations lived "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/life-expectancy-globally"", ""children"": [{""text"": ""in very poor health"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and overcrowding, poor sanitation and low hygiene standards were common. Additionally the populations in many parts of the world were weakened by a global war. Public resources were small and many countries had just spent "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/military-spending#defence-spending-in-uk-over-the-very-long-run"", ""children"": [{""text"": ""large shares"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of their resources on the war."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While most of the world is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/a-history-of-global-living-conditions-in-5-charts"", ""children"": [{""text"": ""much richer and healthier now"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", the concern today too is that it is the poorest people that are going to be hit hardest by the Covid-19 outbreak."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-20"", ""children"": [{""children"": [{""text"": ""20"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These differences suggest that one should be cautious in drawing lessons from the outbreak a century ago."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But the Spanish flu reminds us just how large the impact of a pandemic can be, even in countries that had already been successful in improving population health. A new pathogen can cause terrible devastation and lead to the death of millions. For this reason the Spanish flu has been cited as a warning and as a motivation to prepare well for large pandemic outbreaks, which have been considered likely by many researchers."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-21"", ""children"": [{""children"": [{""text"": ""21"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""0e4a5001b5031a7abec1e1fcea6adbd2d5736b8b"": {""id"": ""0e4a5001b5031a7abec1e1fcea6adbd2d5736b8b"", ""index"": 10, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""In available historical reconstructions (like "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/population?country=OWID_WRL"", ""children"": [{""text"": ""this one"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "") this decline is not shown. The reason for this is that precise annual counts of the world population are not available for the past."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Instead historians try to reconstruct the population figures for 5-year or 10-year intervals and the annual estimates are interpolations between these estimates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In other words, if we had precise annual counts they would likely show a decline of the world population in 1918."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""1546cb83ecd0aa10449b8b0b04b7939c0c40fab2"": {""id"": ""1546cb83ecd0aa10449b8b0b04b7939c0c40fab2"", ""index"": 11, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Nickol, M.E., Kindrachuk, J. (2019) – A year of terror and a century of reflection: perspectives on the great influenza pandemic of 1918–1919. BMC Infect Dis 19, 117 (2019). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1186/s12879-019-3750-8"", ""children"": [{""text"": ""https://doi.org/10.1186/s12879-019-3750-8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""According to Smith (1995) 132,000 died in England, Wales, and Ireland alone."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Smith F. B. (1995) – The Russian influenza in the United Kingdom, 1889-1894. Soc. Hist. Med. 8 55–73. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://academic.oup.com/shm/article-lookup/doi/10.1093/shm/8.1.55"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""1a1e3ffcf5dc01d76ea6531cfba6c6f2f4981c62"": {""id"": ""1a1e3ffcf5dc01d76ea6531cfba6c6f2f4981c62"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The calculation is (17,400,000/1,832,196,157)*100=0.95"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""2f524e62e7a8b8fe64cf93724f5a8df1c2ec1861"": {""id"": ""2f524e62e7a8b8fe64cf93724f5a8df1c2ec1861"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""This is (389,000/7,500,000,000)*100=0.0052%"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""31aa5016d46544d845538507b4bc63bb728f9f46"": {""id"": ""31aa5016d46544d845538507b4bc63bb728f9f46"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""From the paper: "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Further research has seen the consistent upward revision of the estimated global mortality of the pandemic, which a 1920s calculation put in the vicinity of 21.5 million. A 1991 paper revised the mortality as being in the range 24.7-39.3 million. This paper suggests that it was of the order of 50 million. However, it must be acknowledged that even this vast figure may be substantially lower than the real toll, perhaps as much as 100 percent understated."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Johnson, N.P. and Mueller, J. (2002) – Updating the accounts: global mortality of the 1918-1920 “Spanish\"" influenza pandemic. In Bulletin of the History of Medicine, 76(1), pp.105-115. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.jstor.org/stable/44446153?read-now=1&seq=1#page_scan_tab_contents"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The paper includes detailed breakdowns of mortality estimates by world region and country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""3311b289b530328df5efed99a5258f2dc6a2c426"": {""id"": ""3311b289b530328df5efed99a5258f2dc6a2c426"", ""index"": 13, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Worobey, M., Han, G.-Z., & Rambaut, A. (2014). Genesis and pathogenesis of the 1918 pandemic H1N1 influenza A virus. Proceedings of the National Academy of Sciences, 111(22), 8107–8112. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1073/pnas.1324197111"", ""children"": [{""text"": ""https://doi.org/10.1073/pnas.1324197111"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""41d3c2a23fb0a2d83617981a068ec34fd42e0829"": {""id"": ""41d3c2a23fb0a2d83617981a068ec34fd42e0829"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Paget et al (2019) suggest an “average of 389 000 (uncertainty range 294 000-518 000) respiratory deaths were associated with influenza globally each year”."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""John Paget, Peter Spreeuwenberg, Vivek Charu Robert J Taylor, A Danielle Iuliano, Joseph Bresee, Lone Simonsen, Cecile Viboud,3 and for the Global Seasonal Influenza-associated Mortality Collaborator Network and GLaMOR Collaborating Teams (2019) – Global mortality associated with seasonal influenza epidemics: New burden estimates and predictors from the GLaMOR Project. In J Glob Health. 2019 Dec; 9(2): 020421. Published online 2019 Oct 22. doi: 10.7189/jogh.09.020421 PMCID: PMC6815659 PMID: 31673337 Online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6815659/"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""4a081c9984047da72b95c4bbd99fba4adfe24cad"": {""id"": ""4a081c9984047da72b95c4bbd99fba4adfe24cad"", ""index"": 19, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Gilbert, Marius, Giulia Pullano, Francesco Pinotti, Eugenio Valdano, Chiara Poletto, Pierre-Yves Boëlle, Eric D’Ortenzio, et al. (2020) – “Preparedness and Vulnerability of African Countries against Importations of COVID-19: A Modelling Study.” "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""The Lancet"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" (February 20, 2020)."", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/S0140-6736(20)30411-6"", ""children"": [{""text"": "" https://doi.org/10.1016/S0140-6736(20)30411-6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""See also"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Alyssa S. Parpia, Martial L. Ndeffo-Mbah, Natasha S. Wenzel, and Alison P. Galvani (2016) – Effects of Response to 2014–2015 Ebola Outbreak on Deaths from Malaria, HIV/AIDS, and Tuberculosis, West Africa. In Emerg Infect Dis. 2016 Mar; 22(3): 433–441. doi: 10.3201/eid2203.150977 PMCID: PMC4766886 PMID: 26886846 Online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4766886/?fbclid=IwAR26EF-QA82ds5Jz81NNrIHb77S3n8L9D5YnL3GlLSRmQ3ms051ZCVx0zt8#!po=16.6667"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""5380d937132d320c2d4bfee20194610a08360e35"": {""id"": ""5380d937132d320c2d4bfee20194610a08360e35"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Worobey, M., Han, G.-Z., & Rambaut, A. (2014). Genesis and pathogenesis of the 1918 pandemic H1N1 influenza A virus. Proceedings of the National Academy of Sciences, 111(22), 8107–8112. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1073/pnas.1324197111"", ""children"": [{""text"": ""https://doi.org/10.1073/pnas.1324197111"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Barry, J. M. (2004). The site of origin of the 1918 influenza pandemic and its public health implications. Journal of Translational Medicine, 2(1), 3. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1186/1479-5876-2-3"", ""children"": [{""text"": ""https://doi.org/10.1186/1479-5876-2-3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""6712e988a6b8a3ddc14f647c0ac420bcce3e78f2"": {""id"": ""6712e988a6b8a3ddc14f647c0ac420bcce3e78f2"", ""index"": 7, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""P. Spreeuwenberg; et al. (1 December 2018). \""Reassessing the Global Mortality Burden of the 1918 Influenza Pandemic\"". American Journal of Epidemiology. 187 (12): 2561–2567. doi:10.1093/aje/kwy191. PMID 30202996. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://academic.oup.com/aje/article/187/12/2561/5092383"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""72eb568d12fcc4df73225e9fce8d6d8100a2247b"": {""id"": ""72eb568d12fcc4df73225e9fce8d6d8100a2247b"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For the definitions of epidemic and pandemic see the CDC "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.cdc.gov/csels/dsepd/ss1978/lesson1/section11.html"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""81835af42de0755e795a060c10914b3f2caf44ba"": {""id"": ""81835af42de0755e795a060c10914b3f2caf44ba"", ""index"": 20, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""See for example: Pandemic influenza preparedness and response – WHO guidance document. Published in 2009 by the WHO. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://apps.who.int/iris/bitstream/handle/10665/44123/9789241547680_eng.pdf"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "".Roman Duda (2016) – Problem profile: Biorisk reduction. Published by 80,000 hours. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://80000hours.org/problem-profiles/biosecurity/"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""92d81e346a15345f20468b7a1791d3b34dfa139e"": {""id"": ""92d81e346a15345f20468b7a1791d3b34dfa139e"", ""index"": 16, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Smith F. B. (1995) – The Russian influenza in the United Kingdom, 1889-1894. Soc. Hist. Med. 8 55–73. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://academic.oup.com/shm/article-lookup/doi/10.1093/shm/8.1.55"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""bfcd324db8240a46af13f85f3075e4fad6c8d982"": {""id"": ""bfcd324db8240a46af13f85f3075e4fad6c8d982"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Burnet F. M., Clark E. (1942) – Influenza: A Survey of the Last 50 Years in the Light of Modern Work on the Virus of Epidemic Influenza. London: Macmillan. Partly online on Google books."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The mortality rate in some populations like Alaska and Samoa were said to be 90% and 25% respectively. See the following two publications:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""McLane, J. R. (2013). "", ""spanType"": ""span-simple-text""}, {""url"": ""https://sites.otago.ac.nz/Sites/article/view/215"", ""children"": [{""text"": ""Paradise locked: The 1918 influenza pandemic in American Samoa. Sites: a journal of social anthropology and cultural studies"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", 10(2), 30-51."", ""spanType"": ""span-simple-text""}, {""url"": ""https://sites.otago.ac.nz/Sites/article/view/215"", ""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Mamelund, S. E. (2017). Profiling a Pandemic. Who were the victims of the Spanish flu?{ref} While peak mortality was reached in 1918 the pandemic did not wane until two years later in late 1920."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""c5848119f5fd0bedc56c4bd2cd6ba98cacd514d1"": {""id"": ""c5848119f5fd0bedc56c4bd2cd6ba98cacd514d1"", ""index"": 12, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Spreeuwenberg, P., Kroneman, M., & Paget, J. (2018). Reassessing the Global Mortality Burden of the 1918 Influenza Pandemic. American Journal of Epidemiology, 187(12), 2561–2567. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1093/aje/kwy191"", ""children"": [{""text"": ""https://doi.org/10.1093/aje/kwy191"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""cb284500466e767df4196626fbb219b2116bdb9e"": {""id"": ""cb284500466e767df4196626fbb219b2116bdb9e"", ""index"": 14, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Dawood, F. S., Iuliano, A. D., Reed, C., Meltzer, M. I., Shay, D. K., Cheng, P.-Y., Bandaranayake, D., Breiman, R. F., Brooks, W. A., Buchy, P., Feikin, D. R., Fowler, K. B., Gordon, A., Hien, N. T., Horby, P., Huang, Q. S., Katz, M. A., Krishnan, A., Lal, R., … Widdowson, M.-A. (2012). Estimated global mortality associated with the first 12 months of 2009 pandemic influenza A H1N1 virus circulation: A modelling study. The Lancet Infectious Diseases, 12(9), 687–695. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/S1473-3099(12)70121-4"", ""children"": [{""text"": ""https://doi.org/10.1016/S1473-3099(12)70121-4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Simonsen, L., Spreeuwenberg, P., Lustig, R., Taylor, R. J., Fleming, D. M., Kroneman, M., Van Kerkhove, M. D., Mounts, A. W., Paget, W. J., & the GLaMOR Collaborating Teams. (2013). Global Mortality Estimates for the 2009 Influenza Pandemic from the GLaMOR Project: A Modeling Study. PLoS Medicine, 10(11), e1001558. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1371/journal.pmed.1001558"", ""children"": [{""text"": ""https://doi.org/10.1371/journal.pmed.1001558"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Spreeuwenberg, P., Kroneman, M., & Paget, J. (2018). Reassessing the Global Mortality Burden of the 1918 Influenza Pandemic. American Journal of Epidemiology, 187(12), 2561–2567. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1093/aje/kwy191"", ""children"": [{""text"": ""https://doi.org/10.1093/aje/kwy191"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""cd4b8601f631042fcd715cec7611502aff61397e"": {""id"": ""cd4b8601f631042fcd715cec7611502aff61397e"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Patterson and Pyle (1991) wrote 'we believe that approximately 30 million is the best estimate for the terrible demographic toll of the influenza pandemic of 1918' and published a range from 24.7-39.3 million deaths."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Patterson, K.D. and Pyle, G.F. (1991) – The geography and mortality of the 1918 influenza pandemic. Bulletin of the History of Medicine, 65(1), p.4. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20171022101640/https://pida.nihlibrary.com/sites/pida.nihlibrary.com/files/pdf_files/1991_K.David%20Patterson_The%20geography%20and%20mortality%20of%20the%201918%20influenza%20pandemic..pdf"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ea89ea35fd3afc9135de4a26c1aee89fad8e2a16"": {""id"": ""ea89ea35fd3afc9135de4a26c1aee89fad8e2a16"", ""index"": 17, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Zhonghua Liu Xing Bing Xue Za Zhi (2020) – The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Feb 17;41(2):145-151. doi: 10.3760/cma.j.issn.0254-6450.2020.02.003. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ncbi.nlm.nih.gov/pubmed/32064853?fbclid=IwAR3JCxH50VTfg3Q_02YTLdz2Tk7yBTmt-5oCxE4KlBe0evh7ByK3HPVU-pU"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ec9d63bad0e5387c7d81caf737f5166977a1b2ce"": {""id"": ""ec9d63bad0e5387c7d81caf737f5166977a1b2ce"", ""index"": 9, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""50,000,000 deaths / 1,832,196,157 people = 0.02729 And with a death count twice is high: 0.05458."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f0362da343e21ee46e25f8f064dcfa846b39b966"": {""id"": ""f0362da343e21ee46e25f8f064dcfa846b39b966"", ""index"": 18, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Morens D. M., Taubenberger J. K., Fauci A. S. (2008) – Predominant role of bacterial pneumonia as a cause of death in pandemic influenza: implications for pandemic influenza preparedness. J. Infect. Dis. 198 962–970. 10.1086/591708. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2599911/"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""fd26c2ea7b2605c1fbc341e4c6dd2680210d20bd"": {""id"": ""fd26c2ea7b2605c1fbc341e4c6dd2680210d20bd"", ""index"": 15, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Gagnon et al. (2013) – Age-Specific Mortality During the 1918 Influenza Pandemic: Unravelling the Mystery of High Young Adult Mortality.PLoS One. 2013; 8(8): e69586. Published online 2013 Aug 5. doi: 10.1371/journal.pone.0069586. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3734171/"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Russian flu pandemic was a devastating event in itself. Smith (1995) estimates that the Russian flu killed 132,000 in England, Wales, and Ireland."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Smith F. B. (1995) – The Russian influenza in the United Kingdom, 1889-1894. Soc. Hist. Med. 8 55–73. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""https://academic.oup.com/shm/article-lookup/doi/10.1093/shm/8.1.55"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""The Spanish flu: The global impact of the largest influenza pandemic in history"", ""authors"": [""Max Roser""], ""excerpt"": ""The Spanish flu pandemic had a devastating impact on the global population."", ""dateline"": ""March 4, 2020"", ""subtitle"": """", ""featured-image"": ""Life-expectancy-spanish-flu.png""}",1,2023-07-06 14:13:36,2020-03-04 10:23:59,2024-03-18 15:41:59,listed,ALBJ4LvsvfDRL5W-ODlyR3DaFa_cT-fBxe24FXHZXwnRDCGoVj1likG8CLwu8UjBkSIFQaiuuw2Vv8UNxkFZ1g,," In the last 150 years the world has seen an unprecedented improvement in health. The visualization shows that in many countries life expectancy, which measures the average age of death, doubled from around 40 years or less to more than 80 years. This was not just an achievement across the countries shown here; life expectancy [has doubled](https://ourworldindata.org/life-expectancy#how-did-life-expectancy-change-over-time) in all regions of the world. What also stands out is how abrupt and damning negative health events can be. Most striking is the large, sudden decline of life expectancy in 1918, caused by an unusually deadly influenza pandemic that became known as the ‘Spanish flu’. To make sense of the fact life expectancy declined so abruptly, one has to keep in mind what it measures. _Period life expectancy_, which is the precise name for this measure, captures the mortality in _one particular year_. It summarizes the mortality in a particular year by calculating the average age of death of a hypothetical cohort of people for which that year’s mortality pattern would remain constant throughout their entire lifetimes. This influenza outbreak wasn’t restricted to Spain and it didn’t even originate there. Recent genetic research suggests that the strain emerged a few years earlier, around 1915, but did not take off until later on. The earliest recorded outbreak was in Kansas in the United States in 1918.1 But it [was named](https://www.history.com/news/why-was-it-called-the-spanish-flu) as such because Spain was neutral in the First World War (1914-18), which meant it was free to report on the severity of the pandemic, while countries that were fighting tried to suppress reports on how the influenza impacted their population to maintain morale and not appear weakened in the eyes of the enemies. Since it is very valuable to speak openly about the threat of an infectious disease I think Spain should be proud that it was not like other countries at that time. The virus spread rapidly and eventually reached all parts of the world: the epidemic became a pandemic.2 Even in a much less-connected world the virus eventually reached extremely remote places such as the Alaskan wilderness and Samoa in the middle of the Pacific islands. In these remote places the mortality rate was often particularly high.3 ## How many people died in the Spanish flu pandemic? ### The global death count of the flu today: To have a context for the severity of influenza pandemics it might be helpful to know the death count of a typical flu season. Current estimates for the annual number of deaths from influenza are around 400,000 deaths per year. Paget et al (2019) suggest an average of 389,000 with an uncertainty range 294,000 from 518,000.4 This means that in recent years the flu was responsible for the death of 0.005% of the world population.5 Even in comparison to the low estimate for the death count of the Spanish flu (17.4 million) this pandemic, more than a century ago, caused a death rate that was 182-times higher than today’s baseline. ### Global deaths of the Spanish flu Several research teams have worked on the difficult problem of reconstructing the global health impact of the Spanish flu. The visualization here shows the available estimates from the different research publications discussed in the following. The range of published estimates for the Spanish flu is particularly wide. The widely cited study by Johnson and Mueller (2002) arrives at a very high estimate of at least 50 million global deaths. But the authors suggest that this could be an underestimation and that the true death toll was as high as 100 million.6 Patterson and Pyle (1991) estimated that between 24.7 and 39.3 million died from the pandemic.7 The more recent study by Spreeuwenberg et al. (2018) concluded that earlier estimates have been too high. Their own estimate is 17.4 million deaths.8 ### The global death rate of the Spanish flu How do these estimates compare with the size of the world population at the time? How large was the share who died in the pandemic? [Estimates](https://ourworldindata.org/world-population-growth#how-has-world-population-growth-changed-over-time) suggest that the world population in 1918 was 1.8 billion. Based on this, the low estimate of 17.4 million deaths by Spreeuwenberg et al. (2018) implies that the Spanish flu killed almost 1% of the world population.9 The estimate of 50 million deaths published by Johnson and Mueller implies that the Spanish flu killed 2.7% of the world population. And if it was in fact higher – 100 million as these authors suggest – then the global death rate would have been 5.4%.10 The world population was growing by around 13 million every year in this period which suggests that the period of the Spanish flu was likely the last time in history when the world population was declining.11 ### Other large influenza pandemics The Spanish flu pandemic was the largest, but not the only large recent influenza pandemic. Two decades before the Spanish flu the Russian flu pandemic (1889-1894) is believed to have killed 1 million people.12 Estimates for the death toll of the “Asian Flu” (1957-1958) range from 1.7 to 2.7 million according to Spreeuwenberg et al. (2018).13 The same authors estimate that the “Hong Kong Flu” (1968-1969) killed between 2 and 3.8 million people.13 The “Russian Flu” pandemic of 1889-1890 is believed to be caused by an H3 pandemic virus.14 According to Spreeuwenberg et al. (2018) around 3.7 to 5.1 million people died worldwide.13 The “Swine flu” pandemic of 2009-2010 was caused by a new H1N1 pandemic virus. Several research groups have made estimates of the global death toll, which ranges from 130,000 to 1.87 million people worldwide.15 What becomes clear from this overview are two things: influenza pandemics are not rare, but the Spanish flu of 1918 was by far the most devastating influenza pandemic in recorded history. ## The impact of the Spanish flu on different age groups This last visualization here shows the life expectancy in England and Wales by age. The red line shows the life expectancy for a newborn, with the rainbow-colored lines above showing how long a person could expect to live once they had reached that given, older, age. The light green line, for example, represents the life expectancy for children who have reached age 10. It shows that life expectancy increased at all ages, which means that the often-heard assertion that life expectancy ‘only’ increased because child mortality declined is [not true](https://ourworldindata.org/life-expectancy#it-is-not-only-about-child-mortality-life-expectancy-by-age). With respect to the impact of the Spanish flu it is striking that the visualization shows that the pandemic had little impact on older people. While the life expectancy at birth and at young ages declined by more than ten years, the life expectancy of 60- and 70-year olds saw no change. This is at odds with what one would reasonably expect: older populations tend to be most vulnerable to influenza outbreaks and respiratory infections. If we look at mortality for both [lower respiratory infections (pneumonia)](https://ourworldindata.org/grapher/pneumonia-mortality-by-age?time=2017) and [upper respiratory infections](https://ourworldindata.org/grapher/death-rate-upper-respiratory-age) today, death rates are highest for those who are 70 years and older. This data tells us that young people [accounted for a large share](https://ourworldindata.org/age-structure) of the deaths, this made this pandemic especially devastating. Why were older people so resilient to the 1918 pandemic? The research literature suggests that this was the case because older people had lived through an earlier flu outbreak – the already discussed [‘Russian flu pandemic’](https://en.wikipedia.org/wiki/1889%E2%80%9390_flu_pandemic) of 1889–90 – which gave those who lived through it some immunity for the later outbreak of the Spanish flu.16 The earlier 1889-90 pandemic might have given the older population some immunity, but was a destructive event in itself. According to Smith 132,000 people died in England, Wales, and Ireland alone.17 ### How the Spanish flu differs from the Coronavirus outbreak in 2020 Writing in early March 2020 it is an obvious question to ask how the ongoing outbreak of Covid-19 compares. There are a number of important differences that should be considered. They are not the same disease and the virus causing these diseases are very different. The virus that causes Covid-19 is a coronavirus, not an influenza virus that caused the Spanish flu and the other influenza pandemics listed above. The age-specific mortality seems to be very different. As we’ve seen above, the Spanish flu in 1918 was especially dangerous to infants and younger people. The new coronavirus that causes Covid-19 appears to be most lethal to the elderly, based on early evidence in China.18 We’ve also seen above that during the Spanish flu many countries tried to suppress any information about the influenza outbreak. Today the sharing of data, research, and news is certainly not perfect, but very different and much more open than in the past. But it is true that the world today is much better connected. In 1918 it was railroads and steamships that connected the world. Today planes can carry people and viruses to many corners of the world in a very short time. Differences in health systems and infrastructure also matter. The Spanish flu hit the world in the days before antibiotics were invented; and many deaths, perhaps most, were not caused by the influenza virus itself, but by secondary _bacterial_ infections. Morens et al (2008) found that during the Spanish flu “the majority of deaths … likely resulted directly from secondary bacterial pneumonia caused by common upper respiratory–tract bacteria.”19 And not just health systems were different, but also the health and living conditions of the global population. The 1918 flu hit a world population of which a very large share [was extremely poor](https://ourworldindata.org/extreme-history-methods) – large shares of the population were undernourished, in most parts of the world the populations lived [in very poor health](https://ourworldindata.org/life-expectancy-globally), and overcrowding, poor sanitation and low hygiene standards were common. Additionally the populations in many parts of the world were weakened by a global war. Public resources were small and many countries had just spent [large shares](https://ourworldindata.org/military-spending#defence-spending-in-uk-over-the-very-long-run) of their resources on the war. While most of the world is [much richer and healthier now](https://ourworldindata.org/a-history-of-global-living-conditions-in-5-charts), the concern today too is that it is the poorest people that are going to be hit hardest by the Covid-19 outbreak.20 These differences suggest that one should be cautious in drawing lessons from the outbreak a century ago. But the Spanish flu reminds us just how large the impact of a pandemic can be, even in countries that had already been successful in improving population health. A new pathogen can cause terrible devastation and lead to the death of millions. For this reason the Spanish flu has been cited as a warning and as a motivation to prepare well for large pandemic outbreaks, which have been considered likely by many researchers.21 Worobey, M., Han, G.-Z., & Rambaut, A. (2014). Genesis and pathogenesis of the 1918 pandemic H1N1 influenza A virus. Proceedings of the National Academy of Sciences, 111(22), 8107–8112. [https://doi.org/10.1073/pnas.1324197111](https://doi.org/10.1073/pnas.1324197111) Barry, J. M. (2004). The site of origin of the 1918 influenza pandemic and its public health implications. Journal of Translational Medicine, 2(1), 3. [https://doi.org/10.1186/1479-5876-2-3](https://doi.org/10.1186/1479-5876-2-3) For the definitions of epidemic and pandemic see the CDC [here](https://www.cdc.gov/csels/dsepd/ss1978/lesson1/section11.html). Burnet F. M., Clark E. (1942) – Influenza: A Survey of the Last 50 Years in the Light of Modern Work on the Virus of Epidemic Influenza. London: Macmillan. Partly online on Google books. The mortality rate in some populations like Alaska and Samoa were said to be 90% and 25% respectively. See the following two publications: McLane, J. R. (2013). [Paradise locked: The 1918 influenza pandemic in American Samoa. Sites: a journal of social anthropology and cultural studies](https://sites.otago.ac.nz/Sites/article/view/215), 10(2), 30-51.[ ](https://sites.otago.ac.nz/Sites/article/view/215) Mamelund, S. E. (2017). Profiling a Pandemic. Who were the victims of the Spanish flu?{ref} While peak mortality was reached in 1918 the pandemic did not wane until two years later in late 1920. Paget et al (2019) suggest an “average of 389 000 (uncertainty range 294 000-518 000) respiratory deaths were associated with influenza globally each year”. John Paget, Peter Spreeuwenberg, Vivek Charu Robert J Taylor, A Danielle Iuliano, Joseph Bresee, Lone Simonsen, Cecile Viboud,3 and for the Global Seasonal Influenza-associated Mortality Collaborator Network and GLaMOR Collaborating Teams (2019) – Global mortality associated with seasonal influenza epidemics: New burden estimates and predictors from the GLaMOR Project. In J Glob Health. 2019 Dec; 9(2): 020421. Published online 2019 Oct 22. doi: 10.7189/jogh.09.020421 PMCID: PMC6815659 PMID: 31673337 Online [here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6815659/). This is (389,000/7,500,000,000)*100=0.0052% From the paper: _Further research has seen the consistent upward revision of the estimated global mortality of the pandemic, which a 1920s calculation put in the vicinity of 21.5 million. A 1991 paper revised the mortality as being in the range 24.7-39.3 million. This paper suggests that it was of the order of 50 million. However, it must be acknowledged that even this vast figure may be substantially lower than the real toll, perhaps as much as 100 percent understated._ Johnson, N.P. and Mueller, J. (2002) – Updating the accounts: global mortality of the 1918-1920 “Spanish"" influenza pandemic. In Bulletin of the History of Medicine, 76(1), pp.105-115. Online [here](https://www.jstor.org/stable/44446153?read-now=1&seq=1#page_scan_tab_contents). The paper includes detailed breakdowns of mortality estimates by world region and country. Patterson and Pyle (1991) wrote 'we believe that approximately 30 million is the best estimate for the terrible demographic toll of the influenza pandemic of 1918' and published a range from 24.7-39.3 million deaths. Patterson, K.D. and Pyle, G.F. (1991) – The geography and mortality of the 1918 influenza pandemic. Bulletin of the History of Medicine, 65(1), p.4. Online [here](https://web.archive.org/web/20171022101640/https://pida.nihlibrary.com/sites/pida.nihlibrary.com/files/pdf_files/1991_K.David%20Patterson_The%20geography%20and%20mortality%20of%20the%201918%20influenza%20pandemic..pdf). P. Spreeuwenberg; et al. (1 December 2018). ""Reassessing the Global Mortality Burden of the 1918 Influenza Pandemic"". American Journal of Epidemiology. 187 (12): 2561–2567. doi:10.1093/aje/kwy191. PMID 30202996. Online [here](https://academic.oup.com/aje/article/187/12/2561/5092383). The calculation is (17,400,000/1,832,196,157)*100=0.95 50,000,000 deaths / 1,832,196,157 people = 0.02729 And with a death count twice is high: 0.05458. In available historical reconstructions (like [this one](https://ourworldindata.org/grapher/population?country=OWID_WRL)) this decline is not shown. The reason for this is that precise annual counts of the world population are not available for the past. Instead historians try to reconstruct the population figures for 5-year or 10-year intervals and the annual estimates are interpolations between these estimates. In other words, if we had precise annual counts they would likely show a decline of the world population in 1918. Nickol, M.E., Kindrachuk, J. (2019) – A year of terror and a century of reflection: perspectives on the great influenza pandemic of 1918–1919. BMC Infect Dis 19, 117 (2019). [https://doi.org/10.1186/s12879-019-3750-8](https://doi.org/10.1186/s12879-019-3750-8) According to Smith (1995) 132,000 died in England, Wales, and Ireland alone. Smith F. B. (1995) – The Russian influenza in the United Kingdom, 1889-1894. Soc. Hist. Med. 8 55–73. Online [here](https://academic.oup.com/shm/article-lookup/doi/10.1093/shm/8.1.55). Spreeuwenberg, P., Kroneman, M., & Paget, J. (2018). Reassessing the Global Mortality Burden of the 1918 Influenza Pandemic. American Journal of Epidemiology, 187(12), 2561–2567. [https://doi.org/10.1093/aje/kwy191](https://doi.org/10.1093/aje/kwy191) Worobey, M., Han, G.-Z., & Rambaut, A. (2014). Genesis and pathogenesis of the 1918 pandemic H1N1 influenza A virus. Proceedings of the National Academy of Sciences, 111(22), 8107–8112. [https://doi.org/10.1073/pnas.1324197111](https://doi.org/10.1073/pnas.1324197111) Dawood, F. S., Iuliano, A. D., Reed, C., Meltzer, M. I., Shay, D. K., Cheng, P.-Y., Bandaranayake, D., Breiman, R. F., Brooks, W. A., Buchy, P., Feikin, D. R., Fowler, K. B., Gordon, A., Hien, N. T., Horby, P., Huang, Q. S., Katz, M. A., Krishnan, A., Lal, R., … Widdowson, M.-A. (2012). Estimated global mortality associated with the first 12 months of 2009 pandemic influenza A H1N1 virus circulation: A modelling study. The Lancet Infectious Diseases, 12(9), 687–695. [https://doi.org/10.1016/S1473-3099(12)70121-4](https://doi.org/10.1016/S1473-3099(12)70121-4) Simonsen, L., Spreeuwenberg, P., Lustig, R., Taylor, R. J., Fleming, D. M., Kroneman, M., Van Kerkhove, M. D., Mounts, A. W., Paget, W. J., & the GLaMOR Collaborating Teams. (2013). Global Mortality Estimates for the 2009 Influenza Pandemic from the GLaMOR Project: A Modeling Study. PLoS Medicine, 10(11), e1001558. [https://doi.org/10.1371/journal.pmed.1001558](https://doi.org/10.1371/journal.pmed.1001558) Spreeuwenberg, P., Kroneman, M., & Paget, J. (2018). Reassessing the Global Mortality Burden of the 1918 Influenza Pandemic. American Journal of Epidemiology, 187(12), 2561–2567. [https://doi.org/10.1093/aje/kwy191](https://doi.org/10.1093/aje/kwy191) Gagnon et al. (2013) – Age-Specific Mortality During the 1918 Influenza Pandemic: Unravelling the Mystery of High Young Adult Mortality.PLoS One. 2013; 8(8): e69586. Published online 2013 Aug 5. doi: 10.1371/journal.pone.0069586. Online [here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3734171/). The Russian flu pandemic was a devastating event in itself. Smith (1995) estimates that the Russian flu killed 132,000 in England, Wales, and Ireland. Smith F. B. (1995) – The Russian influenza in the United Kingdom, 1889-1894. Soc. Hist. Med. 8 55–73. Online [here](https://academic.oup.com/shm/article-lookup/doi/10.1093/shm/8.1.55). Smith F. B. (1995) – The Russian influenza in the United Kingdom, 1889-1894. Soc. Hist. Med. 8 55–73. Online [here](https://academic.oup.com/shm/article-lookup/doi/10.1093/shm/8.1.55). Zhonghua Liu Xing Bing Xue Za Zhi (2020) – The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Feb 17;41(2):145-151. doi: 10.3760/cma.j.issn.0254-6450.2020.02.003. Online [here](https://www.ncbi.nlm.nih.gov/pubmed/32064853?fbclid=IwAR3JCxH50VTfg3Q_02YTLdz2Tk7yBTmt-5oCxE4KlBe0evh7ByK3HPVU-pU). Morens D. M., Taubenberger J. K., Fauci A. S. (2008) – Predominant role of bacterial pneumonia as a cause of death in pandemic influenza: implications for pandemic influenza preparedness. J. Infect. Dis. 198 962–970. 10.1086/591708. Online [here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2599911/). Gilbert, Marius, Giulia Pullano, Francesco Pinotti, Eugenio Valdano, Chiara Poletto, Pierre-Yves Boëlle, Eric D’Ortenzio, et al. (2020) – “Preparedness and Vulnerability of African Countries against Importations of COVID-19: A Modelling Study.” _The Lancet_ (February 20, 2020).[ https://doi.org/10.1016/S0140-6736(20)30411-6](https://doi.org/10.1016/S0140-6736(20)30411-6). See also Alyssa S. Parpia, Martial L. Ndeffo-Mbah, Natasha S. Wenzel, and Alison P. Galvani (2016) – Effects of Response to 2014–2015 Ebola Outbreak on Deaths from Malaria, HIV/AIDS, and Tuberculosis, West Africa. In Emerg Infect Dis. 2016 Mar; 22(3): 433–441. doi: 10.3201/eid2203.150977 PMCID: PMC4766886 PMID: 26886846 Online [here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4766886/?fbclid=IwAR26EF-QA82ds5Jz81NNrIHb77S3n8L9D5YnL3GlLSRmQ3ms051ZCVx0zt8#!po=16.6667). See for example: Pandemic influenza preparedness and response – WHO guidance document. Published in 2009 by the WHO. Online [here](https://apps.who.int/iris/bitstream/handle/10665/44123/9789241547680_eng.pdf).Roman Duda (2016) – Problem profile: Biorisk reduction. Published by 80,000 hours. Online [here](https://80000hours.org/problem-profiles/biosecurity/).",The Spanish flu: The global impact of the largest influenza pandemic in history 1qQXPsbbG_dMycpoxn5GZ61DM18zIjzwK8doTRuDS2Vg,japans-cherry-trees-have-been-blossoming-earlier-due-to-warmer-spring-temperatures,data-insight,"{""body"": [{""size"": ""narrow"", ""type"": ""image"", ""filename"": ""20240304-cherry-blossoms-final-source-name-fixed.png"", ""parseErrors"": [], ""smallFilename"": ""20240304-cherry-blossoms-final-source-name-fixed.png""}, {""type"": ""text"", ""value"": [{""text"": ""The peak flowering of cherry trees in Kyoto, Japan, has been recorded since the ninth century. 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[Explore this data](https://ourworldindata.org/grapher/date-of-the-peak-cherry-tree-blossom-in-kyoto) →",Japan’s cherry trees have been blossoming earlier due to warmer spring temperatures 1qEoTctp8U_N1qfFFiSx66nsiWzWz8IhE1J3cghKf8dY,sdgs/no-poverty,article,"{""toc"": [{""slug"": ""target-1-1-eradicate-extreme-poverty"", ""text"": ""Eradicate extreme poverty"", ""title"": ""Eradicate extreme poverty"", ""supertitle"": ""Target 1.1"", ""isSubheading"": false}, {""slug"": ""sdg-indicator-1-1-1-share-below-the-international-poverty-line"", ""text"": ""Share below the international poverty line"", ""title"": ""Share below the international poverty line"", ""supertitle"": ""SDG Indicator 1.1.1"", ""isSubheading"": true}, {""slug"": ""target-1-2-reduce-poverty-by-at-least-50"", ""text"": ""Reduce poverty by at least 50%"", ""title"": ""Reduce poverty by at least 50%"", ""supertitle"": ""Target 1.2"", ""isSubheading"": false}, {""slug"": 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topic page on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/poverty"", ""children"": [{""text"": ""poverty"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/share-of-population-living-in-poverty-by-national-poverty-lines"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Share in multidimensional poverty according to national definitions"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""supertitle"": [{""text"": ""SDG Indicator 1.2.2"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Definition of the SDG indicator"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "":"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" Indicator 1.2.2 is the “proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definitions” in the"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""url"": ""https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf"", ""children"": [{""text"": ""UN SDG framework"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This indicator is measured via related multidimensional poverty measures constructed according to national definitions. Multidimensional poverty refers to being deprived on a range of standard indicators related to health, education, and living standards."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Target: "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""By 2030, “reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definitions."", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""”"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Related data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "":"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" Data on multidimensional poverty, measured as the Multidimensional Poverty Index (MPI), can be found in this "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-multi-poverty"", ""children"": [{""text"": ""chart"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". This data has much better coverage across countries and time and is measured consistently, making it comparable between countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/share-people-multidimensional-poverty-national-definitions"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Implement social protection systems"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""supertitle"": [{""text"": ""Target 1.3"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Population covered by social protection floors/systems"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""supertitle"": [{""text"": ""SDG Indicator 1.3.1"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Definition of the SDG indicator"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "":"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" Indicator 1.3.1 is the “proportion of the population covered by social protection floors/systems, by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work-injury victims and the poor and the vulnerable” in the"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""url"": ""https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf"", ""children"": [{""text"": ""UN SDG framework"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This indicator measures the share of the population effectively covered by a social protection system. Such systems include child and maternity benefits, support for persons without jobs, persons with disabilities, victims of work injuries, and older persons."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In our topic page on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/government-spending"", ""children"": [{""text"": ""government spending"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", you can find additional data, including some of the breakdowns mentioned in the definition of indicator 1.3.1."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Target:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" The SDG target is to “implement nationally appropriate social protection systems and measures for all, including floors, and by 2030 achieve substantial coverage of the poor and the vulnerable.”"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""additional-charts"", ""items"": [[{""url"": ""https://ourworldindata.org/grapher/adequacy-of-social-insurance-programs"", ""children"": [{""text"": ""Adequacy of social insurance systems"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], [{""url"": ""https://ourworldindata.org/grapher/adequacy-of-unemployment-benefits"", ""children"": [{""text"": ""Adequacy of unemployment benefits"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], [{""url"": ""https://ourworldindata.org/grapher/adequacy-of-social-safety-net-programs"", ""children"": [{""text"": ""Adequacy of social safety net programs"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}]], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/share-covered-by-one-social-protection-benefit"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Equal rights to ownership, basic services, technology, and economic resources"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""supertitle"": [{""text"": ""Target 1.4"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Access to basic services"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""supertitle"": [{""text"": ""SDG Indicator 1.4.1"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Definition of the SDG indicator"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "":"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" Indicator 1.4.1 is the “proportion of population living in households with access to basic services” in the"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""url"": ""https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf"", ""children"": [{""text"": ""UN SDG framework"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The UN "", ""spanType"": ""span-simple-text""}, {""url"": ""https://unstats.un.org/sdgs/metadata/files/Metadata-01-04-01.pdf"", ""children"": [{""text"": ""defines"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" basic services as “public service provision systems that meet human basic needs” and accounts for access to 9 components: drinking water, sanitation, hygiene facilities, clean fuels and technology, mobility, waste collection, health care, education, and information services. These components also appear elsewhere in the SDG framework as indicators."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since internationally comparable data on this indicator is currently unavailable, we show here the share of the world population with access to four essential services: improved drinking water, sanitation, electricity, and clean cooking fuels. You can view the data for different countries or regions using the “Change country” button at the top of the chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Target: "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""By 2030, “ensure that all men and women, in particular the poor and the vulnerable, have access to basic services.”"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This sets a target of universal access to basic services for all households."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""More research:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" Further data and research can be found on the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Our World in Data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" topic pages on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/clean-water-sanitation"", ""children"": [{""text"": ""clean water and sanitation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/energy-production-and-changing-energy-sources"", ""children"": [{""text"": ""energy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/indoor-air-pollution"", ""children"": [{""text"": ""indoor air pollution"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""additional-charts"", ""items"": [[{""url"": ""https://ourworldindata.org/grapher/share-of-the-population-with-access-to-electricity"", ""children"": [{""text"": ""Access to electricity"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], [{""url"": ""https://ourworldindata.org/grapher/access-to-clean-fuels-and-technologies-for-cooking"", ""children"": [{""text"": ""Access to clean cooking fuels"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], [{""url"": ""https://ourworldindata.org/grapher/share-using-safely-managed-sanitation"", ""children"": [{""text"": ""Access to safe sanitation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], [{""url"": ""https://ourworldindata.org/grapher/proportion-using-safely-managed-drinking-water"", ""children"": [{""text"": ""Access to safe drinking water"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}]], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/access-to-basic-services"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Secure tenure rights to land"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""supertitle"": [{""text"": ""SDG Indicator 1.4.2"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Definition of the SDG indicator"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "":"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" Indicator 1.4.2 is the “proportion of the total adult population with secure tenure rights to land, (a) with legally recognized documentation, and (b) who perceive their rights to land as secure, by sex and type of tenure” in the"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""url"": ""https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf"", ""children"": [{""text"": ""UN SDG framework"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The interactive visualizations show data for this indicator. The first chart shows data on indicator 1.4.2(a) for the share of adults with legal documentation of their rights to land, and the second chart shows data on indicator 1.4.2(b) for the share of individuals who perceive their rights to land as secure."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Target: "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": ""By 2030, “ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property.”"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""type"": ""chart-story"", ""items"": [{""chart"": {""url"": ""https://ourworldindata.org/grapher/legally-recognized-rights-to-land"", ""type"": ""chart"", ""parseErrors"": []}, ""narrative"": {""type"": ""text"", ""value"": [{""text"": ""Legal rights to land"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, ""technical"": []}, {""chart"": {""url"": ""https://ourworldindata.org/grapher/share-of-adults-who-perceive-their-rights-to-land-as-secure"", ""type"": ""chart"", ""parseErrors"": []}, ""narrative"": {""type"": ""text"", ""value"": [{""text"": ""Perceived rights to land"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, ""technical"": []}], ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Build resilience to environmental, economic, and social disasters"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""supertitle"": [{""text"": ""Target 1.5"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Deaths and affected persons from natural disasters"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""supertitle"": [{""text"": ""SDG Indicator 1.5.1"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Definition of the SDG indicators"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "":"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" Indicators 1.5.1 are the “number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population” in the"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""url"": ""https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf"", ""children"": [{""text"": ""UN SDG framework"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the interactive visualizations, we show a component of this indicator in the first chart: the rate of deaths and missing persons from natural disasters, measured as the number of deaths and missing persons per 100,000 population per year. The other charts in the series include a range of metrics relevant to indicator 1.5.1."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Target:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" “By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social, and environmental shocks and disasters.”"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""More research:"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" Further data and research can be found on the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Our World in Data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" topic page on "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/natural-catastrophes"", ""children"": [{""text"": ""natural disasters"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""type"": ""chart-story"", ""items"": [{""chart"": {""url"": ""https://ourworldindata.org/grapher/deaths-and-missing-persons-due-to-natural-disasters"", ""type"": ""chart"", ""parseErrors"": []}, ""narrative"": {""type"": ""text"", ""value"": [{""text"": ""Deaths and missing persons due to natural disasters"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, ""technical"": []}, {""chart"": {""url"": ""https://ourworldindata.org/grapher/total-affected-by-natural-disasters"", ""type"": ""chart"", ""parseErrors"": []}, ""narrative"": {""type"": ""text"", ""value"": [{""text"": ""Number of people affected by disasters"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, ""technical"": []}, {""chart"": {""url"": ""https://ourworldindata.org/explorers/natural-disasters?tab=map&facet=none&hideControls=true&Disaster+Type=All+disasters&Impact=Affected&Timespan=Annual&Per+capita=true&country=~OWID_WRL&hideControls=true"", ""type"": ""chart"", ""parseErrors"": []}, ""narrative"": {""type"": ""text"", ""value"": [{""text"": ""Death rate from disasters"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, ""technical"": []}, {""chart"": {""url"": ""https://ourworldindata.org/explorers/natural-disasters?facet=none&Disaster+Type=All+disasters&Impact=Deaths&Timespan=Annual&Per+capita=false&country=~OWID_WRL&hideControls=true"", ""type"": ""chart"", ""parseErrors"": []}, ""narrative"": {""type"": ""text"", ""value"": [{""text"": ""Deaths from disasters"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, ""technical"": []}, {""chart"": {""url"": ""https://ourworldindata.org/grapher/number-injured-from-disasters"", ""type"": ""chart"", ""parseErrors"": []}, ""narrative"": {""type"": ""text"", ""value"": [{""text"": ""Number of people injured by disasters"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, ""technical"": []}, {""chart"": {""url"": ""https://ourworldindata.org/grapher/number-homeless-from-natural-disasters"", ""type"": ""chart"", ""parseErrors"": []}, ""narrative"": {""type"": ""text"", ""value"": [{""text"": ""Number of people left homeless from disasters"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, ""technical"": []}, {""chart"": {""url"": ""https://ourworldindata.org/grapher/internally-displaced-persons-from-disasters"", ""type"": ""chart"", ""parseErrors"": []}, ""narrative"": {""type"": ""text"", ""value"": [{""text"": ""Internally displaced persons from disasters"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, ""technical"": []}], ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Direct economic loss from natural disasters"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""supertitle"": [{""text"": ""SDG Indicator 1.5.2"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Definition of the SDG indicator"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""children"": [{""text"": "":"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" Indicator 1.5.2 is the “direct economic loss attributed to disasters in relation to global gross domestic product (GDP)” in the"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""url"": ""https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf"", ""children"": [{""text"": ""UN 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Longer-term trends, research, and additional data on poverty can be found on _Our World in Data,_ particularly our topic page on [poverty](https://ourworldindata.org/poverty). The [UN has defined](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf) 7 _targets_ and 13 _indicators_ for SDG 1. Targets specify the goals, and indicators represent the metrics by which the world tracks whether these targets are achieved. Below we quote the original text of all targets and show the data on the agreed indicators. ## Target 1.1 Eradicate extreme poverty ### SDG Indicator 1.1.1 Share below the international poverty line **Definition of the SDG indicator:** Indicator 1.1.1 is the “proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). The international poverty line is defined as living on $2.15 per day ([updated](https://ourworldindata.org/from-1-90-to-2-15-a-day-the-updated-international-poverty-line) from the previous poverty line of $1.90 in 2015). This poverty line is measured in international dollars, a hypothetical currency that adjusts for price differences between countries (purchasing power parity). It is measured in prices of 2017 to adjust for price changes over time (inflation). Data for this indicator on the proportion of the population below the international poverty line is shown in the interactive visualization. Breakdowns by sex, age, employment status, and geographical location are not available for all countries, but our topic page on [poverty](https://ourworldindata.org/poverty) includes some relevant measures in this context. **Target: **By 2030, “eradicate extreme poverty for all people, everywhere_”_. This is defined by the UN based on the international poverty line.1 **More research:** Further data and research can be found on the _Our World in Data_ topic page on [poverty](https://ourworldindata.org/poverty). * [Share of population living in extreme poverty (historical estimates)](https://ourworldindata.org/grapher/share-of-population-living-in-extreme-poverty?country=~OWID_WRL) * [Population living in extreme poverty by region](https://ourworldindata.org/grapher/total-population-living-in-extreme-poverty-by-world-region) * [Share of population living in multidimensional poverty](https://ourworldindata.org/grapher/share-multi-poverty) ## Target 1.2 Reduce poverty by at least 50% ### SDG Indicator 1.2.1 Share below the national poverty line **Definition of the SDG indicator****:** Indicator 1.2.1 is the “proportion of the population living below the national poverty line” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). National poverty lines [differ by country](https://ourworldindata.org/grapher/national-poverty-line-vs-gdp-per-capita) depending on country circumstances, living standards, and cost of living. Data for this indicator on the share of a country's population which lives below each country's specific national poverty line is shown in the interactive visualization. **Target:** By 2030, “reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definitions._”_ **More research:** Further data and research can be found at the _Our World in Data_ topic page on [poverty](https://ourworldindata.org/poverty). ### SDG Indicator 1.2.2 Share in multidimensional poverty according to national definitions **Definition of the SDG indicator****:** Indicator 1.2.2 is the “proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). This indicator is measured via related measures of multidimensional poverty, all of which are constructed according to national definitions. Multidimensional poverty refers to being deprived in a range of standard indicators related to health, education, and living standards. **Target: **By 2030, “reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definitions._”_ **Related data****:** Data on multidimensional poverty, measured as the Multidimensional Poverty Index (MPI), can be found in this [chart](https://ourworldindata.org/grapher/share-multi-poverty). This data has much better coverage across countries and time, and is measured in a consistent way, making it comparable between countries. ## Target 1.3 Implement social protection systems ### SDG Indicator 1.3.1 Population covered by social protection floors/systems **Definition of the SDG indicator****:** Indicator 1.3.1 is the “proportion of population covered by social protection floors/systems, by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work-injury victims and the poor and the vulnerable” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). This indicator is measured as the share of the population effectively covered by a social protection system. Such systems include child and maternity benefits, support for persons without a job, persons with disabilities, victims of work injuries, and older persons. In our topic page on [government spending](https://ourworldindata.org/government-spending), you can find additional data, including some of the breakdowns mentioned in the definition of indicator 1.3.1. **Target:** The SDG target is to “implement nationally appropriate social protection systems and measures for all, including floors, and by 2030 achieve substantial coverage of the poor and the vulnerable.” * [Adequacy of social insurance systems](https://ourworldindata.org/grapher/adequacy-of-social-insurance-programs) * [Adequacy of unemployment benefits](https://ourworldindata.org/grapher/adequacy-of-unemployment-benefits) * [Adequacy of social safety net programs](https://ourworldindata.org/grapher/adequacy-of-social-safety-net-programs) ## Target 1.4 Equal rights to ownership, basic services, technology, and economic resources ### SDG Indicator 1.4.1 Access to basic services **Definition of the SDG indicator****:** Indicator 1.4.1 is the “proportion of population living in households with access to basic services” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). The UN [defines](https://unstats.un.org/sdgs/metadata/files/Metadata-01-04-01.pdf) basic services as “public service provision systems that meet human basic needs” and defines this indicator in terms of access to 9 components: drinking water, sanitation, hygiene facilities, clean fuels and technology, mobility, waste collection, health care, education, and information services. These components also appear elsewhere in the SDG framework as indicators. Since internationally-comparable data on this indicator is currently unavailable, we show here the share of the world population with access to four basic services: improved drinking water, sanitation, electricity, and clean cooking fuels. You can view the data for different countries or regions using the “Change country” button at the top of the chart. **Target: **By 2030, “ensure that all men and women, in particular the poor and the vulnerable, have access to basic services.”2 This sets a target of universal access to basic services for all households. **More research:** Further data and research can be found on the _Our World in Data_ topic pages on [clean water and sanitation](https://ourworldindata.org/clean-water-sanitation), [energy](https://ourworldindata.org/energy-production-and-changing-energy-sources), and [indoor air pollution](https://ourworldindata.org/indoor-air-pollution). * [Access to electricity](https://ourworldindata.org/grapher/share-of-the-population-with-access-to-electricity) * [Access to clean cooking fuels](https://ourworldindata.org/grapher/access-to-clean-fuels-and-technologies-for-cooking) * [Access to safe sanitation](https://ourworldindata.org/grapher/share-using-safely-managed-sanitation) * [Access to safe drinking water](https://ourworldindata.org/grapher/proportion-using-safely-managed-drinking-water) ### SDG Indicator 1.4.2 Secure tenure rights to land **Definition of the SDG indicator****:** Indicator 1.4.2 is the “proportion of total adult population with secure tenure rights to land, (a) with legally recognized documentation, and (b) who perceive their rights to land as secure, by sex and type of tenure” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). Data for this indicator is shown in the interactive visualizations, with the first chart showing data on indicator 1.4.2(a) for the share of adults with legal documentation of their rights to land, and the second chart showing data on indicator 1.4.2(b) for the share of individuals who perceive their rights to land as secure. **Target: **By 2030, “ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property.”2 undefined ## Target 1.5 Build resilience to environmental, economic, and social disasters ### SDG Indicator 1.5.1 Deaths and affected persons from natural disasters **Definition of the SDG indicators****:** Indicators 1.5.1 are the “number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). In the interactive visualizations, we show in the first chart a component of this indicator: the rate of deaths and missing persons from natural disasters, measured as the number of deaths and missing persons per 100,000 population per year. The other charts in the series include a range of metrics relevant to indicator 1.5.1. **Target:** “By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters” **More research:** Further data and research can be found on the _Our World in Data_ topic page on [natural disasters](https://ourworldindata.org/natural-catastrophes). undefined ### SDG Indicator 1.5.2 Direct economic loss from natural disasters **Definition of the SDG indicator****:** Indicator 1.5.2 is the “direct economic loss attributed to disasters in relation to global gross domestic product (GDP)”in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). This indicator measures the ratio of direct economic loss from a disaster to gross domestic product, where direct economic loss is defined as the monetary value of totally or partially destroyed physical assets in the affected area. This includes losses in agriculture, all other productive assets, housing, critical infrastructure, and cultural heritage. **Target:** “By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters” **More research:** Further data and research can be found on the _Our World in Data_ topic page on [natural disasters](https://ourworldindata.org/natural-catastrophes). * [Absolute economic losses from disasters by country](https://ourworldindata.org/grapher/direct-disaster-economic-loss?tab=chart) * [Global weather-related disaster losses as a share of GDP](https://ourworldindata.org/grapher/weather-losses-share-gdp) ### SDG Indicator 1.5.3 Disaster risk reduction strategies **Definition of the SDG indicator****:** Indicator 1.5.3 is the “number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015–2030” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). The Sendai Framework for Disaster Risk Reduction 2015-2030 is an international agreement that aims to strengthen disaster preparedness to reduce risk and losses from disasters. Although the indicator definition is framed in terms of the number of countries adopting national disaster risk reduction strategies in line with the Sendai Framework, the United Nations tracks this measure in terms of country levels of implementation. The interactive visualization shows data for this indicator in terms of levels of country implementation, on a scale from 0 to 1, based on an average score from 10 scored sub-indicators that collectively reflect progress towards implementation of the Sendai Framework. **Target:** “By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters” **More research:** Further data and research can be found on the _Our World in Data_ topic page on [natural disasters](https://ourworldindata.org/natural-catastrophes). * [Disaster risk reduction score](https://ourworldindata.org/grapher/disaster-risk-reduction-progress) ### SDG Indicator 1.5.4 Local disaster risk reduction **Definition of the SDG indicator****:** Indicator 1.5.4 is the “proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). In this context, “local governments” refers to sub-national administrative bodies responsible for developing disaster risk reduction strategies. Data for this indicator is shown in the interactive visualization. **Target: **By 2030, “build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other disasters.”3 **More research:** Further data and research can be found on the _Our World in Data_ topic page on [natural disasters](https://ourworldindata.org/natural-catastrophes). ## Target 1.a Mobilization of resources to end poverty ### SDG Indicator 1.a.1 Development assistance for poverty reduction **Definition of the SDG indicator****:** Indicator 1.a.1 is the “total official development assistance grants from all donors that focus on poverty reduction as a share of the recipient country’s gross national income” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). Official development assistance refers to flows to countries and territories on the Organization for Economic Co-operation and Development’s Development Assistance Committee (DAC) and to multilateral institutions which meet a [set of criteria](http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm) related to the source of the funding, the purpose of the transaction, and the concessional nature of the funding. This indicator is measured differently for donor and recipient countries, and data is accordingly shown separately for donor and recipient countries in the interactive visualizations. For recipient countries, this is shown as official development assistance grants focused on poverty reduction from all donors as a share of the recipient country’s gross national income. For donor countries, this is shown as bilateral official development assistance grants focused on poverty reduction as a share of a donor country’s gross national income. **Target:** By 2030, “ensure significant mobilization of resources from a variety of sources to implement programmes and policies to end poverty in all its dimensions.”4 undefined ### SDG Indicator 1.a.2 Government spending on essential services **Definition of the SDG indicator:** Indicator 1.a.2 is the “proportion of total government spending on essential services (education, health and social protection)” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). Since internationally comparable data on all components of this indicator is not currently available, data on the percentage of government spending on health and education are shown in the two interactive visualizations. **Target:** By 2030, “ensure significant mobilization of resources to implement programmes and policies to end poverty in all its dimensions.”4 There is no defined target for this indicator. **More research:** Further data and research can be found at the _Our World in Data_ topic pages on [financing healthcare](https://ourworldindata.org/financing-healthcare), [financing education](https://ourworldindata.org/financing-education), and [public spending](https://ourworldindata.org/public-spending). * [Social spending](https://ourworldindata.org/grapher/social-spending-oecd-longrun) undefined ## Target 1.b Policy frameworks for poverty eradication ### SDG Indicator 1.b.1 Pro-poor public spending **Definition of the SDG indicator:** Indicator 1.b.1 is “pro-poor public social spending” in the_ _[UN SDG framework](https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202023%20refinement_Eng.pdf). This indicator measures spending by country governments to benefit the poor in terms of health, education, and direct transfers. Data is not currently available for most countries and is not reported here. **Target:** “Create sound policy frameworks to support accelerated investment in poverty eradication actions.”5 undefined Full text: “Ensure significant mobilization of resources from a variety of sources, including through enhanced development cooperation, in order to provide adequate and predictable means for developing countries, in particular least developed countries, to implement programmes and policies to end poverty in all its dimensions” Full text: ”Create sound policy frameworks at the national, regional and international levels, based on pro-poor and gender-sensitive development strategies, to support accelerated investment in poverty eradication actions” Full text: “By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social, and environmental shocks and disasters.” The full text of the target reads: “By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a day”. However, the poverty line has since been updated to $2.15 a day, and the UN tracks this measure accordingly. Full text: ”By 2030, ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property, inheritance, natural resources, appropriate new technology and financial services, including microfinance.”",End poverty in all its forms everywhere 1q1sbAEqbeOgit1Nzc_mEoHLqEzXs5U4ONgHgI6YQN7k,the-history-of-global-economic-inequality,article,"{""toc"": [{""slug"": ""global-divergence-followed-by-convergence"", ""text"": ""Global divergence followed by convergence"", ""title"": ""Global divergence followed by convergence"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""global-income-inequality-increased-for-two-centuries-and-is-now-falling"", ""text"": ""Global income inequality increased for two centuries and is now falling"", ""title"": ""Global income inequality increased for two centuries and is now falling"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""a-look-into-the-future-of-global-inequality"", ""text"": ""A look into the future of global inequality"", ""title"": ""A look into the future of global inequality"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-global-inequality-has-changed-from-2003-to-2013"", ""text"": ""How global inequality has changed from 2003 to 2013"", ""title"": ""How global inequality has changed from 2003 to 2013"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""global-income-inequality-is-very-high-and-will-likely-stay-high-for-a-long-time"", ""text"": ""Global income inequality is very high and will likely stay high for a long time"", ""title"": ""Global income inequality is very high and will likely stay high for a long time"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-long-does-it-take-for-incomes-to-grow-from-480-int-to-14-500-int"", ""text"": ""How long does it take for incomes to grow from 480 int-$ to 14,500 int-$?"", ""title"": ""How long does it take for incomes to grow from 480 int-$ to 14,500 int-$?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""inequality-within-countries-and-inequality-between-countries"", ""text"": ""Inequality within countries and inequality between countries"", ""title"": ""Inequality within countries and inequality between countries"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""text"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""This article was originally published in 2017. The data shown and discussed in the text are therefore not always the latest estimates. For more up-to-date data, see our Data Explorers on "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""children"": [{""url"": ""https://ourworldindata.org/poverty#explore-data-poverty"", ""children"": [{""text"": ""Poverty"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""children"": [{""text"": "" and "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""children"": [{""url"": ""https://ourworldindata.org/economic-inequality#explore-data-poverty"", ""children"": [{""text"": ""Inequality"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""children"": [{""text"": ""."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The inequality in people’s living conditions today is extremely large. The panel of charts below shows how large these differences are, and how the inequality in 12 important measures of living standards maps onto the economic inequality in the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What is most important for how healthy, wealthy, and educated you are is not who you are, but where you are. This was the point I made in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/global-economic-inequality-introduction"", ""children"": [{""text"": ""another article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". A person’s knowledge, skills, and how hard they work all matter for whether they are poor or not – but all these personal factors together matter less than the one factor that is entirely outside of a person’s control: whether they happen to be born into a large, productive economy or not."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""How did we get here? How did the world become so unequal, and what can we expect for the future?"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""Correlates-of-GDP_How-is-life-at-different-levels-of-income.png"", ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Global divergence followed by convergence"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows estimates of the distribution of annual income among all world citizens over the last two centuries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To make incomes comparable across countries and time, daily incomes are measured in international-$ — a hypothetical currency that would buy a comparable amount of goods and services that a U.S. dollar would buy in the United States in 2011 (for a more detailed explanation, see "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/what-are-PPPs"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "")."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The distribution of incomes is shown at 3 points in time:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""By 1800, few countries had achieved economic growth. The chart shows that most of the world lived in poverty with an income similar to today's poorest countries. At the beginning of the 19th century, the vast majority—"", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/poverty?insight=after-200-years-of-progress-the-fight-against-global-poverty-is-just-beginning#introduction"", ""children"": [{""text"": ""roughly 80%"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""—of the world lived in material conditions that we would refer to as extreme poverty today."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 1975, 175 years later, the world had changed—it had become very unequal. The world income distribution was 'bimodal', with the two-humped shape of a camel: one hump below the international poverty line and a second hump at considerably higher incomes. The world had divided into a poor, developing world and a developed world more than 10-times richer."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Over the following 4 decades, the world income distribution has again changed dramatically. There has been a convergence in incomes: in many poorer countries, especially in South-East Asia, incomes have grown faster than in rich countries. While enormous income differences remain, the world can no longer be neatly divided into 'developed' and 'developing' countries. We have moved from a two-hump to a one-hump world. And at the same time, the distribution has also shifted to the right—the incomes of many of the world's poorest citizens have increased, and extreme poverty has "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/history-of-poverty-has-just-begun"", ""children"": [{""text"": ""fallen faster than ever before"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in human history."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We have visualized a similar dataset from the OECD "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/roser/graphs/WorldIncomeDistribution1820to2000/WorldIncomeDistribution1820to2000.html"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Global inequality in 1800, 1975, and 2015"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Global-inequality-in-1800-1975-and-2015.png"", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Global income inequality increased for two centuries and is now falling"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This visualization shows the distribution of incomes between 1988 and 2011. The data was compiled by the economists Branko Milanovic and Christoph Lakner."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To see the change over time, select the years above the distribution."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The previous visualization, which showed the change from 1820 to the year 2015, is based on estimates of inflation-adjusted average incomes per country (GDP per capita) and a measure of income inequality within a country only. It gives us a rough idea of how the distribution of incomes changed, but it is not very detailed or precise. In contrast to this, the work by Branko Milanovic and Christoph Lakner is based on much more detailed household survey data. This data measures household income at each decile of the income distribution, and the two authors used this information to arrive at the global income distribution. The downside of this approach is that we can only go as far back in time as household surveys were conducted in many countries around the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The visualization shows the end of the long era in human history in which global inequality was increasing. Starting with industrialization in North-Western Europe, incomes in this part of the world started to increase while material prosperity in the rest of the world remained low. While some countries followed European industrialization – first Northern America, Oceania, and parts of South America and later Japan and East Asia – other countries in Asia and Africa remained poor. As a consequence of this, global inequality increased over a long period. Only in the period shown in this visualization did this change: with rapid growth in much of Asia and Latin America, the global distribution of incomes became less unequal. The incomes of the poorer half of the world population rose faster than the incomes of the richer half."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""type"": ""html"", ""value"": """", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Global Income Distribution 1988 to 2011"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" "", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""If you want to use this visualization for a presentation or teaching purposes, download a zip folder with an image file for every year and an animated .gif "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""children"": [{""url"": ""https://ourworldindata.org/app/uploads/2018/03/WorldIncomeDistribution1988to2011.zip"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""children"": [{""text"": ""."", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}], ""parseErrors"": []}], ""type"": ""callout"", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""A look into the future of global inequality"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This visualization shows how the global income distribution has changed over the decade up to 2013. Tomáš Hellebrandt and Paolo Mauro, the authors of the paper"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" from which this data is taken, confirm the finding that global inequality has declined but remains very high: the Gini coefficient of global inequality has declined from 68.7 to 64.9."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The visualizations above show the income distribution on a logarithmic x-axis. This chart, in contrast, plots incomes on a linear x-axis and thereby emphasizes how very high global inequality still is: The bulk of the world population lives on very low incomes, and the income distribution stretches out very far to the higher incomes at the right-hand side of the chart; incomes over 14,000 international-$ are cut off as they would make this chart with a linear x-axis unreadable."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A second positive global development shown in this chart is the rise of the global median income. In 2003 half of the world's population lived on less than 1,090 international-$ per year, and the other half lived on more than 1,090 international-$. This level of global median income has almost doubled over the last decade and was 2,010 international-$ in 2013."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Finally, the authors also dare to project what global inequality will look like in 2035. Assuming the growth rates shown in the insert in the top-right corner, the authors project global inequality to decline further and to reach a Gini of 61.3. At the same time, the incomes of the world's poorer half would continue to increase significantly, so that the global median income could again double and reach 4,000 international-$ in 2035."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If you are looking for a visualization of only the observed global income distribution in 2003 and 2013, you can find it "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/app/uploads/2013/11/2-World-Income-Distribution-2003-to-2035.png"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""The global income distribution in 2003, 2013, and the projection for 2035"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""4-World-Income-Distribution-2003-to-2035-growth-rates.png"", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""How global inequality has changed from 2003 to 2013"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The following visualization offers an alternative view of the data by Hellebrandt and Mauro"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" shown in the chart before."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows the yearly disposable income for all world citizens in both 2003 and 2013. On the x-axis, you see the position of an individual in the global distribution of incomes. On the logarithmic y-axis, you see the annual disposable income at that position."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The increase in prosperity—and decrease in poverty—is substantial. The income cut-off of the poorest 10% has increased from 260 international-$ to 480 international-%, and the median income has almost doubled from 1,100 international-$ to 2,010. Global mean income in 2013 was 5,375 international-$."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""At the same it is still the case, as emphasized before, that incomes are very low for most people in the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""The global income distribution in 2003 and 2013"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Global-Inc-Distribution-2003-and-2013-log.png"", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Global income inequality is very high and will likely stay high for a long time"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The visualization presents the same data in the same way, except that the y-axis is now not logarithmic but linear. This perspective shows the still very high level of global inequality even more clearly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The previous and the following visualization show how high global income inequality is. The cut-off to the richest 10% of the world in 2013 was 14,500 int-$; the cut-off for the poorest 10% was 480 int-$. The ratio is 30.2."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While global inequality is still very high, we live in a period of falling inequality. In 2003, this ratio was 37.6. The Gini coefficient has also fallen from 68.7 to 64.9."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Taking the historical experience as a guide for what is possible in the future, we have to conclude that global inequality will remain high for a long time. To understand this, we can ask how long it would take for those with incomes at the poorest 10% cutoff to achieve the current incomes of the richest 10% cutoff (14,500 international-$). This income level is roughly the level of GDP per capita above which the extreme poverty headcount gets close to 0% for most countries ("", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/the-share-of-people-living-in-extreme-poverty-vs-gdp-per-capita"", ""children"": [{""text"": ""see here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "")."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Even under a very optimistic scenario, it will take several decades for the poorest regions to reach the income level of the global top 10%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""2% is roughly the growth rate that the richest countries of today experienced over the last decades ("", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/economic-growth#economic-growth-at-the-technological-frontier-growth-in-the-usa"", ""children"": [{""text"": ""see here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""). We have seen that poorer countries can achieve faster growth, but we have not seen growth rates of more than 6% over a time frame as long as necessary to reach the level of the global 10% in such a short time. If the past is a good guide for the future, the world will likely be highly unequal for a long time."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""The global income distribution in 2003 and 2013"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""Global-Inc-Distribution-2003-and-2013-linear-scale.png"", ""parseErrors"": []}, {""text"": [{""text"": ""How long does it take for incomes to grow from 480 int-$ to 14,500 int-$?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""html"", ""value"": ""
2% growth172.1 years
4% growth86.9 years
6% growth58.5 years
8% growth44.3 years
10% growth35.8 years
"", ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Inequality within countries and inequality between countries"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Global inequality is driven by changes in both the inequality within countries, and the inequality between countries. This visualization shows how both of these changes determine the changing global inequality."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""Inequality within countries followed a U-shape pattern over the 20th century."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Inequality between countries increased over 2 centuries and peaked in the 1980s, according to the data from Bourguignon and Morrison. Since then, inequality between countries has declined."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As is shown in this visualization, the inequality of income between different countries is much higher than the inequality within countries. The consequence of this is that the trend of global inequality is very much driven by what is happening to the inequality between countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/global-inequality-between-world-citizens-and-its-components"", ""type"": ""chart"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""milanovic"": {""id"": ""milanovic"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The data was made available to Our World In Data by the two authors. The data up to 2008 is published with the main publication Milanovic and Lakner (2015) –Global Income Distribution. Available online at the World Bank: "", ""spanType"": ""span-simple-text""}, {""url"": ""http://elibrary.worldbank.org/doi/abs/10.1596/1813-9450-6719"", ""children"": [{""text"": ""http://elibrary.worldbank.org/doi/abs/10.1596/1813-9450-6719"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""hellebrandt"": {""id"": ""hellebrandt"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The data source is: Hellebrandt, Tomas and Mauro, Paolo (2015) – The Future of Worldwide Income Distribution (April 1, 2015). Peterson Institute for International Economics Working Paper No. 15-7. Available at "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ssrn.com/abstract=2593894"", ""children"": [{""text"": ""SSRN"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" or "", ""spanType"": ""span-simple-text""}, {""url"": ""http://dx.doi.org/10.2139/ssrn.2593894"", ""children"": [{""text"": ""http://dx.doi.org/10.2139/ssrn.2593894"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We thank the authors for making the data available for this data visualization."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""gapminder_data"": {""id"": ""gapminder_data"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The data are produced by Ola Rosling and published on the website of Gapminder."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""You can explore the Gapminder visualization of the income distributions of all countries in their interactive tool "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.gapminder.org/tools/#_locale_id=en;&chart-type=mountain"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Regarding the construction of the data, Hans and Ola Rosling note the following "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.gapminder.org/news/data-sources-dont-panic-end-poverty/"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "": “This graph is constructed by combining data from multiple sources. In summary, we take the best available country estimates for the three indicators: GDP per capita, Population, and Gini coefficient (a measure of income inequality). With these numbers, we can approximate the number of people at different income levels in every country. We then combine all these approximations into a global pile using the method described below under The Adjusted Global Income Scale.”"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""2cde1c0fd85595791c4b107ed47fb3945b134a3b"": {""id"": ""2cde1c0fd85595791c4b107ed47fb3945b134a3b"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""I have taken the data for the visualization of the world income distribution in 1820, 1970, and 2000 from van Zanden, J.L., et al. (eds.) (2014), How Was Life?: Global Well-being since 1820, OECD Publishing. Online "", ""spanType"": ""span-simple-text""}, {""url"": ""http://www.oecd.org/statistics/how-was-life-9789264214262-en.htm"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". The plotted data is interpolated using a Cardinal spline."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The data is originally from the Clio-Infra database "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.clio-infra.eu/"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""549ad0c96aa929d0a8c290a98a415751b2c5258a"": {""id"": ""549ad0c96aa929d0a8c290a98a415751b2c5258a"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Note that global GDP per capita in 2013 was around 14,000 international-$ and substantially higher than mean disposable income from household-level surveys at 5,375 international-$."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We discuss the reasons for this discrepancy "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/economic-growth#discrepancy-between-incomes-reported-in-household-surveys-and-gdp-per-capita"", ""children"": [{""text"": ""here"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". See also the Appendix of the original publication for a longer explanation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""The history of global economic inequality"", ""authors"": [""Max Roser""], ""excerpt"": ""The inequality in people’s living conditions across the world is extremely large. How did the world become so unequal, and what can we expect for the future?"", ""subtitle"": ""The inequality in people’s living conditions across the world is extremely large. How did the world become so unequal, and what can we expect for the future?"", ""featured-image"": ""the-history-of-global-inequality-featured-image.png""}",1,2023-06-01 19:27:25,2017-04-17 08:49:55,2023-12-28 16:31:10,unlisted,ALBJ4Lv-XqImPPNaOmdtteq4N-ASsWHRFl-Ci8ucho7EUlpRw96uCaQxHE4KZXXa3hhs9_-7ahkXhFGLaqaHQg,," The inequality in people’s living conditions today is extremely large. The panel of charts below shows how large these differences are, and how the inequality in 12 important measures of living standards maps onto the economic inequality in the world. What is most important for how healthy, wealthy, and educated you are is not who you are, but where you are. This was the point I made in [another article](https://ourworldindata.org/global-economic-inequality-introduction). A person’s knowledge, skills, and how hard they work all matter for whether they are poor or not – but all these personal factors together matter less than the one factor that is entirely outside of a person’s control: whether they happen to be born into a large, productive economy or not. How did we get here? How did the world become so unequal, and what can we expect for the future? ## Global divergence followed by convergence The chart shows estimates of the distribution of annual income among all world citizens over the last two centuries. To make incomes comparable across countries and time, daily incomes are measured in international-$ — a hypothetical currency that would buy a comparable amount of goods and services that a U.S. dollar would buy in the United States in 2011 (for a more detailed explanation, see [here](https://ourworldindata.org/what-are-PPPs)). The distribution of incomes is shown at 3 points in time: * By 1800, few countries had achieved economic growth. The chart shows that most of the world lived in poverty with an income similar to today's poorest countries. At the beginning of the 19th century, the vast majority—[roughly 80%](https://ourworldindata.org/poverty?insight=after-200-years-of-progress-the-fight-against-global-poverty-is-just-beginning#introduction)—of the world lived in material conditions that we would refer to as extreme poverty today. * In 1975, 175 years later, the world had changed—it had become very unequal. The world income distribution was 'bimodal', with the two-humped shape of a camel: one hump below the international poverty line and a second hump at considerably higher incomes. The world had divided into a poor, developing world and a developed world more than 10-times richer. * Over the following 4 decades, the world income distribution has again changed dramatically. There has been a convergence in incomes: in many poorer countries, especially in South-East Asia, incomes have grown faster than in rich countries. While enormous income differences remain, the world can no longer be neatly divided into 'developed' and 'developing' countries. We have moved from a two-hump to a one-hump world. And at the same time, the distribution has also shifted to the right—the incomes of many of the world's poorest citizens have increased, and extreme poverty has [fallen faster than ever before](https://ourworldindata.org/history-of-poverty-has-just-begun) in human history. We have visualized a similar dataset from the OECD [here](http://ourworldindata.org/roser/graphs/WorldIncomeDistribution1820to2000/WorldIncomeDistribution1820to2000.html).1 ## Global income inequality increased for two centuries and is now falling This visualization shows the distribution of incomes between 1988 and 2011. The data was compiled by the economists Branko Milanovic and Christoph Lakner.3 To see the change over time, select the years above the distribution. The previous visualization, which showed the change from 1820 to the year 2015, is based on estimates of inflation-adjusted average incomes per country (GDP per capita) and a measure of income inequality within a country only. It gives us a rough idea of how the distribution of incomes changed, but it is not very detailed or precise. In contrast to this, the work by Branko Milanovic and Christoph Lakner is based on much more detailed household survey data. This data measures household income at each decile of the income distribution, and the two authors used this information to arrive at the global income distribution. The downside of this approach is that we can only go as far back in time as household surveys were conducted in many countries around the world. The visualization shows the end of the long era in human history in which global inequality was increasing. Starting with industrialization in North-Western Europe, incomes in this part of the world started to increase while material prosperity in the rest of the world remained low. While some countries followed European industrialization – first Northern America, Oceania, and parts of South America and later Japan and East Asia – other countries in Asia and Africa remained poor. As a consequence of this, global inequality increased over a long period. Only in the period shown in this visualization did this change: with rapid growth in much of Asia and Latin America, the global distribution of incomes became less unequal. The incomes of the poorer half of the world population rose faster than the incomes of the richer half. _Global Income Distribution 1988 to 2011_ 3 ## A look into the future of global inequality This visualization shows how the global income distribution has changed over the decade up to 2013. Tomáš Hellebrandt and Paolo Mauro, the authors of the paper4 from which this data is taken, confirm the finding that global inequality has declined but remains very high: the Gini coefficient of global inequality has declined from 68.7 to 64.9. The visualizations above show the income distribution on a logarithmic x-axis. This chart, in contrast, plots incomes on a linear x-axis and thereby emphasizes how very high global inequality still is: The bulk of the world population lives on very low incomes, and the income distribution stretches out very far to the higher incomes at the right-hand side of the chart; incomes over 14,000 international-$ are cut off as they would make this chart with a linear x-axis unreadable. A second positive global development shown in this chart is the rise of the global median income. In 2003 half of the world's population lived on less than 1,090 international-$ per year, and the other half lived on more than 1,090 international-$. This level of global median income has almost doubled over the last decade and was 2,010 international-$ in 2013. Finally, the authors also dare to project what global inequality will look like in 2035. Assuming the growth rates shown in the insert in the top-right corner, the authors project global inequality to decline further and to reach a Gini of 61.3. At the same time, the incomes of the world's poorer half would continue to increase significantly, so that the global median income could again double and reach 4,000 international-$ in 2035. If you are looking for a visualization of only the observed global income distribution in 2003 and 2013, you can find it [here](https://ourworldindata.org/app/uploads/2013/11/2-World-Income-Distribution-2003-to-2035.png). ## How global inequality has changed from 2003 to 2013 The following visualization offers an alternative view of the data by Hellebrandt and Mauro4 shown in the chart before. The chart shows the yearly disposable income for all world citizens in both 2003 and 2013. On the x-axis, you see the position of an individual in the global distribution of incomes. On the logarithmic y-axis, you see the annual disposable income at that position. The increase in prosperity—and decrease in poverty—is substantial. The income cut-off of the poorest 10% has increased from 260 international-$ to 480 international-%, and the median income has almost doubled from 1,100 international-$ to 2,010. Global mean income in 2013 was 5,375 international-$.5 At the same it is still the case, as emphasized before, that incomes are very low for most people in the world. ## Global income inequality is very high and will likely stay high for a long time The visualization presents the same data in the same way, except that the y-axis is now not logarithmic but linear. This perspective shows the still very high level of global inequality even more clearly. The previous and the following visualization show how high global income inequality is. The cut-off to the richest 10% of the world in 2013 was 14,500 int-$; the cut-off for the poorest 10% was 480 int-$. The ratio is 30.2. While global inequality is still very high, we live in a period of falling inequality. In 2003, this ratio was 37.6. The Gini coefficient has also fallen from 68.7 to 64.9. Taking the historical experience as a guide for what is possible in the future, we have to conclude that global inequality will remain high for a long time. To understand this, we can ask how long it would take for those with incomes at the poorest 10% cutoff to achieve the current incomes of the richest 10% cutoff (14,500 international-$). This income level is roughly the level of GDP per capita above which the extreme poverty headcount gets close to 0% for most countries ([see here](https://ourworldindata.org/grapher/the-share-of-people-living-in-extreme-poverty-vs-gdp-per-capita)). Even under a very optimistic scenario, it will take several decades for the poorest regions to reach the income level of the global top 10%. 2% is roughly the growth rate that the richest countries of today experienced over the last decades ([see here](https://ourworldindata.org/economic-growth#economic-growth-at-the-technological-frontier-growth-in-the-usa)). We have seen that poorer countries can achieve faster growth, but we have not seen growth rates of more than 6% over a time frame as long as necessary to reach the level of the global 10% in such a short time. If the past is a good guide for the future, the world will likely be highly unequal for a long time. ### How long does it take for incomes to grow from 480 int-$ to 14,500 int-$?
2% growth172.1 years
4% growth86.9 years
6% growth58.5 years
8% growth44.3 years
10% growth35.8 years
## Inequality within countries and inequality between countries Global inequality is driven by changes in both the inequality within countries, and the inequality between countries. This visualization shows how both of these changes determine the changing global inequality. * Inequality within countries followed a U-shape pattern over the 20th century. * Inequality between countries increased over 2 centuries and peaked in the 1980s, according to the data from Bourguignon and Morrison. Since then, inequality between countries has declined. As is shown in this visualization, the inequality of income between different countries is much higher than the inequality within countries. The consequence of this is that the trend of global inequality is very much driven by what is happening to the inequality between countries. The data was made available to Our World In Data by the two authors. The data up to 2008 is published with the main publication Milanovic and Lakner (2015) –Global Income Distribution. Available online at the World Bank: [http://elibrary.worldbank.org/doi/abs/10.1596/1813-9450-6719](http://elibrary.worldbank.org/doi/abs/10.1596/1813-9450-6719). The data source is: Hellebrandt, Tomas and Mauro, Paolo (2015) – The Future of Worldwide Income Distribution (April 1, 2015). Peterson Institute for International Economics Working Paper No. 15-7. Available at [SSRN](https://ssrn.com/abstract=2593894) or [http://dx.doi.org/10.2139/ssrn.2593894](http://dx.doi.org/10.2139/ssrn.2593894). We thank the authors for making the data available for this data visualization. The data are produced by Ola Rosling and published on the website of Gapminder. You can explore the Gapminder visualization of the income distributions of all countries in their interactive tool [here](http://www.gapminder.org/tools/#_locale_id=en;&chart-type=mountain). Regarding the construction of the data, Hans and Ola Rosling note the following [here](http://www.gapminder.org/news/data-sources-dont-panic-end-poverty/): “This graph is constructed by combining data from multiple sources. In summary, we take the best available country estimates for the three indicators: GDP per capita, Population, and Gini coefficient (a measure of income inequality). With these numbers, we can approximate the number of people at different income levels in every country. We then combine all these approximations into a global pile using the method described below under The Adjusted Global Income Scale.” I have taken the data for the visualization of the world income distribution in 1820, 1970, and 2000 from van Zanden, J.L., et al. (eds.) (2014), How Was Life?: Global Well-being since 1820, OECD Publishing. Online [here](http://www.oecd.org/statistics/how-was-life-9789264214262-en.htm). The plotted data is interpolated using a Cardinal spline. The data is originally from the Clio-Infra database [here](https://www.clio-infra.eu/). Note that global GDP per capita in 2013 was around 14,000 international-$ and substantially higher than mean disposable income from household-level surveys at 5,375 international-$. We discuss the reasons for this discrepancy [here](https://ourworldindata.org/economic-growth#discrepancy-between-incomes-reported-in-household-surveys-and-gdp-per-capita). See also the Appendix of the original publication for a longer explanation.",The history of global economic inequality 1pxn1zl3ZUQsPf2VmqkbQlZYmTIqjcWjmE87oVL4MB4k,new-polio-vaccines-are-key-to-preventing-outbreaks-and-achieving-eradication,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""We’ve "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/global-fight-polio"", ""children"": [{""text"": ""come a long way"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" in the fight against "", ""spanType"": ""span-simple-text""}, {""id"": ""polio"", ""children"": [{""text"": ""polio"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" — the infectious disease that used to paralyze hundreds of thousands of people each year. Most of them were children. Eradication is possible, but the last stretch has proven difficult."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Two of the three serotypes (distinct types within a species of virus) of wild poliovirus have already been eradicated."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, two big challenges remain in crossing the finish line. One is eliminating the last serotype of "", ""spanType"": ""span-simple-text""}, {""id"": ""wild-poliovirus"", ""children"": [{""text"": ""wild poliovirus"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "". Another is containing "", ""spanType"": ""span-simple-text""}, {""id"": ""vaccine-derived-poliovirus"", ""children"": [{""text"": ""vaccine-derived polioviruses"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", which arose from "", ""spanType"": ""span-simple-text""}, {""id"": ""oral-polio-vaccine"", ""children"": [{""text"": ""oral polio vaccines"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" in rare circumstances and spread in some regions where protection against the disease declined."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world can overcome these hurdles. We can use new vaccines to contain them and improve testing, outbreak responses, and sanitation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Vaccines have helped us reduce polio cases substantially over time"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart below shows the dramatic decline in polio cases."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This was possible due to effective vaccination efforts with two types of vaccine: "", ""spanType"": ""span-simple-text""}, {""id"": ""inactivated-polio-vaccine"", ""children"": [{""text"": ""inactivated polio vaccines (IPV)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", developed by Jonas Salk in 1955, and "", ""spanType"": ""span-simple-text""}, {""id"": ""oral-polio-vaccine"", ""children"": [{""text"": ""oral polio vaccines (OPV)"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", developed by Albert Sabin in 1961."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Improvements in providing "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/clean-water-sanitation"", ""children"": [{""text"": ""clean water and sanitation"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" have also helped to reduce the spread of poliovirus through contaminated water and food and the risks of other infections, which prevent children from developing immunity against polio."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the early 1980s, there were around 400,000 estimated cases annually. In the last few years, there have been around 4,000. That’s a hundred-fold decline. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/polio#the-costs-and-benefits-of-eradicating-polio"", ""children"": [{""text"": ""Millions of children"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" have been spared lifelong paralysis."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/number-of-estimated-paralytic-polio-cases-by-world-region"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""We are very close to eradicating all three types of wild poliovirus"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""id"": ""wild-poliovirus"", ""children"": [{""text"": ""Wild poliovirus"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" has three serotypes. The three serotypes are distinct types of poliovirus with protein structures that differ sufficiently that protection from one doesn’t protect from the other."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world has eradicated "", ""spanType"": ""span-simple-text""}, {""id"": ""wild-poliovirus"", ""children"": [{""text"": ""wild poliovirus"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" serotypes 2 and 3."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""The last case of wild poliovirus serotype 2 was in 1999 in India, and the WHO declared it eradicated in 2015."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The last case of wild poliovirus serotype 3 was seen in 2012 in Nigeria, and it was declared eradicated in 2019."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The world is, therefore, very close to eradicating all serotypes of wild poliovirus globally."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As shown in the map below, only two countries — Afghanistan and Pakistan — are still endemic for wild poliovirus serotype 1 (WPV1)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But as the chart shows, the number of cases is now very low. In 2023, only six cases of wild polio were reported in Afghanistan and another six in Pakistan."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""By testing widely to identify potential cases, working with local communities in hard-to-reach areas and at borders, and improving vaccination rates and sanitation, this goal is within reach."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""left"": [{""url"": ""https://ourworldindata.org/grapher/cases-of-paralytic-polio-from-wild-polioviruses?tab=map&country=~OWID_WRL"", ""type"": ""chart"", ""parseErrors"": []}], ""type"": ""side-by-side"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/cases-of-paralytic-polio-from-wild-polioviruses"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Most current cases come from vaccine-derived polioviruses"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Although the absolute number of cases is much lower than in the past, most cases"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""recently have"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""come from "", ""spanType"": ""span-simple-text""}, {""id"": ""vaccine-derived-poliovirus"", ""children"": [{""text"": ""vaccine-derived polioviruses"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" (VDPVs), as shown in the chart below."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Vaccine-derived polioviruses can arise from the weakened virus in the "", ""spanType"": ""span-simple-text""}, {""id"": ""oral-polio-vaccine"", ""children"": [{""text"": ""oral polio vaccine"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" if it has mutated significantly over time in vaccinated people and reverted to the original strain of polio."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Oral polio vaccines are "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/polio-vaccine-schedule"", ""children"": [{""text"": ""used in poorer countries"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" because they are much easier to administer (as oral drops) and cheaper to manufacture than "", ""spanType"": ""span-simple-text""}, {""id"": ""inactivated-polio-vaccine"", ""children"": [{""text"": ""inactivated polio vaccines"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", which are given by injection."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""People with immune deficiencies are at higher risk of the vaccine reverting because they can sustain longer infections, giving the virus more time to evolve."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It can then spread and cause new outbreaks if immunity has fallen in communities. 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It was harder to provide inactivated polio vaccines at scale in poorer regions and reach every child, leading to a rise in cases."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/cases-of-paralytic-polio-from-vaccine-derived-viruses-by-strain"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""New vaccines can help prevent further outbreaks"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since 2021, new oral polio vaccines against serotype 2 have been used to prevent further outbreaks of VDPV2."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These are much more genetically stable than the previous oral polio vaccine and much less likely to mutate or potentially revert to the original strain."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""They have already been rolled out widely and helped "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/cases-of-paralytic-polio-from-vaccine-derived-viruses-by-strain"", ""children"": [{""text"": ""effectively control outbreaks of VDPV2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""New oral polio vaccines against serotypes 1 and 3 are still in development."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In addition, new types of "", ""spanType"": ""span-simple-text""}, {""id"": ""inactivated-polio-vaccine"", ""children"": [{""text"": ""inactivated polio vaccines"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" are also being developed. For example, some candidate vaccines can be administered through skin patches instead of injections. These could be cheaper, easier to provide, and unable to revert to the original strain."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These new technologies will be crucial in preventing further outbreaks as we approach the ultimate goal of polio eradication."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Let’s close the chapter on polio"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To achieve polio eradication, it’s crucial to contain every last case quickly to prevent the spread of polio and protect children from this debilitating disease."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We can use new vaccines, increase "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/polio-testing"", ""children"": [{""text"": ""polio testing"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", and improve access to clean water and sanitation."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Together, we can successfully close the chapter on polio, which would be a major victory for humanity."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""Edouard Mathieu, Max Roser, and Hannah Ritchie provided helpful feedback on this article."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Acknowledgments"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""1ab12c1b205789a96fe85437520942ce9d3e9417"": {""id"": ""1ab12c1b205789a96fe85437520942ce9d3e9417"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""When children have multiple infections in their gut simultaneously, this reduces their ability to develop immunity against polio. 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J., Mach, O., Santhana Gopala Krishnan, R., Voorman, A., Vertefeuille, J. F., Abdelwahab, J., Gumede, N., Goel, A., Sosler, S., Sever, J., Bandyopadhyay, A. S., Pallansch, M. A., Nandy, R., Mkanda, P., Diop, O. M., & Sutter, R. W. (2020). Evolving epidemiology of poliovirus serotype 2 following withdrawal of the serotype 2 oral poliovirus vaccine. 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C., Lyons, H., Blake, I. M., Jafari, H., Oberste, M. S., Kew, O. M., & Grassly, N. C. (2016). Preventing Vaccine-Derived Poliovirus Emergence during the Polio Endgame. PLOS Pathogens, 12(7), e1005728. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1371/journal.ppat.1005728"", ""children"": [{""text"": ""https://doi.org/10.1371/journal.ppat.1005728"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Macklin, G. R., O’Reilly, K. M., Grassly, N. C., Edmunds, W. J., Mach, O., Santhana Gopala Krishnan, R., Voorman, A., Vertefeuille, J. F., Abdelwahab, J., Gumede, N., Goel, A., Sosler, S., Sever, J., Bandyopadhyay, A. S., Pallansch, M. A., Nandy, R., Mkanda, P., Diop, O. M., & Sutter, R. W. (2020). Evolving epidemiology of poliovirus serotype 2 following withdrawal of the serotype 2 oral poliovirus vaccine. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Science"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""368"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(6489), 401–405."", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1126/science.aba1238"", ""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""url"": ""https://doi.org/10.1126/science.aba1238"", ""children"": [{""text"": ""https://doi.org/10.1126/science.aba1238"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Oral and inactivated polio vaccines are believed to have similar efficacy in preventing paralytic polio, although their efficacy varies by region. 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"", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1093/infdis/jit601"", ""children"": [{""text"": ""https://doi.org/10.1093/infdis/jit601"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Hird, T. R., & Grassly, N. C. (2012). Systematic Review of Mucosal Immunity Induced by Oral and Inactivated Poliovirus Vaccines against Virus Shedding following Oral Poliovirus Challenge. PLoS Pathogens, 8(4), e1002599. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1371/journal.ppat.1002599"", ""children"": [{""text"": ""https://doi.org/10.1371/journal.ppat.1002599"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""489b81144af1a4777b166e7f4f4a983fe21c3837"": {""id"": ""489b81144af1a4777b166e7f4f4a983fe21c3837"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""​​In Afghanistan and Pakistan, Pashto-speaking communities are disproportionately affected by polio and represent a large majority of cases."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For around a decade, vaccination was restricted in certain areas of Afghanistan, but since 2021, this ban has been lifted."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Bagcchi, S. (2022). Polio vaccination in Afghanistan. The Lancet Microbe, 3(1), e10. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/S2666-5247(21)00336-0"", ""children"": [{""text"": ""https://doi.org/10.1016/S2666-5247(21)00336-0"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""World Health Organization, & Global Polio Eradication Initiative. (2021). Polio eradication strategy 2022–2026: Delivering on a promise. Available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://polioeradication.org/wp-content/uploads/2021/10/9789240031937-eng.pdf"", ""children"": [{""text"": ""online"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""64073a545f241a4b95aa19bc129d2f9ab332d656"": {""id"": ""64073a545f241a4b95aa19bc129d2f9ab332d656"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Unfortunately, global data showing the breakdown of cases by serotype is only available from 2000 onwards."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""6640a63158fab700c102c115e541b7b6e33abe06"": {""id"": ""6640a63158fab700c102c115e541b7b6e33abe06"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Thompson, K. M., & Kalkowska, D. A. (2021). Potential Future Use, Costs, and Value of Poliovirus Vaccines. Risk Analysis, 41(2), 349–363. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1111/risa.13557"", ""children"": [{""text"": ""https://doi.org/10.1111/risa.13557"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""68985979cb4328fce17ab6c556c47c8acb2d822f"": {""id"": ""68985979cb4328fce17ab6c556c47c8acb2d822f"", ""index"": 8, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Famulare, M., Chang, S., Iber, J., Zhao, K., Adeniji, J. A., Bukbuk, D., Baba, M., Behrend, M., Burns, C. C., & Oberste, M. S. (2016). Sabin Vaccine Reversion in the Field: A Comprehensive Analysis of Sabin-Like Poliovirus Isolates in Nigeria. Journal of Virology, 90(1), 317–331. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1128/JVI.01532-15"", ""children"": [{""text"": ""https://doi.org/10.1128/JVI.01532-15"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""aab857b6312fb4065ed56af172cde8949bdb25df"": {""id"": ""aab857b6312fb4065ed56af172cde8949bdb25df"", ""index"": 12, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Yeh, M. T., Smith, M., Carlyle, S., Konopka-Anstadt, J. L., Burns, C. C., Konz, J., Andino, R., & Macadam, A. (2023). Genetic stabilization of attenuated oral vaccines against poliovirus types 1 and 3. Nature, 619(7968), 135–142. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1038/s41586-023-06212-3"", ""children"": [{""text"": ""https://doi.org/10.1038/s41586-023-06212-3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ac78d2e793dde2399b0c22b008f3480d73203a93"": {""id"": ""ac78d2e793dde2399b0c22b008f3480d73203a93"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""U.S. National Authority for Containment of Poliovirus (2023). Polio Disease and Poliovirus Containment. Centers for Disease Control and Prevention. Available "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.cdc.gov/orr/polioviruscontainment/diseaseandvirus.htm"", ""children"": [{""text"": ""online"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b21358b10e4c452327c3ce6f0d0e888ddd116eba"": {""id"": ""b21358b10e4c452327c3ce6f0d0e888ddd116eba"", ""index"": 11, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Bandyopadhyay, A. S., Cooper, L. V., & Zipursky, S. (2024). One billion doses and WHO prequalification of nOPV2: Implications for the global polio situation and beyond. PLOS Global Public Health, 4(2), e0002920. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1371/journal.pgph.0002920"", ""children"": [{""text"": ""https://doi.org/10.1371/journal.pgph.0002920"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""b26081ec78b42bff9aec1eb0e5b89bc61b73a113"": {""id"": ""b26081ec78b42bff9aec1eb0e5b89bc61b73a113"", ""index"": 13, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Kumar, P., Bird, C., Holland, D., Joshi, S. B., & Volkin, D. B. (2022). Current and next-generation formulation strategies for inactivated polio vaccines to lower costs, increase coverage, and facilitate polio eradication. Human Vaccines & Immunotherapeutics, 18(7), 2154100. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1080/21645515.2022.2154100"", ""children"": [{""text"": ""https://doi.org/10.1080/21645515.2022.2154100"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""d1b3df4ea854e4e264efbf0cac38785271aeef1a"": {""id"": ""d1b3df4ea854e4e264efbf0cac38785271aeef1a"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Burns, C. C., Diop, O. M., Sutter, R. W., & Kew, O. M. (2014). Vaccine-Derived Polioviruses. Journal of Infectious Diseases, 210(suppl 1), S283–S293. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1093/infdis/jiu295"", ""children"": [{""text"": ""https://doi.org/10.1093/infdis/jiu295"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ec143397e90eb69386f5cf1097409232c8f6e5d8"": {""id"": ""ec143397e90eb69386f5cf1097409232c8f6e5d8"", ""index"": 10, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Konopka-Anstadt, J. L., Campagnoli, R., Vincent, A., Shaw, J., Wei, L., Wynn, N. T., Smithee, S. E., Bujaki, E., Te Yeh, M., Laassri, M., Zagorodnyaya, T., Weiner, A. J., Chumakov, K., Andino, R., Macadam, A., Kew, O., & Burns, C. C. (2020). Development of a new oral poliovirus vaccine for the eradication end game using codon deoptimization. Npj Vaccines, 5(1), 26. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1038/s41541-020-0176-7"", ""children"": [{""text"": ""https://doi.org/10.1038/s41541-020-0176-7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Bandyopadhyay, A. S., & Zipursky, S. (2023). A novel tool to eradicate an ancient scourge: The novel oral polio vaccine type 2 story. The Lancet Infectious Diseases, 23(2), e67–e71. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/S1473-3099(22)00582-5"", ""children"": [{""text"": ""https://doi.org/10.1016/S1473-3099(22)00582-5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Ochoge, M., Futa, A. C., Umesi, A., Affleck, L., Kotei, L., Daffeh, B., Saidy-Jah, E., Njie, A., Oyadiran, O., Edem, B., Jallow, M., Jallow, E., Donkor, S. A., Tritama, E., Abid, T., Jones, K. A. V., Mainou, B. A., Konz, J. O., Fix, A., … Clarke, E. (2024). Safety of the novel oral poliovirus vaccine type 2 (nOPV2) in infants and young children aged 1 to <5 years and lot-to-lot consistency of the immune response to nOPV2 in infants in The Gambia: A phase 3, double-blind, randomised controlled trial. The Lancet, 403(10432), 1164–1175. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1016/S0140-6736(23)02844-1"", ""children"": [{""text"": ""https://doi.org/10.1016/S0140-6736(23)02844-1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f639d6609b854418a5f29413f64f377f9d637acb"": {""id"": ""f639d6609b854418a5f29413f64f377f9d637acb"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Global Polio Eradication Initiative. (2019, October 24). Two out of three wild poliovirus strains eradicated. News Stories. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20191024163941/http://polioeradication.org/news-post/two-out-of-three-wild-poliovirus-strains-eradicated/"", ""children"": [{""text"": ""https://web.archive.org/web/20191024163941/http://polioeradication.org/news-post/two-out-of-three-wild-poliovirus-strains-eradicated/"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""New polio vaccines are key to preventing outbreaks and achieving eradication"", ""authors"": [""Saloni Dattani""], ""excerpt"": ""To reach the goal of polio eradication, we can use new vaccines to contain outbreaks and improve testing, outbreak responses, and sanitation."", ""subtitle"": ""To reach the goal of polio eradication, we can use new vaccines to contain outbreaks and improve testing, outbreak responses, and sanitation."", ""featured-image"": ""polio-edit-reduced.png""}",1,2024-05-02 09:17:37,2024-05-27 08:01:04,2024-05-27 07:50:21,listed,ALBJ4LvPdv67GYiXAUA03xMBLmeoZ5xN38FE173NNvnQVmtR6Z9_xCczphWllH2Z4HLSMSw7aeFvLG9XdcV54A,,"We’ve [come a long way](https://ourworldindata.org/global-fight-polio) in the fight against polio — the infectious disease that used to paralyze hundreds of thousands of people each year. Most of them were children. Eradication is possible, but the last stretch has proven difficult. Two of the three serotypes (distinct types within a species of virus) of wild poliovirus have already been eradicated. However, two big challenges remain in crossing the finish line. One is eliminating the last serotype of wild poliovirus. Another is containing vaccine-derived polioviruses, which arose from oral polio vaccines in rare circumstances and spread in some regions where protection against the disease declined. The world can overcome these hurdles. We can use new vaccines to contain them and improve testing, outbreak responses, and sanitation. # Vaccines have helped us reduce polio cases substantially over time The chart below shows the dramatic decline in polio cases. This was possible due to effective vaccination efforts with two types of vaccine: inactivated polio vaccines (IPV), developed by Jonas Salk in 1955, and oral polio vaccines (OPV), developed by Albert Sabin in 1961. Improvements in providing [clean water and sanitation](https://ourworldindata.org/clean-water-sanitation) have also helped to reduce the spread of poliovirus through contaminated water and food and the risks of other infections, which prevent children from developing immunity against polio.1 In the early 1980s, there were around 400,000 estimated cases annually. In the last few years, there have been around 4,000. That’s a hundred-fold decline. [Millions of children](https://ourworldindata.org/polio#the-costs-and-benefits-of-eradicating-polio) have been spared lifelong paralysis. # We are very close to eradicating all three types of wild poliovirus Wild poliovirus has three serotypes. The three serotypes are distinct types of poliovirus with protein structures that differ sufficiently that protection from one doesn’t protect from the other. The world has eradicated wild poliovirus serotypes 2 and 3.2 * The last case of wild poliovirus serotype 2 was in 1999 in India, and the WHO declared it eradicated in 2015. * The last case of wild poliovirus serotype 3 was seen in 2012 in Nigeria, and it was declared eradicated in 2019.3 The world is, therefore, very close to eradicating all serotypes of wild poliovirus globally. As shown in the map below, only two countries — Afghanistan and Pakistan — are still endemic for wild poliovirus serotype 1 (WPV1). But as the chart shows, the number of cases is now very low. In 2023, only six cases of wild polio were reported in Afghanistan and another six in Pakistan. By testing widely to identify potential cases, working with local communities in hard-to-reach areas and at borders, and improving vaccination rates and sanitation, this goal is within reach.4 # Most current cases come from vaccine-derived polioviruses Although the absolute number of cases is much lower than in the past, most cases_ _recently have_ _come from vaccine-derived polioviruses (VDPVs), as shown in the chart below.5 Vaccine-derived polioviruses can arise from the weakened virus in the oral polio vaccine if it has mutated significantly over time in vaccinated people and reverted to the original strain of polio. Oral polio vaccines are [used in poorer countries](https://ourworldindata.org/grapher/polio-vaccine-schedule) because they are much easier to administer (as oral drops) and cheaper to manufacture than inactivated polio vaccines, which must be given by injection.6 People with immune deficiencies are at higher risk of the vaccine reverting because they can sustain longer infections, giving the virus more time to evolve.7 It can then spread and cause new outbreaks if immunity has fallen in communities. So, somewhat counterintuitively, communities with lower vaccination coverage are more vulnerable to vaccine-derived polio.8 So far, most cases of vaccine-derived poliovirus have come from vaccine-derived polioviruses of serotype 2 — known as VDPV2 — which can mutate faster than other serotypes, making it more likely to revert than other serotypes.9 In addition, there were interruptions in vaccination against polio serotype 2 in 2016, when vaccination against that particular serotype was switched from the oral to the inactivated polio vaccine. It was harder to provide inactivated polio vaccines at scale in poorer regions and reach every child, leading to a rise in cases.10 # New vaccines can help prevent further outbreaks Since 2021, new oral polio vaccines against serotype 2 have been used to prevent further outbreaks of VDPV2. These are much more genetically stable than the previous oral polio vaccine and much less likely to mutate or potentially revert to the original strain.11 They have already been rolled out widely and helped [effectively control outbreaks of VDPV2](https://ourworldindata.org/grapher/cases-of-paralytic-polio-from-vaccine-derived-viruses-by-strain).12 New oral polio vaccines against serotypes 1 and 3 are still in development.13 In addition, new types of inactivated polio vaccines are also being developed. For example, some candidate vaccines can be administered through skin patches instead of injections. These could be cheaper, easier to provide, and unable to revert to the original strain.14 These new technologies will be crucial in preventing further outbreaks as we approach the ultimate goal of polio eradication. # Let’s close the chapter on polio To achieve polio eradication, it’s crucial to contain every last case quickly to prevent the spread of polio and protect children from this debilitating disease. We can use new vaccines, increase [polio testing](https://ourworldindata.org/polio-testing), and improve access to clean water and sanitation. Together, we can successfully close the chapter on polio, which would be a major victory for humanity. When children have multiple infections in their gut simultaneously, this reduces their ability to develop immunity against polio. This reduces the efficacy of oral polio vaccines and is one reason vaccine efficacy against gut infections tends to be lower in poorer countries. Parker, E. P. K., Kampmann, B., Kang, G., & Grassly, N. C. (2014). Influence of Enteric Infections on Response to Oral Poliovirus Vaccine: A Systematic Review and Meta-analysis. The Journal of Infectious Diseases, 210(6), 853–864. [https://doi.org/10.1093/infdis/jiu182](https://doi.org/10.1093/infdis/jiu182) Global Polio Eradication Initiative. (2019, October 24). Two out of three wild poliovirus strains eradicated. News Stories. [https://web.archive.org/web/20191024163941/http://polioeradication.org/news-post/two-out-of-three-wild-poliovirus-strains-eradicated/](https://web.archive.org/web/20191024163941/http://polioeradication.org/news-post/two-out-of-three-wild-poliovirus-strains-eradicated/) U.S. National Authority for Containment of Poliovirus (2023). Polio Disease and Poliovirus Containment. Centers for Disease Control and Prevention. Available [online](https://www.cdc.gov/orr/polioviruscontainment/diseaseandvirus.htm). ​​In Afghanistan and Pakistan, Pashto-speaking communities are disproportionately affected by polio and represent a large majority of cases. For around a decade, vaccination was restricted in certain areas of Afghanistan, but since 2021, this ban has been lifted. Bagcchi, S. (2022). Polio vaccination in Afghanistan. The Lancet Microbe, 3(1), e10. [https://doi.org/10.1016/S2666-5247(21)00336-0](https://doi.org/10.1016/S2666-5247(21)00336-0) World Health Organization, & Global Polio Eradication Initiative. (2021). Polio eradication strategy 2022–2026: Delivering on a promise. Available [online](https://polioeradication.org/wp-content/uploads/2021/10/9789240031937-eng.pdf). Unfortunately, global data showing the breakdown of cases by serotype is only available from 2000 onwards. Thompson, K. M., & Kalkowska, D. A. (2021). Potential Future Use, Costs, and Value of Poliovirus Vaccines. Risk Analysis, 41(2), 349–363. [https://doi.org/10.1111/risa.13557](https://doi.org/10.1111/risa.13557) Burns, C. C., Diop, O. M., Sutter, R. W., & Kew, O. M. (2014). Vaccine-Derived Polioviruses. Journal of Infectious Diseases, 210(suppl 1), S283–S293. [https://doi.org/10.1093/infdis/jiu295](https://doi.org/10.1093/infdis/jiu295) Macklin, G. R., O’Reilly, K. M., Grassly, N. C., Edmunds, W. J., Mach, O., Santhana Gopala Krishnan, R., Voorman, A., Vertefeuille, J. F., Abdelwahab, J., Gumede, N., Goel, A., Sosler, S., Sever, J., Bandyopadhyay, A. S., Pallansch, M. A., Nandy, R., Mkanda, P., Diop, O. M., & Sutter, R. W. (2020). Evolving epidemiology of poliovirus serotype 2 following withdrawal of the serotype 2 oral poliovirus vaccine. _Science_, _368_(6489), 401–405.[ ](https://doi.org/10.1126/science.aba1238)[https://doi.org/10.1126/science.aba1238](https://doi.org/10.1126/science.aba1238) Famulare, M., Chang, S., Iber, J., Zhao, K., Adeniji, J. A., Bukbuk, D., Baba, M., Behrend, M., Burns, C. C., & Oberste, M. S. (2016). Sabin Vaccine Reversion in the Field: A Comprehensive Analysis of Sabin-Like Poliovirus Isolates in Nigeria. Journal of Virology, 90(1), 317–331. [https://doi.org/10.1128/JVI.01532-15](https://doi.org/10.1128/JVI.01532-15) Researchers attribute vaccine-derived poliovirus outbreaks after 2016 to interruptions in vaccination rates that occurred after the global “switch” in 2016, where countries moved away from using oral polio vaccines for serotype 2 to using inactivated polio vaccines for serotype 2. Although inactivated polio vaccines don’t carry a risk of reversion because they are more expensive and difficult to administer, this reduced vaccination coverage against serotype 2 after that period, leading to a loss of population immunity and easier spread of VDPV2. Pons-Salort, M., Burns, C. C., Lyons, H., Blake, I. M., Jafari, H., Oberste, M. S., Kew, O. M., & Grassly, N. C. (2016). Preventing Vaccine-Derived Poliovirus Emergence during the Polio Endgame. PLOS Pathogens, 12(7), e1005728. [https://doi.org/10.1371/journal.ppat.1005728](https://doi.org/10.1371/journal.ppat.1005728) Macklin, G. R., O’Reilly, K. M., Grassly, N. C., Edmunds, W. J., Mach, O., Santhana Gopala Krishnan, R., Voorman, A., Vertefeuille, J. F., Abdelwahab, J., Gumede, N., Goel, A., Sosler, S., Sever, J., Bandyopadhyay, A. S., Pallansch, M. A., Nandy, R., Mkanda, P., Diop, O. M., & Sutter, R. W. (2020). Evolving epidemiology of poliovirus serotype 2 following withdrawal of the serotype 2 oral poliovirus vaccine. _Science_, _368_(6489), 401–405.[ ](https://doi.org/10.1126/science.aba1238)[https://doi.org/10.1126/science.aba1238](https://doi.org/10.1126/science.aba1238) Oral and inactivated polio vaccines are believed to have similar efficacy in preventing paralytic polio, although their efficacy varies by region. Oral vaccines are more likely to prevent ‘shedding’ of the virus in the stool, which could be more important in some regions. Estivariz, C. F., Kovacs, S. D., & Mach, O. (2023). Review of use of inactivated poliovirus vaccine in campaigns to control type 2 circulating vaccine derived poliovirus (cVDPV) outbreaks. Vaccine, 41, A113–A121. [https://doi.org/10.1016/j.vaccine.2022.03.027](https://doi.org/10.1016/j.vaccine.2022.03.027) Grassly, N. C. (2014). Immunogenicity and Effectiveness of Routine Immunization With 1 or 2 Doses of Inactivated Poliovirus Vaccine: Systematic Review and Meta-analysis. The Journal of Infectious Diseases, 210(suppl_1), S439–S446. [https://doi.org/10.1093/infdis/jit601](https://doi.org/10.1093/infdis/jit601) Hird, T. R., & Grassly, N. C. (2012). Systematic Review of Mucosal Immunity Induced by Oral and Inactivated Poliovirus Vaccines against Virus Shedding following Oral Poliovirus Challenge. PLoS Pathogens, 8(4), e1002599. [https://doi.org/10.1371/journal.ppat.1002599](https://doi.org/10.1371/journal.ppat.1002599) Konopka-Anstadt, J. L., Campagnoli, R., Vincent, A., Shaw, J., Wei, L., Wynn, N. T., Smithee, S. E., Bujaki, E., Te Yeh, M., Laassri, M., Zagorodnyaya, T., Weiner, A. J., Chumakov, K., Andino, R., Macadam, A., Kew, O., & Burns, C. C. (2020). Development of a new oral poliovirus vaccine for the eradication end game using codon deoptimization. Npj Vaccines, 5(1), 26. [https://doi.org/10.1038/s41541-020-0176-7](https://doi.org/10.1038/s41541-020-0176-7) Bandyopadhyay, A. S., & Zipursky, S. (2023). A novel tool to eradicate an ancient scourge: The novel oral polio vaccine type 2 story. The Lancet Infectious Diseases, 23(2), e67–e71. [https://doi.org/10.1016/S1473-3099(22)00582-5](https://doi.org/10.1016/S1473-3099(22)00582-5) Ochoge, M., Futa, A. C., Umesi, A., Affleck, L., Kotei, L., Daffeh, B., Saidy-Jah, E., Njie, A., Oyadiran, O., Edem, B., Jallow, M., Jallow, E., Donkor, S. A., Tritama, E., Abid, T., Jones, K. A. V., Mainou, B. A., Konz, J. O., Fix, A., … Clarke, E. (2024). Safety of the novel oral poliovirus vaccine type 2 (nOPV2) in infants and young children aged 1 to <5 years and lot-to-lot consistency of the immune response to nOPV2 in infants in The Gambia: A phase 3, double-blind, randomised controlled trial. The Lancet, 403(10432), 1164–1175. [https://doi.org/10.1016/S0140-6736(23)02844-1](https://doi.org/10.1016/S0140-6736(23)02844-1) Bandyopadhyay, A. S., Cooper, L. V., & Zipursky, S. (2024). One billion doses and WHO prequalification of nOPV2: Implications for the global polio situation and beyond. PLOS Global Public Health, 4(2), e0002920. [https://doi.org/10.1371/journal.pgph.0002920](https://doi.org/10.1371/journal.pgph.0002920) Yeh, M. T., Smith, M., Carlyle, S., Konopka-Anstadt, J. L., Burns, C. C., Konz, J., Andino, R., & Macadam, A. (2023). Genetic stabilization of attenuated oral vaccines against poliovirus types 1 and 3. Nature, 619(7968), 135–142. [https://doi.org/10.1038/s41586-023-06212-3](https://doi.org/10.1038/s41586-023-06212-3) Kumar, P., Bird, C., Holland, D., Joshi, S. B., & Volkin, D. B. (2022). Current and next-generation formulation strategies for inactivated polio vaccines to lower costs, increase coverage, and facilitate polio eradication. Human Vaccines & Immunotherapeutics, 18(7), 2154100. [https://doi.org/10.1080/21645515.2022.2154100](https://doi.org/10.1080/21645515.2022.2154100)",New polio vaccines are key to preventing outbreaks and achieving eradication 1pwTrbEKOy4D3LPbpbNaivQyYVv2KTePpw-P4F5fMNjA,details-on-demand,fragment,"{""toc"": [], ""body"": [], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""fragment"", ""title"": ""Details on Demand"", ""byline"": ""Our World in Data team"", ""authors"": [""Our World in Data team""], ""details"": [{""id"": ""centroid"", ""text"": [{""type"": ""text"", ""value"": ""Centroid""}, {""type"": ""text"", ""value"": ""The centroid of a country is the geometric center of its shape. It is the point that represents the average position of all the points within the country’s borders, often referred to as the “center of mass” of the country’s area. The centroid is used in geographic analysis to find a central point that summarizes the country’s location, although it may not always align with the intuitive geographic center due to the country’s shape and geographic features.""}]}, {""id"": ""intact-forest-landscapes"", ""text"": [{""type"": ""text"", ""value"": ""Intact Forest Landscapes (IFL)""}, {""type"": ""text"", ""value"": ""Intact Forest Landscapes (IFL) are defined as territories within the global extent of forest cover which contains forest and non-forest ecosystems minimally influenced by human economic activity, with an area of at least 500 km2 and a minimal width of 10 km.""}, {""type"": ""text"", ""value"": ""Areas with evidence of certain types of human influence are considered disturbed and consequently not eligible for inclusion in an IFL:""}, {""type"": ""text"", ""value"": ""- Settlements (including a buffer zone of 1 km);""}, {""type"": ""text"", ""value"": ""- Infrastructure used for transportation between settlements or for industrial development of natural resources, including roads (except unpaved trails), railways, navigable waterways (including seashore), pipelines, and power transmission lines (including in all cases a buffer zone of 1 km on either side);""}, {""type"": ""text"", ""value"": ""- Agriculture and timber production;""}, {""type"": ""text"", ""value"": ""- Industrial activities during the last 30-70 years, such as logging, mining, oil and gas exploration and extraction, peat extraction, etc.""}, {""type"": ""text"", ""value"": ""Areas with evidence of low-intensity disturbances are eligible for inclusion in an IFL. For example, local shifting cultivation activities, diffuse grazing by domestic animals, low-intensity selective logging, and hunting.""}]}, {""id"": ""plugin_hybrid"", ""text"": [{""type"": ""text"", ""value"": ""Plug-in hybrid""}, {""type"": ""text"", ""value"": ""Cars or other vehicles that have a rechargeable battery and electric motor, and an internal combustion engine.""}, {""type"": ""text"", ""value"": ""The battery in plug-in hybrids is smaller and has a shorter range than battery-electric cars, so over longer distances, the car starts running on gasoline once the battery has run out.""}]}, {""id"": ""battery_electric"", ""text"": [{""type"": ""text"", ""value"": ""Fully battery-electric""}, {""type"": ""text"", ""value"": ""Cars or other vehicles that are powered entirely by an electric motor and battery, instead of an internal combustion engine.""}]}, {""id"": ""trade-openness-index"", ""text"": [{""type"": ""text"", ""value"": ""Trade openness index""}, {""type"": ""text"", ""value"": ""The trade openness index represents the ratio of total trade (exports plus imports) to economic output. The higher the index, the greater the influence of trade transactions on economic activity.""}, {""type"": ""text"", ""value"": ""An important consideration is that the openness index, when calculated for the world as a whole, includes double-counting of transactions: when country A sells goods to country B, this shows up in the data both as an import (B imports from A) and as an export (A sells to B).""}, {""type"": ""text"", ""value"": ""If you compare a chart showing the global trade openness index and one showing global merchandise exports as a share of GDP, you find that the former is almost twice as large as the latter.""}, {""type"": ""text"", ""value"": ""Why is the global openness index not exactly twice the value reported in the chart plotting global merchandise exports? There are three reasons.""}, {""type"": ""text"", ""value"": ""First, the global openness index uses different sources. Second, the global openness index includes trade in goods and services, while merchandise exports include goods but not services. And third, the amount that country A reports exporting to country B does not always match what B reports importing from A.""}, {""type"": ""text"", ""value"": ""We explore this in more detail in the “Measurement” section of our page on trade and globalization.""}]}, {""id"": ""gdp"", ""text"": [{""type"": ""text"", ""value"": ""Gross domestic product""}, {""type"": ""text"", ""value"": ""Gross Domestic Product (GDP) is a measure of a country's economic performance. It represents the total monetary value of all goods and services produced within a nation’s borders over a specific time period, typically annually or quarterly. GDP includes consumption, government spending, investments, and net exports (exports minus imports).""}, {""type"": ""text"", ""value"": ""GDP is used to gauge the health of an economy, with increases indicating growth and decreases signaling contraction. Policymakers, economists, and analysts use GDP to make informed decisions and comparisons between countries. It can be measured in nominal terms or adjusted for inflation to reflect real GDP.""}]}, {""id"": ""employment_agriculture"", ""text"": [{""type"": ""text"", ""value"": ""Employment in agriculture""}, {""type"": ""text"", ""value"": ""The agriculture sector includes activities in agriculture, hunting, forestry, and fishing.""}]}, {""id"": ""employment_industry"", ""text"": [{""type"": ""text"", ""value"": ""Employment in industry""}, {""type"": ""text"", ""value"": ""The industry sector includes mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water).""}]}, {""id"": ""employment_services"", ""text"": [{""type"": ""text"", ""value"": ""Employment in services""}, {""type"": ""text"", ""value"": ""The services sector includes wholesale and retail trade; restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services.""}]}, {""id"": ""incidence_prevalence"", ""text"": [{""type"": ""text"", ""value"": ""Incidence and prevalence""}, {""type"": ""text"", ""value"": ""Incidence refers to new cases of a condition within a given time period, such as a year.""}, {""type"": ""text"", ""value"": ""Prevalence refers to the total cases of a condition, which includes both new and existing cases.""}]}, {""id"": ""primaryenergy"", ""text"": [{""type"": ""text"", ""value"": ""Primary energy""}, {""type"": ""text"", ""value"": ""Primary energy is the energy available as resources – such as the fuels burnt in power plants – before it has been transformed. This relates to the coal before it has been burned, the uranium, or the barrels of oil.""}, {""type"": ""text"", ""value"": ""Primary energy includes energy that the end user needs, in the form of electricity, transport and heating, plus inefficiencies and energy that is lost when raw resources are transformed into a usable form.""}, {""type"": ""text"", ""value"": ""You can read more on the different ways of measuring energy in our article.""}]}, {""id"": ""substitutionmethod"", ""text"": [{""type"": ""text"", ""value"": ""Substitution method""}, {""type"": ""text"", ""value"": ""The ‘substitution method’ is used by researchers to correct primary energy consumption for efficiency losses experienced by fossil fuels. It tries to adjust non-fossil energy sources to the inputs that would be needed if it was generated from fossil fuels. It assumes that wind and solar electricity is as inefficient as coal or gas.""}, {""type"": ""text"", ""value"": ""To do this, energy generation from non-fossil sources are divided by a standard ‘thermal efficiency factor’ – typically around 0.4""}, {""type"": ""text"", ""value"": ""Nuclear power is also adjusted despite it also experiencing thermal losses in a power plant. Since it’s reported in terms of electricity output, we need to do this adjustment to calculate its equivalent input value.""}, {""type"": ""text"", ""value"": ""You can read more about this adjustment in our article.""}]}, {""id"": ""foodinsecurity"", ""text"": [{""type"": ""text"", ""value"": ""Food insecurity""}, {""type"": ""text"", ""value"": ""Food insecurity is defined by the Food and Agriculture Organization (FAO) of the United Nations as the “situation when people lack secure access to sufficient amounts of safe and nutritious food for normal growth and development and an active and healthy life.”""}, {""type"": ""text"", ""value"": ""It is measured using the Food Insecurity Experience Scale (FIES). This is based on household survey data about several conditions someone with food insecurity would typically experience.""}, {""type"": ""text"", ""value"": ""Moderate food insecurity is generally associated with the inability to regularly eat healthy, nutritious diets.""}, {""type"": ""text"", ""value"": ""Severe food insecurity is more strongly related to insufficient food (energy).""}, {""type"": ""text"", ""value"": ""You can read more about this in our article.""}]}, {""id"": ""stunting"", ""text"": [{""type"": ""text"", ""value"": ""Childhood stunting""}, {""type"": ""text"", ""value"": ""Stunting is one of the leading measures used to assess childhood malnutrition. It indicates that a child has failed to reach their growth potential due to disease, poor health, and malnutrition.""}, {""type"": ""text"", ""value"": ""A child is defined as ‘stunted’ if they are too short for their age. This indicates that their growth and development have been hindered.""}, {""type"": ""text"", ""value"": ""Stunting is measured based on a child’s height relative to their age.""}, {""type"": ""text"", ""value"": ""Stunting is the share of children under five years old that fall two standard deviations below the expected height for their age.""}, {""type"": ""text"", ""value"": ""You can read more about this in our article.""}]}, {""id"": ""standard-deviation"", ""text"": [{""type"": ""text"", ""value"": ""Standard deviation""}, {""type"": ""text"", ""value"": ""Standard deviations measure the spread of values in a set.""}, {""type"": ""text"", ""value"": ""They indicate how much individual values deviate from the average of the dataset.""}, {""type"": ""text"", ""value"": ""In a normal distribution, about 68% of the data points fall within one standard deviation of the mean in both directions. This means that one standard deviation defines the range around the mean that contains approximately 68% of the values. Similarly, two standard deviations defines the range around the mean that contains approximately 95% of the values.""}]}, {""id"": ""wasting"", ""text"": [{""type"": ""text"", ""value"": ""Childhood wasting""}, {""type"": ""text"", ""value"": ""‘Wasting’ is one of the key indicators used to assess the prevalence of childhood malnutrition.""}, {""type"": ""text"", ""value"": ""A child is defined as ‘wasted’ if their weight is too low for their height.""}, {""type"": ""text"", ""value"": ""Wasting is often referred to as acute malnutrition. It is a sign that a child has experienced short periods of undernutrition, resulting in significant wastage of muscle and fat tissue.""}, {""type"": ""text"", ""value"": ""Wasting is measured based on a child’s weight relative to their height.""}, {""type"": ""text"", ""value"": ""In a population, the prevalence of wasting is defined as the share of child under five years old that fall two standard deviations below the expected weight for their height.""}, {""type"": ""text"", ""value"": ""You can read more about this in our article.""}]}, {""id"": ""sendai"", ""text"": [{""type"": ""text"", ""value"": ""Sendai Framework for Disaster Risk Reduction""}, {""type"": ""text"", ""value"": ""The Sendai Framework for Disaster Risk Reduction is a 15-year global agreement that was adopted by United Nations member states in 2015 to manage and reduce disaster risks.""}, {""type"": ""text"", ""value"": ""It aims to substantially reduce disaster risks and losses in lives, livelihoods, health and in the economic, physical, social, cultural and environmental assets of persons, businesses, communities and countries.""}, {""type"": ""text"", ""value"": ""It outlines 4 priorities for action: (1) understanding disaster risk, (2) strengthening disaster risk governance to manage disaster risk, (3) investing in disaster risk reduction for resilience, and (4) enhancing disaster preparedness for effective response and recovery.""}]}, {""id"": ""dobsonunit"", ""text"": [{""type"": ""text"", ""value"": ""Dobson unit (DU)""}, {""type"": ""text"", ""value"": ""Dobson Units (DU) are used to measure the concentration of ozone in the atmosphere.""}, {""type"": ""text"", ""value"": ""One Dobson Unit is the number of molecules of ozone that would be required to create a layer of pure ozone 0.01 millimeters thick at a temperature of 0 degrees Celsius and a pressure of 1 atmosphere.""}]}, {""id"": ""inadequatelymanagedwaste"", ""text"": [{""type"": ""text"", ""value"": ""Inadequately disposed plastic waste""}, {""type"": ""text"", ""value"": ""Inadequately disposed plastic waste is not formally managed and includes disposal in dumps or open, uncontrolled landfills, where it is not fully contained.""}, {""type"": ""text"", ""value"": ""This makes it at a much higher risk of leaking into the natural environment, rivers, or the ocean.""}]}, {""id"": ""peacekeeping"", ""text"": [{""type"": ""text"", ""value"": ""Peacekeeping mission""}, {""type"": ""text"", ""value"": ""UN peacekeeping operations are meant to maintain peace and security; to facilitate the political process; to protect civilians; to assist in the disarmament, demobilization and reintegration of former combatants; to support the organization of elections; and to protect and promote human rights and assist in restoring the rule of law.""}, {""type"": ""text"", ""value"": ""Operations are based on the principles of consent of the parties, impartiality, and the non-use of force except in self-defence and defence of the mandate.""}]}, {""id"": ""flop"", ""text"": [{""type"": ""text"", ""value"": ""Floating-point operation""}, {""type"": ""text"", ""value"": ""A floating-point operation (FLOP) is a type of computer operation. One FLOP represents a single arithmetic operation involving floating-point numbers, such as addition, subtraction, multiplication, or division.""}]}, {""id"": ""dalys"", ""text"": [{""type"": ""text"", ""value"": ""Disability-adjusted life years""}, {""type"": ""text"", ""value"": ""Disability-adjusted life years (DALYs) measure the total burden of disease – both from years of life lost due to premature death and years lived with a disability. One DALY equals one year of healthy life.""}, {""type"": ""text"", ""value"": ""📄 Learn more about how the burden of disease is measured in our article.""}]}, {""id"": ""fossilemissions"", ""text"": [{""type"": ""text"", ""value"": ""Fossil emissions""}, {""type"": ""text"", ""value"": ""Fossil emissions measure the quantity of carbon dioxide (CO₂) emitted from the burning of fossil fuels, and directly from industrial processes such as cement and steel production.""}, {""type"": ""text"", ""value"": ""Fossil CO₂ includes emissions from coal, oil, gas, flaring, cement, steel, and other industrial processes.""}, {""type"": ""text"", ""value"": ""Fossil emissions do not include land use change, deforestation, soils, or vegetation.""}]}, {""id"": ""consumptionbasedemissions"", ""text"": [{""type"": ""text"", ""value"": ""Consumption-based emissions""}, {""type"": ""text"", ""value"": ""Consumption-based emissions are national or regional emissions that have been adjusted for trade.""}, {""type"": ""text"", ""value"": ""They are calculated as domestic (or ‘production-based’ emissions) emissions minus the emissions generated in the production of goods and services that are exported to other countries or regions, plus emissions from the production of goods and services that are imported.""}, {""type"": ""text"", ""value"": ""Consumption-based emissions = Production-based – Exported + Imported emissions""}]}, {""id"": ""gwp"", ""text"": [{""type"": ""text"", ""value"": ""Global warming potential""}, {""type"": ""text"", ""value"": ""Global warming potential (GWP) measures the amount of heat absorbed by a greenhouse gas relative to the same mass of carbon dioxide (CO₂). It measures the amount of warming a gas creates compared to CO₂.""}, {""type"": ""text"", ""value"": ""Carbon dioxide is given a GWP value of one. If a gas had a GWP of 10 then one kilogram of that gas would generate ten times the warming effect as one kilogram of CO₂.""}, {""type"": ""text"", ""value"": ""Since greenhouse gases spend different amounts of time in the atmosphere, their global warming potential depends on the length of time that it’s measured over. For example, GWP can be measured as the warming effect over 20 years, 50 years, or 100 years.""}, {""type"": ""text"", ""value"": ""Potent but short-lived greenhouse gases – like methane, for example – will have a higher GWP when measured over 20 years than over 100 years.""}, {""type"": ""text"", ""value"": ""The GWP value for methane over 100 years (GWP100) is 28. This means one kilogram of methane would cause 28 times the warming of one kilogram of CO₂.""}]}, {""id"": ""carbondioxideequivalents"", ""text"": [{""type"": ""text"", ""value"": ""Carbon dioxide equivalents (CO₂eq)""}, {""type"": ""text"", ""value"": ""Carbon dioxide is the most important greenhouse gas, but not the only one. To capture all greenhouse gas emissions, researchers express them in “carbon dioxide equivalents” (CO₂eq). This takes all greenhouse gases into account, not just CO₂.""}, {""type"": ""text"", ""value"": ""To express all greenhouse gases in carbon dioxide equivalents (CO₂eq), each one is weighted by its global warming potential (GWP) value. GWP measures the amount of warming a gas creates compared to CO₂. CO₂ is given a GWP value of one. If a gas had a GWP of 10 then one kilogram of that gas would generate ten times the warming effect as one kilogram of CO₂.""}, {""type"": ""text"", ""value"": ""Carbon dioxide equivalents are calculated for each gas by multiplying the mass of emissions of a specific greenhouse gas by its GWP factor.""}, {""type"": ""text"", ""value"": ""This warming can be stated over different timescales. To calculate CO₂eq over 100 years, we’d multiply each gas by its GWP over a 100-year timescale (GWP100).""}, {""type"": ""text"", ""value"": ""Total greenhouse gas emissions – measured in CO₂eq – are then calculated by summing each gas’ CO₂eq value.""}]}, {""id"": ""carbonopportunitycost"", ""text"": [{""type"": ""text"", ""value"": ""Carbon opportunity cost""}, {""type"": ""text"", ""value"": ""Carbon opportunity cost of foods and crops is the amount of carbon lost from native vegetation and soils in order to produce it.""}, {""type"": ""text"", ""value"": ""When we use land for agriculture – to grow crops, raise livestock, or produce biofuels – we give up the opportunity for that land to be natural vegetation, such as grassland, forest, shrub, or peatland. This would sequester more carbon in its soils and vegetation than using the land for agriculture.""}, {""type"": ""text"", ""value"": ""We describe this foregone carbon sequestration as a ‘carbon opportunity cost’. It measures the amount of carbon that would be captured if we stopped using the land for agriculture and allowed natural vegetation to regrow.""}]}, {""id"": ""radiativeforcing"", ""text"": [{""type"": ""text"", ""value"": ""Radiative forcing""}, {""type"": ""text"", ""value"": ""Radiative forcing measures the difference between incoming energy and the energy radiated back to space. If more energy is absorbed than radiated, the atmosphere becomes warmer.""}]}, {""id"": ""foodmiles"", ""text"": [{""type"": ""text"", ""value"": ""Food miles""}, {""type"": ""text"", ""value"": ""Food miles are measured in tonne-kilometers which represent the transport of one tonne of food over a distance of one kilometer.""}, {""type"": ""text"", ""value"": ""Food miles are usually expressed by the mode of travel, such as road, rail, air, or boat.""}]}, {""id"": ""lca"", ""text"": [{""type"": ""text"", ""value"": ""Life cycle analysis""}, {""type"": ""text"", ""value"": ""Life cycle analysis (LCA) is a method used by researchers to quantify the environmental impact of products over their entire lifetime.""}, {""type"": ""text"", ""value"": ""Environmental impacts are often measured for goods at their use stage – the amount of electricity needed to power a lightbulb or the emissions from the tailpipe of a car.""}, {""type"": ""text"", ""value"": ""Life cycle analyses also quantify the environmental impact upstream and downstream of this stage. It often includes the extraction and processing of raw materials, energy used in manufacturing, processing, transport, packaging, its use, and waste disposal.""}, {""type"": ""text"", ""value"": ""In the case of a car, it might include the environmental impact of the mining of raw materials to build the car, the energy used in manufacturing, distribution to the seller, customer use, and its disposal at the end of its life. The parts of the supply chain that LCAs cover can vary. Some may cover the full supply chain to disposal. Some may only cover impacts up to the retailer.""}]}, {""id"": ""ghg"", ""text"": [{""type"": ""text"", ""value"": ""Greenhouse gas""}, {""type"": ""text"", ""value"": ""A greenhouse gas (GHG) is a gas that causes the atmosphere to warm by absorbing and emitting radiant energy. Greenhouse gases absorb radiation that is radiated by Earth, preventing this heat from escaping to space.""}, {""type"": ""text"", ""value"": ""Carbon dioxide (CO₂) is the most well-known greenhouse gas, but there are others including methane, nitrous oxide, and in fact, water vapor.""}, {""type"": ""text"", ""value"": ""Human-made emissions of greenhouse gases from fossil fuels, industry, and agriculture are the leading cause of global climate change.""}]}, {""id"": ""ghgemissions"", ""text"": [{""type"": ""text"", ""value"": ""Greenhouse gas emissions""}, {""type"": ""text"", ""value"": ""A greenhouse gas (GHG) is a gas that causes the atmosphere to warm by absorbing and emitting radiant energy. Greenhouse gases absorb radiation that is radiated by Earth, preventing this heat from escaping to space.""}, {""type"": ""text"", ""value"": ""Carbon dioxide (CO₂) is the most well-known greenhouse gas, but there are others including methane, nitrous oxide, and in fact, water vapor.""}, {""type"": ""text"", ""value"": ""Human-made emissions of greenhouse gases from fossil fuels, industry, and agriculture are the leading cause of global climate change.""}, {""type"": ""text"", ""value"": ""Greenhouse gas emissions measure the total amount of all greenhouse gases that are emitted. These are often quantified in carbon dioxide equivalents (CO₂eq) which take account of the amount of warming that each molecule of different gases creates.""}]}, {""id"": ""hiv"", ""text"": [{""type"": ""text"", ""value"": ""HIV""}, {""type"": ""text"", ""value"": ""The human immunodeficiency virus (HIV) is a virus that is transmitted through sex, blood transfer, or from mother to child during pregnancy, childbirth or breastfeeding. It targets immune cells that help the body respond to infection, which gradually results in AIDS (acquired immunodeficiency syndrome).""}, {""type"": ""text"", ""value"": ""Without effective treatment, the immune system will become weakened to the point that it can no longer fight infection and disease.""}]}, {""id"": ""hiv_aids"", ""text"": [{""type"": ""text"", ""value"": ""HIV/AIDS""}, {""type"": ""text"", ""value"": ""Acquired immunodeficiency syndrome (AIDS) is a condition that describes the most advanced stages of HIV infection. It is defined by the occurrence of at least one of more than 20 life-threatening cancers or “opportunistic infections” that can take advantage of a weakened immune system.""}]}, {""id"": ""antiretroviral_therapy"", ""text"": [{""type"": ""text"", ""value"": ""Antiretroviral therapy""}, {""type"": ""text"", ""value"": ""Antiretroviral therapy (ART) is a long-term medical treatment for HIV/AIDS. It works by suppressing the virus from multiplying in the body. This keeps the infection under control and helps to prevent the disease from progressing.""}]}, {""id"": ""int_dollar_abbreviation"", ""text"": [{""type"": ""text"", ""value"": ""International dollars""}, {""type"": ""text"", ""value"": ""International dollars are a hypothetical currency that is used to make meaningful comparisons of monetary indicators of living standards.""}, {""type"": ""text"", ""value"": ""Figures expressed in international dollars are adjusted for inflation within countries over time, and for differences in the cost of living between countries.""}, {""type"": ""text"", ""value"": ""The goal of such adjustments is to provide a unit whose purchasing power is held fixed over time and across countries, such that one international dollar can buy the same quantity and quality of goods and services no matter where or when it is spent.""}, {""type"": ""text"", ""value"": ""Read more in our article: What are Purchasing Power Parity adjustments and why do we need them?""}]}, {""id"": ""icd"", ""text"": [{""type"": ""text"", ""value"": ""International Classification of Diseases""}, {""type"": ""text"", ""value"": ""The International Classification of Diseases (ICD) is a manual used by many countries to classify diseases, injuries and causes of death into standard codes. These can be helpful for researchers to analyze data on diseases and causes of death over time and across countries.""}, {""type"": ""text"", ""value"": ""Each ICD code represents a specific disease, injury, or cause of death.""}, {""type"": ""text"", ""value"": ""The ICD manual has been updated several times in history, to reflect advances in medical knowledge and cultural context, so that diseases and deaths can be analyzed in a consistent manner. The ICD is organized by the World Health Organization (WHO).""}]}, {""id"": ""malaria"", ""text"": [{""type"": ""text"", ""value"": ""Malaria""}, {""type"": ""text"", ""value"": ""Malaria is a life-threatening disease caused by parasites that are transmitted by female Anopheles mosquitoes. There are five parasite species that cause malaria in humans. Two of these species – P. falciparum and P. vivax – pose the greatest threat.""}, {""type"": ""text"", ""value"": ""The first symptoms – fever, headache and chills – usually appear 10 to 15 days after the infective mosquito bite and may be mild and difficult to recognize as malaria. Left untreated, P. falciparum malaria can progress to severe illness and death within 24 hours.""}, {""type"": ""text"", ""value"": ""📖 Read more on our page on malaria.""}]}, {""id"": ""meningitis"", ""text"": [{""type"": ""text"", ""value"": ""Meningitis""}, {""type"": ""text"", ""value"": ""Meningitis is a serious infection of the meninges, the membranes covering the brain and spinal cord. The disease can be caused by many different pathogens including bacteria, fungi or viruses, but the highest global burden is seen with bacterial meningitis.""}, {""type"": ""text"", ""value"": ""Meningitis can infect people of any age, but mainly affects babies, young children and young people. The disease can occur in a range of situations including sporadic cases, small clusters, or large epidemics throughout the world, with seasonal variations.""}]}, {""id"": ""polio"", ""text"": [{""type"": ""text"", ""value"": ""Polio""}, {""type"": ""text"", ""value"": ""Poliomyelitis, often called polio, is a highly infectious viral disease that largely affects children under five years of age.""}, {""type"": ""text"", ""value"": ""The virus is transmitted by person-to-person spread, mainly through the fecal-oral route or, less frequently, by a common vehicle (e.g. contaminated water or food). It then multiplies in the intestine, where it can invade the nervous system and cause paralysis.""}, {""type"": ""text"", ""value"": ""Paralytic polio refers to cases of paralytic disease, and does not include infections that lack symptoms.""}, {""type"": ""text"", ""value"": ""📖 Read more on our page on polio.""}]}, {""id"": ""wild-poliovirus"", ""text"": [{""type"": ""text"", ""value"": ""Wild poliovirus (WPV)""}, {""type"": ""text"", ""value"": ""Wild poliovirus refers to polio viruses that have come from the environment.""}, {""type"": ""text"", ""value"": ""There are three serotypes of wild poliovirus: wild poliovirus 1, 2 and 3.""}, {""type"": ""text"", ""value"": ""Two of the three serotypes have already been eradicated worldwide.""}, {""type"": ""text"", ""value"": ""The last case of wild poliovirus serotype 2 was seen in 1999 in India. It was declared globally eradicated by the WHO in 2015.""}, {""type"": ""text"", ""value"": ""The last case of wild poliovirus serotype 3 was seen in 2012 in Nigeria and declared eradicated in 2019.""}, {""type"": ""text"", ""value"": ""📖 Read more on our page on polio.""}]}, {""id"": ""vaccine-derived-poliovirus"", ""text"": [{""type"": ""text"", ""value"": ""Vaccine-derived poliovirus (VDPV)""}, {""type"": ""text"", ""value"": ""Vaccine-derived poliovirus refers to polio viruses that have come from Oral Polio Vaccines (OPV) in rare circumstances.""}, {""type"": ""text"", ""value"": ""There are three serotypes of vaccine-derived poliovirus: vaccine-derived poliovirus 1, 2 and 3.""}, {""type"": ""text"", ""value"": ""These arise the virus used in the oral poliovirus vaccine can, in very rare circumstances, evolve mutations that allow it to cause disease.""}, {""type"": ""text"", ""value"": ""If a population has low immunity to polio, because of low rates of vaccination, these vaccine-derived polioviruses can spread more easily and cause an outbreak.""}, {""type"": ""text"", ""value"": ""Since 2021, the world has a new version of the oral poliovirus vaccine called the “novel Oral Polio Vaccine” (nOPV) that is more genetically stable than the previous vaccine, and can prevent outbreaks of vaccine-derived poliovirus.""}, {""type"": ""text"", ""value"": ""Vaccine-derived polioviruses contrast with wild poliovirus.""}, {""type"": ""text"", ""value"": ""📖 Read more on our page on polio.""}]}, {""id"": ""oral-polio-vaccine"", ""text"": [{""type"": ""text"", ""value"": ""Oral polio vaccine (OPV)""}, {""type"": ""text"", ""value"": ""Oral polio vaccines are a type of vaccine against polio. They are given orally, into the mouth, through a drop containing the vaccine.""}, {""type"": ""text"", ""value"": ""The vaccine contains a live “attenuated” version of the virus. This means the virus has been forced to evolve in a way that makes it mild and prevents it from causing paralysis — but still able to stimulate our immune system, which allows us to recognize the original virus.""}, {""type"": ""text"", ""value"": ""Oral polio vaccines (OPV) contrast with inactivated polio vaccines (IPV).""}, {""type"": ""text"", ""value"": ""📖 Read more on our page on polio.""}]}, {""id"": ""inactivated-polio-vaccine"", ""text"": [{""type"": ""text"", ""value"": ""Inactivated polio vaccine (IPV)""}, {""type"": ""text"", ""value"": ""Inactivated polio vaccines are a type of vaccine against polio. They are typically given through an injection into muscle or skin.""}, {""type"": ""text"", ""value"": ""The vaccine contains “inactivated” versions of the virus, which means the virus has been made inert by chemical compounds, which prevents it from causing disease or spreading.""}, {""type"": ""text"", ""value"": ""This inactivated virus still stimulates our immune system, which allows us to recognize the original virus.""}, {""type"": ""text"", ""value"": ""Inactivated polio vaccines (IPV) contrast with oral polio vaccines (OPV).""}, {""type"": ""text"", ""value"": ""📖 Read more on our page on polio.""}]}, {""id"": ""pneumonia"", ""text"": [{""type"": ""text"", ""value"": ""Pneumonia""}, {""type"": ""text"", ""value"": ""Pneumonia describes a condition of the inflammation of the lungs, specifically in the alveoli, which are millions of tiny air sacs that help us take in oxygen.""}, {""type"": ""text"", ""value"": ""In pneumonia, these alveoli become filled with pus and fluid, which makes breathing painful and reduces our ability to take in oxygen from the air we breathe and exhale carbon dioxide. Pneumonia can develop from a range of different infections, which are caused by different pathogens, including viruses, bacteria, and fungi.""}, {""type"": ""text"", ""value"": ""This includes, for example, Streptococcus pneumoniae, Haemophilus influenzae, Staphylococcus aureus, influenza (flu), respiratory syncytial virus (RSV), COVID-19, and more.""}, {""type"": ""text"", ""value"": ""Estimates of pneumonia globally tend to be based on a clinical definition of the condition, because of a lack of diagnosis and testing. The clinical definition refers to when people develop symptoms including fast breathing and coughing, and may include other lower respiratory tract infections.""}, {""type"": ""text"", ""value"": ""Read more on our page on Pneumonia.""}]}, {""id"": ""respiratory-infections"", ""text"": [{""type"": ""text"", ""value"": ""Respiratory infections""}, {""type"": ""text"", ""value"": ""Respiratory infections are diseases caused by pathogens that affect the nose, throat, lungs, or related organs. This includes middle ear infections, common colds, influenza, pneumonia, SARS, and COVID-19, among others.""}]}, {""id"": ""pathogen"", ""text"": [{""type"": ""text"", ""value"": ""Pathogen""}, {""type"": ""text"", ""value"": ""A pathogen is a microorganism – such as a bacterium, virus, or fungus – that can cause disease.""}]}, {""id"": ""parasitic-infection"", ""text"": [{""type"": ""text"", ""value"": ""Parasitic infection""}, {""type"": ""text"", ""value"": ""A parasitic disease is an illness caused by a parasite, which are organisms that live in or on another organism (the ‘host’). Parasites can be microscopic, like bacteria or protozoa, or larger, like worms and insects.""}, {""type"": ""text"", ""value"": ""Parasitic diseases include malaria, giardiasis, toxoplasmosis, hookworm, roundworm, schistosomiasis, and more.""}]}, {""id"": ""schistosomiasis"", ""text"": [{""type"": ""text"", ""value"": ""Schistosomiasis""}, {""type"": ""text"", ""value"": ""Schistosomiasis is a disease caused by parasitic flatworms called schistosomes. These parasitic worms live in some types of freshwater snails.""}, {""type"": ""text"", ""value"": ""They can cause infection when larval forms of the parasite enter the body through the skin. The parasite can remain in the body for years, damaging organs like the bladder, kidneys, and liver, causing long-term effects such as infertility. It can also affect people’s ability to work, leading to economic impacts.""}, {""type"": ""text"", ""value"": ""Eggs of the parasite can then be spread to other people by contaminating water.""}, {""type"": ""text"", ""value"": ""Schistosomiasis can be controlled through large-scale preventive treatment, clean water and sanitation, and snail control.""}, {""type"": ""text"", ""value"": ""It is classified as a neglected tropical disease.""}]}, {""id"": ""preventive-chemotherapy"", ""text"": [{""type"": ""text"", ""value"": ""Preventive chemotherapy""}, {""type"": ""text"", ""value"": ""Preventive Chemotherapy (PC) is when people are given medicines that aim to reduce or prevent certain diseases.""}, {""type"": ""text"", ""value"": ""This is often used as a public health strategy against a number of neglected tropical diseases — including lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiasis and trachoma — where inexpensive medications are given to large populations who are at risk.""}, {""type"": ""text"", ""value"": ""Preventive chemotherapy is effective against these diseases because they tend to develop slowly and have symptoms that aren’t easily distinguished from other diseases, meaning that diagnosis and targeted treatment to individuals can be difficult to provide, especially in poorer regions.""}]}, {""id"": ""unintentional-injuries"", ""text"": [{""type"": ""text"", ""value"": ""Unintentional injury""}, {""type"": ""text"", ""value"": ""Unintentional injuries are accidents or mishaps which lead to physical harm to a person. They can include falls, traffic accidents, burns, poisonings, drowning, and various other incidents that result from unforeseen circumstances.""}]}, {""id"": ""alcohol_use"", ""text"": [{""type"": ""text"", ""value"": ""Alcohol use""}, {""type"": ""text"", ""value"": ""Alcohol use is the consumption of alcoholic beverages. It is associated with health risks including mental and behavioral disorders, alcohol dependence, and major noncommunicable diseases such as liver cirrhosis, cancers, and cardiovascular diseases.""}]}, {""id"": ""alcohol_use_disorder"", ""text"": [{""type"": ""text"", ""value"": ""Alcohol use disorder""}, {""type"": ""text"", ""value"": ""Alcohol use disorders are medical conditions characterized by an impaired ability to stop or control alcohol use despite adverse social, occupational, or health consequences.""}, {""type"": ""text"", ""value"": ""📘 Find out how alcohol use disorders are diagnosed in the International Classification of Diseases manual.""}]}, {""id"": ""substance_use_disorder"", ""text"": [{""type"": ""text"", ""value"": ""Substance use disorder""}, {""type"": ""text"", ""value"": ""Substance use disorders are a wide range of conditions defined by the ICD as being related to the use of psychologically active substances.""}, {""type"": ""text"", ""value"": ""📘 Find out how each condition is diagnosed in the International Classification of Diseases manual.""}]}, {""id"": ""alzheimers"", ""text"": [{""type"": ""text"", ""value"": ""Alzheimer's""}, {""type"": ""text"", ""value"": ""Alzheimer's disease is the most common form of dementia. Dementia patients show worsening cognitive function over time, beyond what might be expected from typical aging.""}, {""type"": ""text"", ""value"": ""Dementia affects memory, thinking, orientation, comprehension, calculation, learning capacity, language, and judgment. This is commonly accompanied by changes in mood, emotional control, behavior, or motivation.""}]}, {""id"": ""cancer"", ""text"": [{""type"": ""text"", ""value"": ""Cancer""}, {""type"": ""text"", ""value"": ""Cancer describes a group of diseases in which abnormal cells in the body begin to grow and multiply uncontrollably. These cells can form lumps of tissue called tumors, which can interfere with normal bodily functions. Cancerous cells have the potential to spread to other parts of the body (this process is called “metastasis”), disrupting normal processes and causing serious health problems.""}]}, {""id"": ""cvd"", ""text"": [{""type"": ""text"", ""value"": ""Cardiovascular disease""}, {""type"": ""text"", ""value"": ""Cardiovascular diseases cover all diseases of the heart and blood vessels – including heart attacks and strokes, atherosclerosis, ischemic heart disease, hypertensive diseases, cardiomyopathy, rheumatic heart disease, and more.""}, {""type"": ""text"", ""value"": ""They tend to develop gradually with age, especially when people have risk factors like high blood pressure, smoking, alcohol use, poor diet, and air pollution.""}]}, {""id"": ""chronic_respiratory_disease"", ""text"": [{""type"": ""text"", ""value"": ""Chronic respiratory disease""}, {""type"": ""text"", ""value"": ""Chronic respiratory disease refers to a group of long-term conditions that affect the lungs and airways, making it difficult to breathe.""}, {""type"": ""text"", ""value"": ""This includes conditions such as chronic obstructive pulmonary disease (COPD), asthma, and pulmonary fibrosis.""}, {""type"": ""text"", ""value"": ""These diseases often involve inflammation or damage to the airways and lung tissue, leading to symptoms such as coughing, shortness of breath, and reduced lung function. Over time, chronic respiratory diseases can affect the body's ability to get enough oxygen, impacting daily activities and overall well-being.""}]}, {""id"": ""diabetes"", ""text"": [{""type"": ""text"", ""value"": ""Diabetes""}, {""type"": ""text"", ""value"": ""Diabetes is a medical condition characterized by elevated levels of sugar (glucose) in the blood, which is called “hyperglycemia”. This happens when the body has difficulty using or producing a hormone called insulin, which helps regulate blood sugar levels. There are two main types of diabetes:""}, {""type"": ""text"", ""value"": ""Type 1 Diabetes: In this type, the immune system mistakenly attacks and destroys the insulin-producing cells in the pancreas. As a result, people with type 1 diabetes require regular insulin injections or an insulin pump to manage their blood sugar levels. It usually starts in childhood or early adulthood and requires lifelong insulin therapy.""}, {""type"": ""text"", ""value"": ""Type 2 Diabetes: This type is much more common, and is often linked to lifestyle factors such as obesity, poor diet, and lack of physical activity. In type 2 diabetes, the body becomes resistant to the effects of insulin, and the pancreas may not produce enough insulin to maintain normal blood sugar levels. It can often be managed through a combination of dietary changes, exercise, oral medications, and sometimes insulin injections.""}, {""type"": ""text"", ""value"": ""Both types of diabetes can lead to high blood sugar levels, which, if not well managed, can result in various complications affecting the eyes, kidneys, nerves, and blood vessels.""}]}, {""id"": ""digestive_disease"", ""text"": [{""type"": ""text"", ""value"": ""Digestive disease""}, {""type"": ""text"", ""value"": ""Digestive diseases refer to a range of conditions affecting the stomach, intestines, liver, and related organs, which disrupt digestion and nutrient absorption.""}, {""type"": ""text"", ""value"": ""These can include issues like irritable bowel syndrome, peptic ulcer disease, liver cirrhosis, appendicitis, pancreatitis. Symptoms vary but often involve stomach pain, changes in bowel habits, and discomfort. Treatment often involves dietary changes, medications, and lifestyle adjustments.""}]}, {""id"": ""drug_use_disorder"", ""text"": [{""type"": ""text"", ""value"": ""Drug use disorders""}, {""type"": ""text"", ""value"": ""Drug use disorders are the continued use of drugs such as opioids, amphetamines, cocaine, and cannabis leading to impairments in health, social function, and control over substance use.""}]}, {""id"": ""cirrhosis"", ""text"": [{""type"": ""text"", ""value"": ""Chronic liver disease""}, {""type"": ""text"", ""value"": ""Chronic liver disease is a condition where the liver gradually becomes damaged and less able to perform its vital functions – such as the production of clotting factors and other proteins, the detoxification of harmful products of metabolism, and the excretion of bile.\u000b\u000bIt can be caused by factors like excessive alcohol consumption, viral infections like hepatitis, or certain health conditions. As the liver's health declines, it might lead to symptoms such as fatigue, jaundice (yellowing of the skin and eyes), and abdominal discomfort.""}, {""type"": ""text"", ""value"": ""Cirrhosis is the final stage of chronic liver disease that can eventually lead to liver failure, which can be life-threatening.""}]}, {""id"": ""parkinsons"", ""text"": [{""type"": ""text"", ""value"": ""Parkinson's disease""}, {""type"": ""text"", ""value"": ""Parkinson's disease is a brain condition that affects movement control. Symptoms usually begin gradually and worsen over time, as parts of the brain become progressively damaged over many years.""}, {""type"": ""text"", ""value"": ""It arises when certain cells in the brain, responsible for producing a chemical called dopamine, become damaged or die. Dopamine helps regulate muscle movements, and its deficiency in Parkinson's leads to symptoms like tremors (shaking), stiffness, and difficulty with balance and coordination.""}, {""type"": ""text"", ""value"": ""As the disease progresses, it can also bring about changes in speech, sleep problems, depression, memory difficulties, and fatigue. Although there's no cure, treatments like medication and therapies can help manage symptoms and improve quality of life for those with Parkinson's.""}]}, {""id"": ""stroke"", ""text"": [{""type"": ""text"", ""value"": ""Stroke""}, {""type"": ""text"", ""value"": ""A stroke is a sudden medical event that happens when blood flow to a part of the brain is disrupted.""}, {""type"": ""text"", ""value"": ""It can occur due to a blocked blood vessel (ischemic stroke) or a burst blood vessel (hemorrhagic stroke), which can be serious life-threatening medical conditions.""}, {""type"": ""text"", ""value"": ""When brain cells don't receive enough oxygen and nutrients, they can start to die, leading to potentially lasting damage. Symptoms of a stroke can include sudden numbness or weakness on one side of the body, confusion, trouble speaking or understanding, severe headache, and difficulty with coordination.""}]}, {""id"": ""drug_use"", ""text"": [{""type"": ""text"", ""value"": ""Drug use""}, {""type"": ""text"", ""value"": ""Drug use is the use of opioid, amphetamine, cocaine, cannabis and other drugs. The use of opioid, amphetamine, and cocaine is associated with an increased risk of suicide. Use of injected drugs also has an increased risk of blood-borne infections.""}]}, {""id"": ""hypertension"", ""text"": [{""type"": ""text"", ""value"": ""High blood pressure (hypertension)""}, {""type"": ""text"", ""value"": ""High blood pressure, also called hypertension, is blood pressure that is higher than normal. This means the force of the blood against the walls of blood vessels is consistently too high.""}, {""type"": ""text"", ""value"": ""Blood pressure is recorded as two numbers. The first (systolic) represents the pressure in blood vessels when the heart contracts or beats. The second (diastolic) represents the pressure in the vessels when the heart rests between beats.""}, {""type"": ""text"", ""value"": ""High blood pressure is diagnosed if, when it is measured on different days, the systolic blood pressure readings are consistently ≥140 mmHg, or the diastolic blood pressure readings are consistently ≥90 mmHg.""}, {""type"": ""text"", ""value"": ""High blood pressure increases the risk that people will suffer from cardiovascular diseases.""}]}, {""id"": ""hyperglycaemia"", ""text"": [{""type"": ""text"", ""value"": ""High blood sugar""}, {""type"": ""text"", ""value"": ""High blood sugar (hyperglycaemia) is when the level of sugar in the blood is too high. High levels of blood glucose after fasting is a risk factor for diabetes and cardiovascular diseases.""}]}, {""id"": ""garbage-codes"", ""text"": [{""type"": ""text"", ""value"": ""Garbage codes""}, {""type"": ""text"", ""value"": ""“Garbage codes” are ICD codes assigned to causes of death when information like evidence, tests, or medical records are missing.""}, {""type"": ""text"", ""value"": ""Examples include “sudden death”, “chest pain”, and “unspecified sepsis”. These codes are less useful because they often list only the immediate cause or symptoms, not the underlying reason for death.""}, {""type"": ""text"", ""value"": ""Read more in our article: How are causes of death registered around the world?""}]}, {""id"": ""indoor_air_pollution"", ""text"": [{""type"": ""text"", ""value"": ""Indoor air pollution""}, {""type"": ""text"", ""value"": ""Indoor air pollution is generated by the use of inefficient and polluting fuels – such as coal, firewood, crop waste, or dung – in and around the home. These fuels contain a range of health-damaging pollutants, including small particles that penetrate deep into the lungs and enter the bloodstream.""}]}, {""id"": ""low_physical_activity"", ""text"": [{""type"": ""text"", ""value"": ""Low physical activity""}, {""type"": ""text"", ""value"": ""Physical activity is any bodily movement produced by skeletal muscles that requires energy expenditure. It includes walking, cycling, sports, active recreation, and play.""}, {""type"": ""text"", ""value"": ""Regular physical activity is proven to help prevent and manage noncommunicable diseases (NCDs) such as heart diseases, stroke, diabetes and several cancers. It also helps prevent hypertension, maintain healthy body weight and can improve mental health, quality of life and well-being.""}]}, {""id"": ""no_handwashing"", ""text"": [{""type"": ""text"", ""value"": ""No access to handwashing""}, {""type"": ""text"", ""value"": ""Handwashing interrupts the transmission of many diseases, particularly diarrheal diseases and respiratory, skin and eye infections. A lack of access to handwashing facilities means these diseases are more likely to spread.""}]}, {""id"": ""basic-handwashing"", ""text"": [{""type"": ""text"", ""value"": ""Access to basic handwashing""}, {""type"": ""text"", ""value"": ""Access to basic handwashing facilities refers to a device to facilitate handwashing with soap and water available on the premises. Handwashing facilities may be fixed or mobile, and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing.""}]}, {""id"": ""obesity"", ""text"": [{""type"": ""text"", ""value"": ""Obesity""}, {""type"": ""text"", ""value"": ""Obesity is defined as having a body-mass index (BMI) above 30.""}, {""type"": ""text"", ""value"": ""A person’s BMI is calculated as their weight (in kilograms) divided by their height (in meters) squared. For example, someone measuring 1.60 meters and weighing 64 kilograms has a BMI of 64 / 1.6² = 25.""}, {""type"": ""text"", ""value"": ""Obesity increases the mortality risk of many conditions, including cardiovascular disease, gastrointestinal disorders, type 2 diabetes, joint and muscular disorders, respiratory problems, and psychological issues.""}]}, {""id"": ""ring-vaccination"", ""text"": [{""type"": ""text"", ""value"": ""Ring vaccination""}, {""type"": ""text"", ""value"": ""Ring vaccination is a strategy to vaccinate the local contacts of someone who was identified as infected with a disease such as smallpox.""}, {""type"": ""text"", ""value"": ""This strategy is effective in creating a buffer of immune individuals to halt the spread of the infectious disease. It can also be more quickly effective than vaccinating the entire population. However, it comes with the downside that the disease can spread easily if it is reintroduced from somewhere else.""}, {""type"": ""text"", ""value"": ""The ring vaccination strategy was instrumental in the eradication of smallpox.""}, {""type"": ""text"", ""value"": ""Read more on our page on smallpox.""}]}, {""id"": ""ozone_pollution"", ""text"": [{""type"": ""text"", ""value"": ""Ozone pollution""}, {""type"": ""text"", ""value"": ""Ozone pollution refers to ‘tropospheric ozone’ – that is, ozone which exists in the lower atmosphere, close to the surface. Ozone is formed by the reaction with sunlight (photochemical reaction) of pollutants such as nitrogen oxides (NOx) from vehicle and industry emissions and volatile organic compounds (VOCs) emitted by vehicles, solvents and industry.""}, {""type"": ""text"", ""value"": ""Excessive ozone in the air can have a marked effect on human health. It can cause breathing problems, trigger asthma, reduce lung function, and cause lung diseases.""}]}, {""id"": ""particulate_matter_pollution"", ""text"": [{""type"": ""text"", ""value"": ""Particulate matter pollution""}, {""type"": ""text"", ""value"": ""Particulate matter (PM2.5) pollution is caused by very small particles that are 2.5 micrometers or less in diameter. These particles are typically produced by the burning of fuels, for example from vehicle exhausts, or from the burning of cooking fuels. These small particles tend to have more adverse health effects because they can enter airways and affect the respiratory system.""}]}, {""id"": ""secondhand_smoke"", ""text"": [{""type"": ""text"", ""value"": ""Secondhand smoke""}, {""type"": ""text"", ""value"": ""Secondhand smoke, also called 'passive smoking', is the inhalation of tobacco smoke by someone other than the person smoking.""}]}, {""id"": ""smoking"", ""text"": [{""type"": ""text"", ""value"": ""Smoking""}, {""type"": ""text"", ""value"": ""Tobacco smoking is the practice of burning tobacco and ingesting the smoke that is produced. Smoking is a risk factor for many diseases including heart attacks, strokes and cancer. It is the leading risk factor for death in men, globally.""}]}, {""id"": ""risk-factor"", ""text"": [{""type"": ""text"", ""value"": ""Risk factor""}, {""type"": ""text"", ""value"": ""A risk factor is a condition or behavior that increases the likelihood of developing a given disease or injury, or an outcome such as death.""}, {""type"": ""text"", ""value"": ""The impact of a risk factor is estimated in different ways. For example, a common approach is to estimate the number of deaths that would occur if the risk factor was absent.""}, {""type"": ""text"", ""value"": ""Risk factors are not mutually exclusive: people can be exposed to multiple risk factors, which contribute to their disease or death. Because of this, the number of deaths caused by each risk factor is typically estimated separately.""}, {""type"": ""text"", ""value"": ""📃 Read more: How do researchers estimate the death toll caused by each risk factor, whether it’s smoking, obesity or air pollution?""}, {""type"": ""text"", ""value"": ""📃 Read more: Why isn’t it possible to sum up the death toll from different risk factors?""}]}, {""id"": ""unsafe_sanitation"", ""text"": [{""type"": ""text"", ""value"": ""Unsafe sanitation""}, {""type"": ""text"", ""value"": ""Unsafe sanitation includes practices like open defecation and improper treatment of household wastewater. It is linked to the spread of diseases like cholera, dysentery, typhoid, intestinal worms, and polio.""}, {""type"": ""text"", ""value"": ""Unsafe sanitation exacerbates impaired growth and development in children and contributes to the problem of antimicrobial resistance.""}]}, {""id"": ""unsafe_sex"", ""text"": [{""type"": ""text"", ""value"": ""Unsafe sex""}, {""type"": ""text"", ""value"": ""Unsafe sex is defined as the risk of disease due to sexual transmission. Unsafe sex is an important risk factor in cervical cancer, HIV and sexually-transmitted infections.""}]}, {""id"": ""unsafe_water"", ""text"": [{""type"": ""text"", ""value"": ""Unsafe water""}, {""type"": ""text"", ""value"": ""Microbial contamination of drinking water poses the greatest risk to drinking-water safety. It is often the result of contamination with feces. Unsafe water is a risk factor in diarrhea, cholera, dysentery, typhoid, and polio.""}]}, {""id"": ""internetuser"", ""text"": [{""type"": ""text"", ""value"": ""Internet user""}, {""type"": ""text"", ""value"": ""An internet user is defined by the International Telecommunication Union as anyone who has accessed the internet from any location in the last three months.""}, {""type"": ""text"", ""value"": ""This can be from any type of device, including a computer, mobile phone, personal digital assistant, games machine, digital TV, and other technological devices.""}]}, {""id"": ""migrant"", ""text"": [{""type"": ""text"", ""value"": ""Migrant""}, {""type"": ""text"", ""value"": ""Migrants have both an origin and a destination, meaning that international migrants can be viewed from two directions:""}, {""type"": ""text"", ""value"": ""- An emigrant is someone leaving their country of birth (origin).""}, {""type"": ""text"", ""value"": ""- An immigrant is someone moving to a country that they were not born in (destination).""}]}, {""id"": ""refugee"", ""text"": [{""type"": ""text"", ""value"": ""Refugee""}, {""type"": ""text"", ""value"": ""United Nations High Commissioner for Refugees (UNHCR) defines refugees in the following way:""}, {""type"": ""text"", ""value"": ""“Those in need of international protection, being outside their country of origin because of serious threats against which the authorities of their home country cannot or will not protect them.""}, {""type"": ""text"", ""value"": ""In addition, individuals who are outside their country of origin (typically because they have been forcibly displaced across international borders) but who may not qualify as refugees under international or regional law, may in certain circumstances also require international protection, on a temporary or longer-term basis.""}, {""type"": ""text"", ""value"": ""This may include, for example, persons who are displaced across an international border in the context of disasters or the adverse effects of climate change but who are not refugees. In such situations, a need for international protection would reflect the inability of the country of origin to protect against serious harm.”""}]}, {""id"": ""asylumseeker"", ""text"": [{""type"": ""text"", ""value"": ""Asylum seeker""}, {""type"": ""text"", ""value"": ""An “asylum seeker” is an individual that has an application for asylum pending at any stage in the approval process in a country that is different from their home country.""}, {""type"": ""text"", ""value"": ""The United Nations High Commissioner for Refugees (UNHCR) defines asylum in the following way:""}, {""type"": ""text"", ""value"": ""“Asylum is a term that is not defined in international law, but has become a universally-recognized term for the protection provided by a country to refugees and other persons in need of international protection on its territory.""}, {""type"": ""text"", ""value"": ""The need for international protection arises when a person is outside their home country and unable to return home because they would be at risk there, and their country is unable or unwilling to protect them.""}, {""type"": ""text"", ""value"": ""Risks that give rise to a need for international protection classically include those of persecution, threats to life, freedom or physical integrity arising from armed conflict, serious public disorder, or different situations of violence.ˮ""}]}, {""id"": ""internally-displaced"", ""text"": [{""type"": ""text"", ""value"": ""Internally displaced person""}, {""type"": ""text"", ""value"": ""An internally displaced person is an individual who had to leave their home or place of residence but did not cross an international border.""}, {""type"": ""text"", ""value"": ""The United Nations High Commissioner for Refugees (UNHCR) defines internally displaced persons in the following way:""}, {""type"": ""text"", ""value"": ""“Internally displaced persons (IDPs) are persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of, or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized State border. For the purposes of UNHCR’s statistics, this population includes only conflict-generated IDPs to whom the Office extends protection and/or assistance. The IDP population also includes people in an IDP-like situation.”""}]}, {""id"": ""host-community"", ""text"": [{""type"": ""text"", ""value"": ""Host community""}, {""type"": ""text"", ""value"": ""Host community refers to a community that hosts large populations of refugees or internally displaced persons, whether in camps, integrated into households, or independently.""}]}, {""id"": ""international-protection"", ""text"": [{""type"": ""text"", ""value"": ""Other people in need of international protextion""}, {""type"": ""text"", ""value"": ""The United Nations High Commissioner for Refugees (UNHCR) defines other people in need of protection in the following way:""}, {""type"": ""text"", ""value"": ""“Other people in need of international protection refers to people who are outside their country or territory of origin, typically because they have been forcibly displaced across international borders, who have not been reported under other categories (asylum-seekers, refugees, people in refugee-like situations) but who likely need international protection, including protection against forced return, as well as access to basic services on a temporary or longer-term basis.”""}]}, {""id"": ""stateless"", ""text"": [{""type"": ""text"", ""value"": ""Stateless people""}, {""type"": ""text"", ""value"": ""A stateless person is a person is not considered to be a national of any State.""}, {""type"": ""text"", ""value"": ""The United Nations High Commissioner for Refugees (UNHCR) defines stateless individuals in the following way:""}, {""type"": ""text"", ""value"": ""“Individuals under UNHCR’s statelessness mandate are defined under the 1954 Convention Relating to the Status of Stateless People as those not considered as nationals by any State under the operation of its law. In other words, they do not possess the nationality of any State. UNHCR statistics refer to people who fall under the organization’s statelessness mandate as those who are stateless according to this international definition.""}, {""type"": ""text"", ""value"": ""Data from some countries may also include people with undetermined nationality. These are people who lack proof of possession of any nationality and at the same time have or are regarded as having important links to more than one State.”""}]}, {""id"": ""unhcr-concern"", ""text"": [{""type"": ""text"", ""value"": ""Other persons of concern""}, {""type"": ""text"", ""value"": ""The United Nations High Commissioner for Refugees (UNHCR) defines other persons of concern in the following way:""}, {""type"": ""text"", ""value"": ""“Other groups or persons of concern refers to individuals who do not necessarily fall directly into any of the other groups (refugees, asylum-seekers, stateless persons, internally displaced persons) but to whom UNHCR has extended its protection and/or assistance services, based on humanitarian or other special grounds.”""}]}, {""id"": ""naturalization"", ""text"": [{""type"": ""text"", ""value"": ""Naturalization""}, {""type"": ""text"", ""value"": ""Naturalization is the legal act or process by which a non-citizen in a country may acquire citizenship or nationality of that country. This usually includes filing an application or motion and fulfilling certain requirements such as a minimum residency or knowledge of a countries culture and customs.""}]}, {""id"": ""resettlement"", ""text"": [{""type"": ""text"", ""value"": ""Resettlement of refugees""}, {""type"": ""text"", ""value"": ""Resettlement is the process by which refugees relocate to another country which has agreed to admit them with a legal status ensuring international protection and ultimately permanent residence.""}, {""type"": ""text"", ""value"": ""The United Nations High Commissioner for Refugees (UNHCR) defines resettled refugees in the following way:""}, {""type"": ""text"", ""value"": ""“Resettled [refugees](#dod:refugee) are those who have who have been resettled to another country. Resettlement is used to assist refugees in countries that cannot provide them with appropriate protection and support. Resettlement is primarily facilitated by UNHCR in most countries around the world, although significant private sponsorship schemes do exist as well (e.g. in Canada).”""}]}, {""id"": ""unrwa"", ""text"": [{""type"": ""text"", ""value"": ""The United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)""}, {""type"": ""text"", ""value"": ""UNRWA is a UN agency that was established in 1949 to carry out direct relief and works programmes for Palestine refugees.""}, {""type"": ""text"", ""value"": ""UNRWA has a humanitarian and development mandate to “provide assistance and protection to Palestine refugees pending a just and lasting solution to their plight.” This includes essential service delivery, primarily in the areas of basic education, primary health care and mental health care, relief and social services, microcredit, and emergency assistance, including in situations of armed conflict to registered Palestinian refugees.""}, {""type"": ""text"", ""value"": ""They operate in Jordan, Lebanon, Syria the West Bank including East Jerusalem, and Gaza. The Agency does not have a mandate to engage in political negotiations or durable solutions.""}]}, {""id"": ""emigrant"", ""text"": [{""type"": ""text"", ""value"": ""Emigrant""}, {""type"": ""text"", ""value"": ""An emigrant is someone leaving their country of birth (origin).""}]}, {""id"": ""immigrant"", ""text"": [{""type"": ""text"", ""value"": ""Immigrant""}, {""type"": ""text"", ""value"": ""An immigrant is someone moving to a country that they were not born in (destination).""}]}, {""id"": ""remittances"", ""text"": [{""type"": ""text"", ""value"": ""Remittances""}, {""type"": ""text"", ""value"": ""Remittances are in-kind cash transfers made from individuals in a given country to households outside of the host country.""}, {""type"": ""text"", ""value"": ""Remittances are defined by the World Bank as: “Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals.”""}]}, {""id"": ""remittancecost"", ""text"": [{""type"": ""text"", ""value"": ""Remittance cost""}, {""type"": ""text"", ""value"": ""Remittance costs are explained by the International Monetary Fund (IMF) as:""}, {""type"": ""text"", ""value"": ""“The costs of a remittance transaction include a fee charged by the sending agent, typically paid by the sender, and a currency-conversion fee for delivery of local currency to the beneficiary in another country.""}, {""type"": ""text"", ""value"": ""Some smaller operators charge the beneficiary a fee to collect remittances, presumably to account for unexpected exchange-rate movements.""}, {""type"": ""text"", ""value"": ""And remittance agents (especially banks) may earn an indirect fee in the form of interest (or “float”) by investing funds before delivering them to the beneficiary. The float can be significant in countries where overnight interest rates are high.”""}]}, {""id"": ""netmigration"", ""text"": [{""type"": ""text"", ""value"": ""Net migration""}, {""type"": ""text"", ""value"": ""Net migration is the total number of immigrants (people moving into a given country) minus the number of emigrants (people moving out of the country).""}]}, {""id"": ""netmigrationrate"", ""text"": [{""type"": ""text"", ""value"": ""Net migration rate""}, {""type"": ""text"", ""value"": ""Net migration rate is the number of immigrants (people moving into a given country) minus the number of emigrants (people moving out of the country) in the previous five years, divided by the person-years lived by the population of the receiving country over that period.""}]}, {""id"": ""rangelands"", ""text"": [{""type"": ""text"", ""value"": ""Rangelands""}, {""type"": ""text"", ""value"": ""Rangelands are grasslands, shrublands, woodlands, wetlands, and deserts that are grazed by domestic livestock or wild animals.""}, {""type"": ""text"", ""value"": ""The intensity of grazing on rangelands can vary a lot. That can make it difficult to accurately quantify how much rangelands are used for grazing, and therefore how much is used for food production.""}]}, {""id"": ""valueaddedperworker"", ""text"": [{""type"": ""text"", ""value"": ""Value added per worker""}, {""type"": ""text"", ""value"": ""Value added per worker is an important measure of labor productivity.""}, {""type"": ""text"", ""value"": ""It tells us how much economic output is generated per unit of labor input.""}, {""type"": ""text"", ""value"": ""Researchers calculate the value added per worker by dividing the economic output – the number of dollars generated – by the number of workers.""}, {""type"": ""text"", ""value"": ""This then gives us a measure of the number of dollars generated per worker.""}, {""type"": ""text"", ""value"": ""This can be calculated at the aggregate level, across all sectors of an economy. Or for individual sectors, such as agriculture, industry, or services.""}]}, {""id"": ""landowner"", ""text"": [{""type"": ""text"", ""value"": ""Landowner""}, {""type"": ""text"", ""value"": ""A person is considered a landowner if they are the only owner of a plot of land or own it jointly with someone in their own household, or outside of the household.""}, {""type"": ""text"", ""value"": ""This means that a plot of land – and a household – can have multiple owners. And,households can have multiple plots of land, but with different people identified as the owner of each.""}, {""type"": ""text"", ""value"": ""In many countries, land ownership is assigned based on legal documentation and title deeds. But in some countries without official documentation and titles, this data is calculated based on household surveys. In these surveys, households are asked about the ownership of plots of land.""}]}, {""id"": ""nagoya_protocol"", ""text"": [{""type"": ""text"", ""value"": ""Nagoya Protocol""}, {""type"": ""text"", ""value"": ""The Nagoya Protocol is an international agreement that was adopted in 2010 under the UN Convention on Biological Diversity. It aims to ensure that the benefits of genetic resources are shared fairly and equitably between countries and the people who provide them.""}, {""type"": ""text"", ""value"": ""The Protocol also sets out rules for accessing genetic resources and sharing the benefits that arise from their use. It applies to both traditional knowledge and modern genetic resources, and is intended to protect the rights of indigenous and local communities that are often associated with these resources.""}]}, {""id"": ""invasive_species"", ""text"": [{""type"": ""text"", ""value"": ""Invasive species""}, {""type"": ""text"", ""value"": ""An \""alien\"" species is described as one which has been introduced outside its natural distribution range because of human activity. Once an alien species becomes a threat to native biodiversity it is known as an \""invasive alien species\"". These species can be plants, animals, or other organisms, and they can cause harm to the environment, economy, or human health.""}]}, {""id"": ""invasive_alien_species"", ""text"": [{""type"": ""text"", ""value"": ""Invasive alien species""}, {""type"": ""text"", ""value"": ""An \""alien\"" species is described as one which has been introduced outside its natural distribution range because of human activity. Once an alien species becomes a threat to native biodiversity it is known as an \""invasive alien species\"". These species can be plants, animals, or other organisms, and they can cause harm to the environment, economy, or human health.""}]}, {""id"": ""key_biodiversity_area"", ""text"": [{""type"": ""text"", ""value"": ""Key Biodiversity Area (KBA)""}, {""type"": ""text"", ""value"": ""A Key Biodiversity Area is a site that makes a significant contribution to the global persistence of biodiversity. This is often the case if a site contains many unique species. It can also mean that the site is home to a species that isn’t found anywhere else, or is only found in a few other locations.""}, {""type"": ""text"", ""value"": ""The IUCN uses 11 criteria to assess whether a site is a KBA. These cover five categories: threatened biodiversity, geographically restricted biodiversity, ecological integrity, biological processes (e.g. nesting) and irreplaceability.""}]}, {""id"": ""protected_area"", ""text"": [{""type"": ""text"", ""value"": ""Protected area""}, {""type"": ""text"", ""value"": ""A protected area is a clearly defined geographical space that is recognised, and managed through legal or other effective means. Protected areas are managed to preserve their ecosystem services and cultural values over the long-term.""}, {""type"": ""text"", ""value"": ""There are seven different categories of protected areas, ranging from strict nature reserves which are protected from all but light human use; to protected areas which allow the sustainable use of natural resources (such as logging, or fishing).""}, {""type"": ""text"", ""value"": ""Protected areas can be in the ocean (a marine protected area – MPA) or on land.""}]}, {""id"": ""enso_cycle"", ""text"": [{""type"": ""text"", ""value"": ""El Niño–Southern Oscillation (ENSO) Cycle""}, {""type"": ""text"", ""value"": ""The El Niño–Southern Oscillation (ENSO) is a periodic variation in winds and sea surface temperatures over the tropical eastern Pacific Ocean that affects the climate of regions in the tropics and subtropics. The warming phase of the sea temperature is known as El Niño, and the cooling phase as La Niña.""}, {""type"": ""text"", ""value"": ""The ENSO cycle is defined by changes in atmospheric pressure. El Niño is accompanied by high air surface pressure in the tropical western Pacific, and La Niña with low air surface pressure. The two periods last several months each and tend to occur every few years. The intensity of each cycle can vary.""}]}, {""id"": ""taxonomic_group"", ""text"": [{""type"": ""text"", ""value"": ""Taxonomic group""}, {""type"": ""text"", ""value"": ""A taxonomic group is a category in the scientific classification of living things, based on shared characteristics and genetic similarity. It is arranged in a hierarchical system, with each group being more specific than the one above it, and all groups forming the entire classification of living things.""}]}, {""id"": ""population"", ""text"": [{""type"": ""text"", ""value"": ""Population""}, {""type"": ""text"", ""value"": ""A population is a group of individuals of the same species that live in the same geographic area. A species will often have multiple or many populations, each living in a different area.""}]}, {""id"": ""endemic_species"", ""text"": [{""type"": ""text"", ""value"": ""Endemic species""}, {""type"": ""text"", ""value"": ""Endemic species are plants and animals that only exist in one geographical area. Endemic species are more common in isolated environments, such as islands, due to the unique conditions and barriers to immigration. As a result of long-term geographic isolation, it is more likely that distinct and unique species will evolve in these isolated areas""}]}, {""id"": ""extinction_risk"", ""text"": [{""type"": ""text"", ""value"": ""Extinction risk""}, {""type"": ""text"", ""value"": ""The International Union for the Conservation of Nature (IUCN) evaluates the risk of a species going extinct based on several criteria, including their geographical range and current population size.""}, {""type"": ""text"", ""value"": ""The IUCN publishes these assessments in its flagship Red List.""}, {""type"": ""text"", ""value"": ""Species are sorted into nine categories, extending through: Not Evaluated, Data Deficient, Least Concern, Near Threatened, Vulnerable, Endangered, Critically Endangered, Extinct in the Wild and Extinct.""}]}, {""id"": ""red_list_index"", ""text"": [{""type"": ""text"", ""value"": ""Red List Index""}, {""type"": ""text"", ""value"": ""The Red List Index (RLI) tracks the status of species groups within the IUCN’s Red List, which is a measure of species extinction risk.""}, {""type"": ""text"", ""value"": ""The RLI is an index between 0 and 1, which changes over time based on changes in a species extinction risk. A declining RLI trend indicates that the risk of extinction among the species included in the index is increasing.""}, {""type"": ""text"", ""value"": ""To be included in the RLI, species groups need to be fully assessed. Currently, only mammals, birds, corals, amphibians, and cycads have the necessary data to be included.""}, {""type"": ""text"", ""value"": ""This means the RLI is an aggregate index based on these species groups only. An RLI can also be calculated for individual species groups or for countries.""}]}, {""id"": ""coral_bleaching"", ""text"": [{""type"": ""text"", ""value"": ""Coral bleaching""}, {""type"": ""text"", ""value"": ""Corals contain microscopic algae that photosynthesize, providing corals with the majority of their energy. When exposed to warmer waters, corals can expel their algal symbionts, meaning they lose their source of energy. Without their algae, corals turn pale; causing them to look ‘bleached’. Several successive bleaching events can cause coral to die.""}]}, {""id"": ""msy"", ""text"": [{""type"": ""text"", ""value"": ""Maximum Sustainable Yield""}, {""type"": ""text"", ""value"": ""Maximum Sustainable Yield (MSY) is defined as the greatest average amount of catch that can be harvested in the long-term from a fish stock under constant and current environmental conditions (e.g., habitat, water conditions, species composition and interactions, and anything that could affect birth, growth, or death rates of the stock), without affecting the long-term productivity of the fish stock.""}]}, {""id"": ""fish-stock"", ""text"": [{""type"": ""text"", ""value"": ""Fish stocks""}, {""type"": ""text"", ""value"": ""Fish stocks are subpopulations of a particular species of fish which have common parameters such as location, growth and mortality which define their population dynamics.""}]}, {""id"": ""gravitational_waves"", ""text"": [{""type"": ""text"", ""value"": ""Gravitational waves""}, {""type"": ""text"", ""value"": ""Gravitational waves are ripples in the fabric of space-time that are created when two massive objects, such as black holes or neutron stars, collide or interact. They move at the speed of light, but they are extremely difficult to detect because they are so small and faint. It wasn't until the development of advanced technology like LIGO (the Laser Interferometer Gravitational-Wave Observatory) that researchers were able to detect these waves for the first time in 2015.""}]}, {""id"": ""homicide"", ""text"": [{""type"": ""text"", ""value"": ""Homicide""}, {""type"": ""text"", ""value"": ""The killing of a person by another with intent to cause death or injury.""}]}, {""id"": ""age_standardized"", ""text"": [{""type"": ""text"", ""value"": ""Age standardization""}, {""type"": ""text"", ""value"": ""Age standardization is an adjustment that makes it possible to compare populations with different age structures by standardizing them to a common reference population.""}, {""type"": ""text"", ""value"": ""📄 Read more: How does age standardization make health metrics comparable?""}]}, {""id"": ""low_earth_orbit"", ""text"": [{""type"": ""text"", ""value"": ""Low Earth orbit""}, {""type"": ""text"", ""value"": ""A low Earth orbit (LEO) is an Earth-centered orbit with an altitude of 2,000 kilometers or less (approximately one-third of Earth’s radius). This is the orbit where most artificial objects in outer space live. LEOs are often used for satellites, including those for communication, Earth observation, and space stations due to their proximity to Earth’s surface, facilitating shorter communication times and detailed surface imaging.""}]}, {""id"": ""medium_earth_orbit"", ""text"": [{""type"": ""text"", ""value"": ""Medium Earth orbit""}, {""type"": ""text"", ""value"": ""A medium Earth orbit (MEO) is characterized by an altitude ranging from 2,000 kilometers to just below the geostationary orbit at approximately 35,786 kilometers above Earth. MEOs are mainly used for navigation satellite systems like the Global Positioning System (GPS), as their higher altitude allows for a larger coverage area with fewer satellites. This orbit is also beneficial for satellites that require a balance between Earth coverage and signal delay, making it a strategic choice for global navigation and communication systems.""}]}, {""id"": ""geostationary_orbit"", ""text"": [{""type"": ""text"", ""value"": ""Geostationary orbit""}, {""type"": ""text"", ""value"": ""A geostationary orbit (GEO) is a specific orbit located approximately 35,786 kilometers above the Earth's equator. This orbit allows satellites to match the Earth’s rotation, effectively remaining stationary over a fixed point. This unique characteristic makes GEO ideal for communication, weather satellites, and certain broadcasting systems that benefit from a constant position relative to the Earth's surface, ensuring continuous coverage over specific geographical areas.""}]}, {""id"": ""high_earth_orbit"", ""text"": [{""type"": ""text"", ""value"": ""High Earth orbit""}, {""type"": ""text"", ""value"": ""A high Earth orbit (HEO) refers to orbits above the altitude of a geostationary orbit (above 35,786 kilometers from the Earth’s surface). These orbits are particularly useful for satellites that require a broad field of view of the Earth, such as for monitoring large areas or for scientific observations of the Earth's atmosphere and space environment. Due to their high altitude, satellites in HEO have longer orbital periods and can observe larger portions of the Earth's surface than lower orbits.""}]}, {""id"": ""moore_law"", ""text"": [{""type"": ""text"", ""value"": ""Moore's law""}, {""type"": ""text"", ""value"": ""Moore's law is the observation that the number of transistors in an integrated circuit doubles about every two years, thanks to improvements in production. It was first described by Gordon E. Moore, the co-founder of Intel, in 1965.""}, {""type"": ""text"", ""value"": ""Read more: What is Moore’s Law?""}]}, {""id"": ""depressive_disorders"", ""text"": [{""type"": ""text"", ""value"": ""Depressive disorders""}, {""type"": ""text"", ""value"": ""Depressive disorders are defined by the ICD as a group of conditions that involve significant, persistent feelings of sadness or a loss of interest, along with several other symptoms.""}, {""type"": ""text"", ""value"": ""📄 Read more: How are mental illnesses defined?""}, {""type"": ""text"", ""value"": ""📘 Find out how each illness is diagnosed in the International Classification of Diseases manual.""}]}, {""id"": ""odp_tonnes"", ""text"": [{""type"": ""text"", ""value"": ""Ozone-depleting tonnes (ODP tonnes)""}, {""type"": ""text"", ""value"": ""Ozone-depleting tonnes measure the total potential of substances to deplete the ozone layer. Some substances that deplete the ozone layer are 'stronger' than others, meaning one tonne will cause greater damage than one tonne of another. ODP tonnes are calculated by multiplying a substance's emissions in tonnes, by its 'ozone-depleting potential'.""}, {""type"": ""text"", ""value"": ""Ozone-depleting potential measures how much depletion a substance causes relative to CFC-11, which has a value of 1.0. If one tonne of a gas caused twice the depletion of CFC-11, it would have a potential of 2.0.""}]}, {""id"": ""qubits"", ""text"": [{""type"": ""text"", ""value"": ""Quantum bits""}, {""type"": ""text"", ""value"": ""Quantum bits, or qubits, are the basic units of quantum computing. Unlike classical bits that store information as 0 or 1, qubits can exist in multiple states simultaneously, enabling complex calculations and enhanced computing power.""}]}, {""id"": ""anxiety_disorders"", ""text"": [{""type"": ""text"", ""value"": ""Anxiety disorders""}, {""type"": ""text"", ""value"": ""Anxiety disorders are defined by the ICD as a group of conditions that involve feelings of intense fear and distress, along with other physical symptoms.""}, {""type"": ""text"", ""value"": ""📄 Read more: How are mental illnesses defined?""}, {""type"": ""text"", ""value"": ""📘 Find out how each illness is diagnosed in the International Classification of Diseases manual.""}]}, {""id"": ""schizophrenia"", ""text"": [{""type"": ""text"", ""value"": ""Schizophrenia""}, {""type"": ""text"", ""value"": ""Schizophrenia is defined by the ICD as a condition that involves significant problems in perceiving reality, difficulty with memory and attention, and changes in behavior and movement.""}, {""type"": ""text"", ""value"": ""📄 Read more: How are mental illnesses defined?""}, {""type"": ""text"", ""value"": ""📘 Find out how each illness is diagnosed in the International Classification of Diseases manual.""}]}, {""id"": ""bipolar_disorder"", ""text"": [{""type"": ""text"", ""value"": ""Bipolar disorder""}, {""type"": ""text"", ""value"": ""Bipolar disorder is defined by the ICD as a condition that involves two different sets of symptoms. One involves depressive symptoms, while the other involves significantly increased excitement, irritability and energy.""}, {""type"": ""text"", ""value"": ""📄 Read more: How are mental illnesses defined?""}, {""type"": ""text"", ""value"": ""📘 Find out how each illness is diagnosed in the International Classification of Diseases manual.""}]}, {""id"": ""eating_disorder"", ""text"": [{""type"": ""text"", ""value"": ""Eating disorders""}, {""type"": ""text"", ""value"": ""Eating disorders are defined by the ICD as a group of conditions that involve abnormal behaviors and preoccupations with food, along with strong concerns about body weight and shape.""}, {""type"": ""text"", ""value"": ""📄 Read more: How are mental illnesses defined?""}, {""type"": ""text"", ""value"": ""📘 Find out how each illness is diagnosed in the International Classification of Diseases manual.""}]}, {""id"": ""major_depression"", ""text"": [{""type"": ""text"", ""value"": ""Major depression""}, {""type"": ""text"", ""value"": ""Major depression is a condition that involves feeling persistent sadness or a loss of interest, along with several other symptoms, for at least two weeks.""}]}, {""id"": ""anorexia_nervosa"", ""text"": [{""type"": ""text"", ""value"": ""Anorexia nervosa""}, {""type"": ""text"", ""value"": ""Anorexia nervosa is defined by the ICD as a condition that involves having persistent patterns of behavior that are aimed at reaching or maintaining an abnormally low body weight, along with an extreme fear of weight gain.""}, {""type"": ""text"", ""value"": ""📄 Read more: How are mental illnesses defined?""}, {""type"": ""text"", ""value"": ""📘 Find out how each illness is diagnosed in the International Classification of Diseases manual.""}]}, {""id"": ""bulimia_nervosa"", ""text"": [{""type"": ""text"", ""value"": ""Bulimia nervosa""}, {""type"": ""text"", ""value"": ""Bulimia nervosa is defined by the ICD as a condition that involves having frequent recurring episodes of binge eating, as well as inappropriate behaviours to prevent weight gain. Patients have an excessive preoccupation or significant distress about their body weight or shape.""}, {""type"": ""text"", ""value"": ""📄 Read more: How are mental illnesses defined?""}, {""type"": ""text"", ""value"": ""📘 Find out how each illness is diagnosed in the International Classification of Diseases manual.""}]}, {""id"": ""rotavirus"", ""text"": [{""type"": ""text"", ""value"": ""Rotavirus""}, {""type"": ""text"", ""value"": ""Rotavirus is a contagious virus that can cause severe diarrhea. It is among the most common causes of diarrheal deaths in children.""}]}, {""id"": ""cholera"", ""text"": [{""type"": ""text"", ""value"": ""Cholera""}, {""type"": ""text"", ""value"": ""Cholera is a severe diarrheal disease caused by the bacteria Vibrio cholerae. It is spread through contaminated water and food, typically in areas lacking access to clean water and sanitation. Although cholera is easily treatable it can kill within hours or days if untreated.""}]}, {""id"": ""indicator-2-4-1"", ""text"": [{""type"": ""text"", ""value"": ""Sub-indicators of indicator 2.4.1""}, {""type"": ""text"", ""value"": ""The 11 sub-indicators used to measure this indicator are:""}, {""type"": ""text"", ""value"": ""1) Land productivity: Farm output value per hectare""}, {""type"": ""text"", ""value"": ""2) Profitability: Net farm income""}, {""type"": ""text"", ""value"": ""3) Resilience: Risk mitigation mechanisms""}, {""type"": ""text"", ""value"": ""4) Soil health: Prevalence of soil degradation""}, {""type"": ""text"", ""value"": ""5) Water use: Variation in water availability""}, {""type"": ""text"", ""value"": ""6) Fertilizer pollution risk: Management of fertilizers""}, {""type"": ""text"", ""value"": ""7) Pesticide risk: Management of pesticides""}, {""type"": ""text"", ""value"": ""8) Biodiversity: Use of agro-biodiversity-supportive practices""}, {""type"": ""text"", ""value"": ""9) Decent employment: Wage rate in agriculture""}, {""type"": ""text"", ""value"": ""10) Food security: Food Insecurity Experience Scale (FIES)""}, {""type"": ""text"", ""value"": ""11) Land tenure: Secure tenure rights to land""}]}, {""id"": ""indicator-5-a-2"", ""text"": [{""type"": ""text"", ""value"": ""Proxies of equality in land rights""}, {""type"": ""text"", ""value"": ""The six legal proxies used to measure this indicator are:""}, {""type"": ""text"", ""value"": ""1) Joint registration of land is compulsory or encouraged through economic incentives""}, {""type"": ""text"", ""value"": ""2) Compulsory spousal consent for land transactions""}, {""type"": ""text"", ""value"": ""3) Women’s and girls’ equal inheritance rights""}, {""type"": ""text"", ""value"": ""4) Allocation of financial resources to increase women’s ownership and control over land, in legal systems that recognize customary land tenure""}, {""type"": ""text"", ""value"": ""5) The existence of explicit protection of the land rights of women""}, {""type"": ""text"", ""value"": ""6) Mandatory quotas for women’s participation in land management and administration institutions.""}]}, {""id"": ""indicator-5-6-2"", ""text"": [{""type"": ""text"", ""value"": ""Regulations on access to sexual health care""}, {""type"": ""text"", ""value"": ""The 13 sexual and reproductive health components where information is collected on the existence of i) specific legal enablers (positive laws, and regulations) and ii) specific legal barriers are:""}, {""type"": ""text"", ""value"": ""1) Maternity care""}, {""type"": ""text"", ""value"": ""2) Life-saving commodities for reproductive, maternal, newborn and child health""}, {""type"": ""text"", ""value"": ""3) Abortion""}, {""type"": ""text"", ""value"": ""4) Post-abortion care""}, {""type"": ""text"", ""value"": ""5) Contraception""}, {""type"": ""text"", ""value"": ""6) Consent for contraceptive services""}, {""type"": ""text"", ""value"": ""7) Emergency contraception""}, {""type"": ""text"", ""value"": ""8) Comprehensive sexuality education law""}, {""type"": ""text"", ""value"": ""9) Comprehensive sexuality education curriculum""}, {""type"": ""text"", ""value"": ""10) HIV testing and counselling""}, {""type"": ""text"", ""value"": ""11) HIV treatment and care""}, {""type"": ""text"", ""value"": ""12) Confidentiality of health status for men and women living with HIV""}, {""type"": ""text"", ""value"": ""13) HPV vaccine""}]}, {""id"": ""neglected-tropical-diseases"", ""text"": [{""type"": ""text"", ""value"": ""Neglected tropical diseases""}, {""type"": ""text"", ""value"": ""Neglected tropical diseases is a category that refers to infectious diseases that are: neglected by public health funding, disproportionately affect the extremely poor, and tend to be found in tropical regions of the world.""}, {""type"": ""text"", ""value"": ""The World Health Organization lists the following 20 diseases under the category:""}, {""type"": ""text"", ""value"": ""• Buruli ulcer""}, {""type"": ""text"", ""value"": ""• Chagas disease""}, {""type"": ""text"", ""value"": ""• Chromoblastomycosis and other deep mycoses""}, {""type"": ""text"", ""value"": ""• Cysticercosis""}, {""type"": ""text"", ""value"": ""• Chikungunya""}, {""type"": ""text"", ""value"": ""• Dengue""}, {""type"": ""text"", ""value"": ""• Guinea worm disease (“dracunculiasis”)""}, {""type"": ""text"", ""value"": ""• Echinococcosis""}, {""type"": ""text"", ""value"": ""• Foodborne trematodiases""}, {""type"": ""text"", ""value"": ""• Sleeping sickness (“African trypanosomiasis”)""}, {""type"": ""text"", ""value"": ""• Leishmaniasis""}, {""type"": ""text"", ""value"": ""• Leprosy (“Hansen's disease”)""}, {""type"": ""text"", ""value"": ""• Lymphatic filariasis (“elephantiasis”)""}, {""type"": ""text"", ""value"": ""• Mycetoma""}, {""type"": ""text"", ""value"": ""• River blindness (“onchocerciasis”)""}, {""type"": ""text"", ""value"": ""• Rabies""}, {""type"": ""text"", ""value"": ""• Scabies and other ectoparasites""}, {""type"": ""text"", ""value"": ""• Schistosomiasis""}, {""type"": ""text"", ""value"": ""• Soil-transmitted helminthiases""}, {""type"": ""text"", ""value"": ""• Snakebite envenoming""}, {""type"": ""text"", ""value"": ""• Taeniasis""}, {""type"": ""text"", ""value"": ""• Trachoma""}, {""type"": ""text"", ""value"": ""• Yaws""}]}, {""id"": ""buruli-ulcer"", ""text"": [{""type"": ""text"", ""value"": ""Buruli ulcer""}, {""type"": ""text"", ""value"": ""Buruli ulcer is a skin disease that causes severe sores and disfigurement if not treated early. It is caused by bacteria called Mycobacterium ulcerans.""}, {""type"": ""text"", ""value"": ""The disease starts as a small swelling, but if untreated, it turns into a large open sore or ulcer that destroys skin, muscles, and sometimes even bone. As the ulcers get bigger, they can permanently disfigure and disable the affected body part.""}, {""type"": ""text"", ""value"": ""Buruli ulcer can be treated with antibiotics and surgery to remove the affected tissue.""}, {""type"": ""text"", ""value"": ""It is classified as a neglected tropical disease.""}]}, {""id"": ""chromoblastomycosis"", ""text"": [{""type"": ""text"", ""value"": ""Chromoblastomycosis""}, {""type"": ""text"", ""value"": ""Chromoblastomycosis and other deep mycoses are serious fungal infections that can lead to deformities, disabilities and sometimes cancer.""}, {""type"": ""text"", ""value"": ""They are caused by certain fungi entering the body through cuts or skin wounds, often on the feet or legs.""}, {""type"": ""text"", ""value"": ""People experience skin sores, bumps under the skin, and progressive destruction of surrounding tissue. If untreated, these deep fungal infections can spread to bones and other organs, and cause deformities and life-threatening complications.""}, {""type"": ""text"", ""value"": ""Long-term antifungal treatment and surgery are often required.""}, {""type"": ""text"", ""value"": ""It is classified as a neglected tropical disease.""}]}, {""id"": ""taeniasis-cysticercosis"", ""text"": [{""type"": ""text"", ""value"": ""Taeniasis and cysticercosis""}, {""type"": ""text"", ""value"": ""Taeniasis and cysticercosis are infections caused by the same tapeworm species Taenia solium. It can be ingested from undercooked beef or pork containing the larval cysts of some tapeworm species.""}, {""type"": ""text"", ""value"": ""Taeniasis is an intestinal infection of the adult stage of the tapeworm, which can cause digestive problems and nutritional deficiencies.""}, {""type"": ""text"", ""value"": ""People have symptoms such as abdominal pain, diarrhea, weight loss and general discomfort from having the adult tapeworm residing in the intestines.""}, {""type"": ""text"", ""value"": ""Cysticercosis describes the infection with the larval stage of the tapeworm. The larvae migrate around the body and form cysts. These can also form in the brain, which can cause seizures. This is called neurocysticercosis.""}, {""type"": ""text"", ""value"": ""Taeniasis is treated with antiparasitic medications. Cysticercosis is more difficult to treat and can require surgery to remove the cysts.""}, {""type"": ""text"", ""value"": ""It is classified as a neglected tropical disease.""}]}, {""id"": ""chromoblastomycosis"", ""text"": [{""type"": ""text"", ""value"": ""Chromoblastomycosis""}, {""type"": ""text"", ""value"": ""Chromoblastomycosis and other deep mycoses are serious fungal infections that can lead to deformities, disabilities and sometimes cancer.""}, {""type"": ""text"", ""value"": ""They are caused by certain fungi entering the body through cuts or skin wounds, often on the feet or legs.""}, {""type"": ""text"", ""value"": ""People experience skin sores, bumps under the skin, and progressive destruction of surrounding tissue. If untreated, these deep fungal infections can spread to bones and other organs, and cause deformities and life-threatening complications.""}, {""type"": ""text"", ""value"": ""Long-term antifungal treatment and surgery are often required.""}, {""type"": ""text"", ""value"": ""It is classified as a neglected tropical disease.""}]}, {""id"": ""chagas-disease"", ""text"": [{""type"": ""text"", ""value"": ""Chagas disease""}, {""type"": ""text"", ""value"": ""Chagas disease can severely damage the heart and digestive system over time if left untreated.""}, {""type"": ""text"", ""value"": ""It is caused by the parasite Trypanosoma cruzi, spread through the feces of \""kissing bugs\"", which can live in walls and cracks in rural or suburban areas.""}, {""type"": ""text"", ""value"": ""Early symptoms include fever, fatigue, body aches, and rashes. But the parasites keep living in the body for life if untreated, gradually damaging the heart muscle and digestive system over decades, leading to an enlarged heart, heart failure, severe digestive issues, and potentially death.""}, {""type"": ""text"", ""value"": ""Chagas disease can be treated with antiparasitic medications.""}, {""type"": ""text"", ""value"": ""It is classified as a neglected tropical disease.""}]}, {""id"": ""dengue"", ""text"": [{""type"": ""text"", ""value"": ""Dengue""}, {""type"": ""text"", ""value"": ""Dengue fever is a potentially fatal disease caused by dengue virus, which is spread by mosquito bites.""}, {""type"": ""text"", ""value"": ""It causes high fevers, rashes, muscle and joint pains. Although many people recover, some develop life-threatening dengue hemorrhagic fever or dengue shock syndrome, where they experience severe bleeding, organ impairment, and leaking of blood plasma, which can lead to death without urgent care.""}, {""type"": ""text"", ""value"": ""It is classified as a neglected tropical disease.""}]}, {""id"": ""chikungunya"", ""text"": [{""type"": ""text"", ""value"": ""Chikungunya""}, {""type"": ""text"", ""value"": ""Chikungunya is an infectious disease caused by the chikungunya virus, which is spread by mosquitoes.""}, {""type"": ""text"", ""value"": ""It causes sudden high fevers and extremely painful joint swelling and stiffness, which can be very severe or even disabling. Other symptoms include headaches, muscle aches, nausea and rashes.""}, {""type"": ""text"", ""value"": ""Most people recover soon, but some develop joint pain that persists for months or years, severely affecting their daily life.""}, {""type"": ""text"", ""value"": ""It is classified as a neglected tropical disease.""}]}, {""id"": ""echinococcosis"", ""text"": [{""type"": ""text"", ""value"": ""Echinococcosis""}, {""type"": ""text"", ""value"": ""Echinococcosis is an infectious disease caused by a parasitic tapeworm that can lead to severe disease of the lungs or liver and potentially death.""}, {""type"": ""text"", ""value"": ""It is caused by ingesting eggs of the Echinococcus tapeworm species, often from contact with infected dogs, which can initially lead to symptoms like abdominal pain, nausea, and vomiting.""}, {""type"": ""text"", ""value"": ""Treatment involves surgery or antiparasitic medications.""}, {""type"": ""text"", ""value"": ""It is classified as a neglected tropical disease.""}]}, {""id"": ""scabies"", ""text"": [{""type"": ""text"", ""value"": ""Scabies""}, {""type"": ""text"", ""value"": ""Scabies is a highly contagious skin disease caused by the mite Sarcoptes scabiei, which leads to intense itching and rashes.""}, {""type"": ""text"", ""value"": ""The mites burrow under the skin and lay eggs, causing an allergic reaction. Other ectoparasites like lice and fleas can also cause itching, rashes, and skin irritation.""}, {""type"": ""text"", ""value"": ""The mites spread through close person-to-person or skin-to-skin contact, and can be treated with antiparasitic medications.""}, {""type"": ""text"", ""value"": ""It is classified as a neglected tropical disease.""}]}, {""id"": ""soil-transmitted-helminths"", ""text"": [{""type"": ""text"", ""value"": ""Soil-transmitted helminths""}, {""type"": ""text"", ""value"": ""Soil-transmitted helminths are parasitic worms that cause diseases that affect physical and intellectual development, especially in children.""}, {""type"": ""text"", ""value"": ""They include different species such as roundworm, whipworm and hookworms, which can infect people through contact with soil contaminated by human feces containing worm eggs.""}, {""type"": ""text"", ""value"": ""People develop symptoms such as diarrhea, abdominal pain, malnutrition and related complications.""}, {""type"": ""text"", ""value"": ""These infections can be treated with deworming medications and improved sanitation.""}, {""type"": ""text"", ""value"": ""Soil-transmitted helminthiases are classified as neglected tropical diseases.""}]}, {""id"": ""foodborne-trematodiases"", ""text"": [{""type"": ""text"", ""value"": ""Foodborne trematodiases""}, {""type"": ""text"", ""value"": ""Foodborne trematodiases are parasitic worm infections that can cause liver and lung disease if untreated.""}, {""type"": ""text"", ""value"": ""They are caused by ingesting undercooked fish, crustaceans, or vegetables that contain microscopic parasitic worms called trematodes or flukes.""}, {""type"": ""text"", ""value"": ""People first develop symptoms like fever, abdominal pain, diarrhea, and blood in stools.""}, {""type"": ""text"", ""value"": ""Over time, the worms can migrate to the liver, lungs, or other organs, causing severe complications like obstructing the bile duct, liver scarring, and lung problems.""}, {""type"": ""text"", ""value"": ""These infections can be treated with antiparasitic medications.""}, {""type"": ""text"", ""value"": ""They are classified as neglected tropical diseases.""}]}, {""id"": ""mycetoma"", ""text"": [{""type"": ""text"", ""value"": ""Mycetoma""}, {""type"": ""text"", ""value"": ""Mycetoma is a destructive skin and bone infection that can lead to deformity and disability if left untreated.""}, {""type"": ""text"", ""value"": ""It can be caused by some different species of fungi or bacteria, which can enter the body through minor skin injuries, often on the feet or hands.""}, {""type"": ""text"", ""value"": ""If untreated, the infection spreads to underlying bone, causing severe deformities.""}, {""type"": ""text"", ""value"": ""Mycetoma requires long-term antifungal or antibiotic treatment, often with surgery, potentially including amputations. It is classified as a neglected tropical disease.""}]}, {""id"": ""maternal-mortality"", ""text"": [{""type"": ""text"", ""value"": ""Maternal mortality""}, {""type"": ""text"", ""value"": ""Maternal mortality relates to the death of women during or shortly after pregnancy.""}, {""type"": ""text"", ""value"": ""This is defined differently by different organizations.""}, {""type"": ""text"", ""value"": ""According to the ICD-11, maternal deaths are defined as the deaths of women while pregnant or within 42 days of termination of pregnancy, from maternal conditions, but excluding accidental or incidental causes of death.""}, {""type"": ""text"", ""value"": ""Maternal conditions are defined from the “underlying cause of death” on death certificates.""}, {""type"": ""text"", ""value"": ""In the WHO Mortality Database and IHME GBD (estimates up to 2019), maternal deaths up to 1 year of termination of pregnancy are also included.""}, {""type"": ""text"", ""value"": ""In countries where cause of death data is lacking, estimates (such as in the WHO GHO and IHME GBD) include deaths during or shortly after pregnancy, irrespective of the cause — this means they include causes incidental to the pregnancy.""}, {""type"": ""text"", ""value"": ""Read more on our page: Maternal mortality.""}]}, {""id"": ""guinea-worm"", ""text"": [{""type"": ""text"", ""value"": ""Guinea worm""}, {""type"": ""text"", ""value"": ""Guinea worm is a parasitic worm that causes guinea worm disease (also known as “dracunculiasis”). which is a painful and debilitating disease of the bones, connective tissue, and joints.""}, {""type"": ""text"", ""value"": ""The worm’s larvae can be ingested from contaminated water, and grow into adult worms whose growth in the body can lead to arthritic conditions. The disease used to be common in Asia, the Middle East, and many countries in Africa, but it is close to eradication.""}, {""type"": ""text"", ""value"": ""It is classified as a neglected tropical disease.""}, {""type"": ""text"", ""value"": ""📃Read more in our article: Guinea worm disease is close to being eradicated – how was this progress achieved?""}]}, {""id"": ""rabies"", ""text"": [{""type"": ""text"", ""value"": ""Rabies""}, {""type"": ""text"", ""value"": ""Rabies is a viral disease that spreads from the bite of an infected animal, such as a rabid dog.""}, {""type"": ""text"", ""value"": ""The disease is fatal without vaccination. The disease causes brain inflammation, fever, and delirium.""}, {""type"": ""text"", ""value"": ""Rabies vaccines are typically given after a bite, but some vaccines are used as preventive treatment.""}, {""type"": ""text"", ""value"": ""It is classified as a neglected tropical disease.""}]}, {""id"": ""yaws"", ""text"": [{""type"": ""text"", ""value"": ""Yaws""}, {""type"": ""text"", ""value"": ""Yaws is a chronic infectious disease that can be disfiguring and debilitating. It is caused by the bacterium Treponema pallidum.""}, {""type"": ""text"", ""value"": ""It primarily affects the skin, bones, and connective tissue: patients develop highly contagious skin lumps and ulcers and bone deformities, and spreads through direct contact with the wounds of an infected person.""}, {""type"": ""text"", ""value"": ""The infection can be effectively treated with antibiotics, such as azithromycin or benzathine penicillin.""}, {""type"": ""text"", ""value"": ""It is classified as a neglected tropical disease.""}]}, {""id"": ""smallpox"", ""text"": [{""type"": ""text"", ""value"": ""Smallpox""}, {""type"": ""text"", ""value"": ""Smallpox was a severe and contagious disease caused by the variola virus.""}, {""type"": ""text"", ""value"": ""Patients infected by the virus developed fever, body aches, and a distinctive rash that developed into fluid-filled blisters.""}, {""type"": ""text"", ""value"": ""The disease was known for its high mortality rate and the permanent scarring it often left on survivors. Historically, it affected people across various continents. Through a global vaccination campaign, smallpox became the first disease to be eradicated by human effort.""}, {""type"": ""text"", ""value"": ""📃 Read more on our page on smallpox.""}]}, {""id"": ""family-planning"", ""text"": [{""type"": ""text"", ""value"": ""Family planning""}, {""type"": ""text"", ""value"": ""Family planning allows people to attain their desired number of children, if any, and to determine the spacing of their pregnancies. It is achieved through use of contraceptive methods and the treatment of infertility.""}]}, {""id"": ""modern-contraception"", ""text"": [{""type"": ""text"", ""value"": ""Modern contraception""}, {""type"": ""text"", ""value"": ""Modern contraceptive methods include: oral hormonal pills, intrauterine devices (IUDs), male or female condoms, emergency contraception, implant, sterilization, injectables, and vaginal barrier methods.""}]}, {""id"": ""diarrhea"", ""text"": [{""type"": ""text"", ""value"": ""Diarrheal disease episode""}, {""type"": ""text"", ""value"": ""Diarrheal disease episodes are defined as three or more loose stools in a 24-hour period.""}]}, {""id"": ""improved-sanitation"", ""text"": [{""type"": ""text"", ""value"": ""Improved sanitation facilities""}, {""type"": ""text"", ""value"": ""Improved sanitation facilities are those designed to hygienically separate excreta from human contact, and include: flush/pour flush toilets connected to piped sewer systems, septic tanks or pit latrines; pit latrines with slabs (including ventilated pit latrines), and composting toilets.""}]}, {""id"": ""population-growth-rate"", ""text"": [{""type"": ""text"", ""value"": ""Population growth rate""}, {""type"": ""text"", ""value"": ""Population growth rate explains the change in population over a given interval of time. This is often expressed as the annual growth rate.""}, {""type"": ""text"", ""value"": ""The United Nations calculates this as an exponential growth rate. It calculates it by taking the natural logarithm of the annual change, which is the population in that year compared to the previous year. For example, the change in population from 2017 to 2018. This is expressed as a percentage.""}, {""type"": ""text"", ""value"": ""Natural population growth rate is the expected change in population from births and deaths only. It does not include changes as a result of migration.""}]}, {""id"": ""fertility-rate"", ""text"": [{""type"": ""text"", ""value"": ""Fertility rate""}, {""type"": ""text"", ""value"": ""The total fertility rate is a period metric. It summarizes fertility rates across all age groups in one particular year.""}, {""type"": ""text"", ""value"": ""For a given year, the total fertility rate represents the average number of children that would be born to a hypothetical woman if she (1) lived to the end of her childbearing years, and (2) experienced the same age-specific fertility rates throughout her whole reproductive life as the age-specific fertility rates seen in that particular year.""}, {""type"": ""text"", ""value"": ""It is different from the actual average number of children that women have.""}, {""type"": ""text"", ""value"": ""The fertility rate should not be confused with biological fertility, which is about the ability of a person to conceive.""}, {""type"": ""text"", ""value"": ""📄 Read more: Fertility rate""}]}, {""id"": ""un-projection-scenarios"", ""text"": [{""type"": ""text"", ""value"": ""UN projection scenarios""}, {""type"": ""text"", ""value"": ""The UN's World Population Prospects provides a range of projected scenarios of population change. These rely on different assumptions in fertility, mortality and/or migration patterns to explore different demographic futures.""}, {""type"": ""text"", ""value"": ""📄 Read more: Definition of Projection Scenarios (UN)""}]}, {""id"": ""diarrheal-diseases"", ""text"": [{""type"": ""text"", ""value"": ""Diarrheal diseases""}, {""type"": ""text"", ""value"": ""Diarrheal diseases are a group of illnesses that are usually caused by viral, bacterial, or protist infections. They tend to be spread through contaminated food or drinking water, or between people through the fecal-oral route or direct contact.""}, {""type"": ""text"", ""value"": ""There are many public health measures that can prevent diarrheal disease, including sanitation, clean drinking water, pasteurization, food safety, and hand washing with soap.""}]}, {""id"": ""oral-rehydration-therapy"", ""text"": [{""type"": ""text"", ""value"": ""Oral rehydration therapy""}, {""type"": ""text"", ""value"": ""Oral rehydration therapy (ORT) is the use of treatments to replenish fluids lost, especially from diarrhea. It can include oral rehydration salts, recommended home-made fluids, and feeding practices.""}]}, {""id"": ""oral-rehydration-salts"", ""text"": [{""type"": ""text"", ""value"": ""Oral rehydration salts""}, {""type"": ""text"", ""value"": ""Oral rehydration salts (ORS) are a mixture of salt and sugar dissolved in clean water. This mixture is used to treat dehydration caused by severe diarrhea, heat stroke or other conditions.""}, {""type"": ""text"", ""value"": ""It can be easily prepared by community health workers and caregivers at home.""}]}, {""id"": ""e-coli"", ""text"": [{""type"": ""text"", ""value"": ""E. coli""}, {""type"": ""text"", ""value"": """"}, {""type"": ""text"", ""value"": ""Escherichia coli (E. coli) is a bacterium that is commonly found in the gut of humans and warm-blooded animals. Most strains of E. coli are harmless, but some – such as Shiga toxin-producing E. coli (STEC) – can cause severe foodborne disease.""}, {""type"": ""text"", ""value"": ""It can be spread through contaminated foods, such as raw meat, milk, and vegetables, or between people through the fecal-oral route or direct contact.""}]}, {""id"": ""typhoid-fever"", ""text"": [{""type"": ""text"", ""value"": ""Typhoid fever""}, {""type"": ""text"", ""value"": ""Typhoid fever is a life-threatening infection caused by the bacterium Salmonella Typhi. It is usually spread through contaminated food or water. Once Salmonella Typhi bacteria are ingested, they multiply and spread into the bloodstream.""}, {""type"": ""text"", ""value"": ""Symptoms include prolonged high fever, fatigue, headache, nausea, abdominal pain, and constipation or diarrhea.""}]}, {""id"": ""paratyphoid-fever"", ""text"": [{""type"": ""text"", ""value"": ""Paratyphoid fever""}, {""type"": ""text"", ""value"": ""Paratyphoid fever is an infection caused by the bacterium Salmonella Paratyphi. It causes similar clinical syndromes to typhoid fever, including fevers, chills, abdominal pain, and can be a life-threatening illness in severe cases.""}]}, {""id"": ""non-typhoidal-salmonella"", ""text"": [{""type"": ""text"", ""value"": ""Invasive non-typhoidal salmonella""}, {""type"": ""text"", ""value"": ""Non-typhoidal salmonellae are a group of bacteria that cause inflammation of the stomach and intestines.""}, {""type"": ""text"", ""value"": ""They can sometimes spread beyond the gut into the bloodstream, causing invasive non-typhoidal Salmonella (iNTS), especially in people who have weak immune systems, for example those with malnutrition or HIV/AIDS.""}]}, {""id"": ""equivalization"", ""text"": [{""type"": ""text"", ""value"": ""Equivalization""}, {""type"": ""text"", ""value"": ""Equivalization is a statistical technique used in household income surveys to allow for fairer comparisons in living standards between households of different sizes or age compositions.""}, {""type"": ""text"", ""value"": ""With each additional member, a household's needs grow but – due to economies of scale in consumption – not in a proportional way. A household with three members does not need three times the housing space, electricity, heating etc. as a single-person household to achieve the same standard of living.""}, {""type"": ""text"", ""value"": ""In the process of equivalization, each household's actual income level is scaled up or down to find the “equivalent” income that would be needed for a given standard household size to achieve that same standard of living. This requires making assumptions about the economies of scale in household consumption, and different 'equivalence scales' for making such an adjustment are in common use.""}]}, {""id"": ""v-dem"", ""text"": [{""type"": ""text"", ""value"": ""V-Dem""}, {""type"": ""text"", ""value"": ""The Varieties of Democracy (V-Dem) project publishes data and research on democracy and human rights.""}, {""type"": ""text"", ""value"": ""It relies on evaluations by around 3,500 country experts and supplementary work by its own researchers to assess political institutions and the protection of rights.""}, {""type"": ""text"", ""value"": ""The project is managed by the V-Dem Institute, based at the University of Gothenburg in Sweden.""}, {""type"": ""text"", ""value"": ""Learn more:""}, {""type"": ""text"", ""value"": ""Democracy data: how do researchers measure democracy?""}, {""type"": ""text"", ""value"": ""The ‘Varieties of Democracy’ data: how do researchers measure democracy?""}, {""type"": ""text"", ""value"": ""The ‘Varieties of Democracy’ data: how do researchers measure human rights?""}]}, {""id"": ""polity"", ""text"": [{""type"": ""text"", ""value"": ""Polity""}, {""type"": ""text"", ""value"": ""The Polity project publishes data on democracy based on evaluations of its own researchers.""}, {""type"": ""text"", ""value"": ""The project is managed by the Center for Systemic Peace.""}, {""type"": ""text"", ""value"": ""Learn more: Democracy data: how do researchers measure democracy?""}]}, {""id"": ""freedom-house"", ""text"": [{""type"": ""text"", ""value"": ""Freedom House""}, {""type"": ""text"", ""value"": ""Freedom House publishes data on democracy and human rights.""}, {""type"": ""text"", ""value"": ""It relies on evaluations by country and regional experts as well as their own researchers to assess political institutions and the protection of rights.""}, {""type"": ""text"", ""value"": ""Learn more: Democracy data: how do researchers measure democracy?""}]}, {""id"": ""bertelsmann-transformation-index"", ""text"": [{""type"": ""text"", ""value"": ""Bertelsmann Transformation Index""}, {""type"": ""text"", ""value"": ""The Bertelsmann Transformation Index (BTI) project publishes data and research on democracy and human rights.""}, {""type"": ""text"", ""value"": ""It relies on evaluations by nearly 300 country, regional, and general experts, as well as supplementary representative surveys of regular citizens, to assess political institutions and the protection of rights.""}, {""type"": ""text"", ""value"": ""The project is managed by the Bertelsmann Foundation.""}, {""type"": ""text"", ""value"": ""Learn more: Democracy data: how do researchers measure democracy?""}]}, {""id"": ""economist-intelligence-unit"", ""text"": [{""type"": ""text"", ""value"": ""Economist Intelligence Unit""}, {""type"": ""text"", ""value"": ""The Economist Intelligence Unit publishes data and research on democracy and human rights.""}, {""type"": ""text"", ""value"": ""It relies on evaluations by its own country experts, supplemented by representative surveys of regular citizens to assess political institutions and the protection of rights.""}, {""type"": ""text"", ""value"": ""The Economist Intelligence Unit is the research and analysis division of The Economist Group, the sister company of The Economist newspaper.""}, {""type"": ""text"", ""value"": ""Learn more: Democracy data: how do researchers measure democracy?""}]}, {""id"": ""clean-cooking-fuels"", ""text"": [{""type"": ""text"", ""value"": ""Access to clean fuels for cooking""}, {""type"": ""text"", ""value"": ""Clean cooking fuels are defined as those that, when burned, emit less than the World Health Organization (WHO) recommended amounts of air pollutants (specifically, particulate matter). They reduce the burden of air pollution – and its health impacts – for households that use them.""}, {""type"": ""text"", ""value"": ""The following fuels are defined as ‘clean’: natural gas, biogas, liquefied petroleum gas (LPG), electricity, solar, , and alcohol fuels including ethanol.""}, {""type"": ""text"", ""value"": ""Polluting of ‘non-clean’ fuels include solid fuels such as unprocessed biomass (wood or dung), charcoal, coal, and kerosene.""}, {""type"": ""text"", ""value"": ""Learn more from the World Health Organization (WHO).""}]}, {""id"": ""global-hunger-index"", ""text"": [{""type"": ""text"", ""value"": ""Global Hunger Index""}, {""type"": ""text"", ""value"": ""The Global Hunger Index (GHI) tries to capture the multidimensional nature of hunger, by combining four component indicators into one index score.""}, {""type"": ""text"", ""value"": ""The GHI is calculated from four indicators: undernourishment (the share of people that do not get enough calories to meet their energy requirements); childhood wasting; childhood stunting; and child mortality.""}, {""type"": ""text"", ""value"": ""An increase in a country's GHI score indicates that the hunger situation is worsening, while a decrease in the score indicates an improvement in the hunger situation.""}, {""type"": ""text"", ""value"": ""Learn more from the Global Hunger Index’s methodology.""}]}, {""id"": ""food-loss-index"", ""text"": [{""type"": ""text"", ""value"": ""Food loss index""}, {""type"": ""text"", ""value"": ""The food loss index measures how losses in the food supply chain have changed relative to 2015.""}, {""type"": ""text"", ""value"": ""Food “losses” are defined as the loss or wastage of food from the farm up to the retail level. Pre-harvest losses, and retail and consumer waste are not included.""}, {""type"": ""text"", ""value"": ""Food losses are measured across five food groups: cereals & pulses; fruits & vegetables; roots, tubers and oil crops; animal products; and seafood.""}, {""type"": ""text"", ""value"": ""The percentage of food that is lost across the supply chain in 2015 is given a value of 100 as a baseline. If the index value is greater than 100 then food losses have increased since 2015. If the value is less than 100, they’ve decreased.""}]}, {""id"": ""biomass"", ""text"": [{""type"": ""text"", ""value"": ""Biomass""}, {""type"": ""text"", ""value"": ""Biomass is a unit used as a way of measuring the amount of living material in a given environment.""}]}, {""id"": ""chlorophyll-a"", ""text"": [{""type"": ""text"", ""value"": ""Chlorophyll-a""}, {""type"": ""text"", ""value"": ""Chlorophyll-a is a widely used proxy for phytoplankton biomass and an indicator for changes in phytoplankton production.""}, {""type"": ""text"", ""value"": ""High levels of chlorophyll-a indicate pollution from sources suce as fertilizers, sewage or urban runoff.""}]}, {""id"": ""oda"", ""text"": [{""type"": ""text"", ""value"": ""Official Development Assistance""}, {""type"": ""text"", ""value"": ""Financial flows to countries and territories on the Development Assistance Committee list of recipients and to multilateral development institutions that are:""}, {""type"": ""text"", ""value"": ""1) Provided by official agencies, including state and local governments, or by their executive agencies""}, {""type"": ""text"", ""value"": ""2) Are administered with the promotion of the economic development and welfare of developing countries as their main objective""}, {""type"": ""text"", ""value"": ""3) Are concessional in character, with a grant element of 10-45 per cent, depending on the type of loan and the specific recipient.""}]}, {""id"": ""fdi"", ""text"": [{""type"": ""text"", ""value"": ""Foreign direct investment""}, {""type"": ""text"", ""value"": ""A financial transaction to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor.""}, {""type"": ""text"", ""value"": ""It includes equity capital, reinvestment of earnings, and other long-term capital.""}]}, {""id"": ""per-capita"", ""text"": [{""type"": ""text"", ""value"": ""Per capita""}, {""type"": ""text"", ""value"": ""'Per capita' here means that each person (including children) is attributed an equal share of the total income received by all members of their household.""}]}, {""id"": ""gini"", ""text"": [{""type"": ""text"", ""value"": ""Gini coefficient""}, {""type"": ""text"", ""value"": ""The Gini coefficient is the most commonly used measure of inequality.""}, {""type"": ""text"", ""value"": ""It is typically used as a measure of income inequality, but it can be used to measure the inequality of any distribution – such as the distribution of wealth, or even life expectancy.""}, {""type"": ""text"", ""value"": ""It measures inequality on a scale from 0 to 1, where higher values indicate higher inequality. This can sometimes be shown as a percentage from 0 to 100%, this is then called the ‘Gini Index’.""}, {""type"": ""text"", ""value"": ""A value of 0 indicates perfect equality – where everyone has the same income. A value of 1 indicates perfect inequality – where one person receives all the income, and everyone else receives nothing.""}, {""type"": ""text"", ""value"": ""Read more in our article: Measuring inequality: What is the Gini coefficient?""}]}, {""id"": ""atkinson"", ""text"": [{""type"": ""text"", ""value"": ""Atkinson index""}, {""type"": ""text"", ""value"": ""The Atkinson index is a measure of inequality.""}, {""type"": ""text"", ""value"": ""It is typically used as a measure of income inequality, but it can be used to measure the inequality of any distribution – such as the distribution of wealth, or even life expectancy.""}, {""type"": ""text"", ""value"": ""It measures inequality on a scale from 0 to 1, where higher values indicate higher inequality. This can sometimes be shown as a percentage from 0 to 100%.""}, {""type"": ""text"", ""value"": ""A value of 0 indicates perfect equality – where everyone has the same income. A value of 1 indicates perfect inequality – where one person receives all the income, and everyone else receives nothing.""}, {""type"": ""text"", ""value"": ""There are different varieties of the index, depending on ε, the inequality aversion parameter. The parameter defines how sensitive the index is to changes in the lower end of the distribution.""}]}, {""id"": ""forest-resources"", ""text"": [{""type"": ""text"", ""value"": ""Forest resources""}, {""type"": ""text"", ""value"": ""Forest resources include wood and non-wood forest products, the protection of soil and water, biodiversity conservation, social and cultural uses, and any combination of these.""}]}, {""id"": ""forest-certification-schemes"", ""text"": [{""type"": ""text"", ""value"": ""Forest certification schemes""}, {""type"": ""text"", ""value"": ""Forest certification schemes aim to ensure the sustainable use of forest products and ecosystem services.""}, {""type"": ""text"", ""value"": ""Independently verified forest certification schemes include the Forest Stewardship Council (FSC) and the Programme for the Endorsement of Forest Certification (PEFC).""}, {""type"": ""text"", ""value"": ""Forest certification is voluntary and market-based, therefore there are significant areas of sustainably managed forest that are not certified.""}]}, {""id"": ""land-degradation"", ""text"": [{""type"": ""text"", ""value"": ""Land degradation""}, {""type"": ""text"", ""value"": ""Land degradation is defined as the reduction or loss of the biological or economic productivity and complexity of rain-fed cropland, irrigated cropland, or range, pasture, forest and woodlands resulting from a combination of pressures, including land use and management practices.""}]}, {""id"": ""tourism-adjusted"", ""text"": [{""type"": ""text"", ""value"": ""Tourism adjusted""}, {""type"": ""text"", ""value"": ""To adjust for tourism emissions from international flights are multiplied by the ratio of outbound-to-inbound travelers. A high outbound-to-inbound tourist ratio suggests that a country's residents travel abroad more often than it receives visitors, resulting in a relatively lower burden of emissions from international flights being allocated to the country whose residents don't travel internationally as frequently.""}]}, {""id"": ""unclos"", ""text"": [{""type"": ""text"", ""value"": ""United Nations Convention on the Law of the Sea""}, {""type"": ""text"", ""value"": ""The United Nations Convention on the Law of the Sea (UNCLOS) was adopted in 1982. It lays down a comprehensive regime of law and order in the world's oceans and seas establishing rules governing all uses of the oceans and their resources.""}, {""type"": ""text"", ""value"": ""It embodies in one instrument traditional rules for the uses of the oceans and at the same time introduces new legal concepts and regimes and addresses new concerns. It enshrines the notion that all problems of ocean space are closely interrelated and need to be addressed as a whole.""}]}, {""id"": ""economic-complexity"", ""text"": [{""type"": ""text"", ""value"": ""Economic Complexity Index""}, {""type"": ""text"", ""value"": ""The Economic Complexity Index (ECI) is a measure of the productive capabilities of a country, proposed by Ricardo Hausmann and Cesar Hidalgo. Productive capabilities are defined by them as all the inputs, technologies and ideas that, in combination, determine the frontiers of what an economy can produce.""}, {""type"": ""text"", ""value"": ""The ECI takes data on exports, and reduces a country’s economic system into two dimensions:""}, {""type"": ""text"", ""value"": ""- The diversity of products in the export basket.""}, {""type"": ""text"", ""value"": ""- The ubiquity of products in the export basket.""}, {""type"": ""text"", ""value"": ""‘Diversity’ is the number of products that a country can export competitively; and ‘ubiquity’ is the number of countries that are able to export a product competitively.""}, {""type"": ""text"", ""value"": ""Read more in our article: How and why should we study ‘economic complexity’?""}]}, {""id"": ""ai-MMLU"", ""text"": [{""type"": ""text"", ""value"": ""MMLU benchmark""}, {""type"": ""text"", ""value"": ""The Massive Multitask Language Understanding (MMLU) benchmark mimics a multiple-choice knowledge quiz designed to gauge how proficiently AI systems can comprehend various topics like history, science, or psychology. It has 57 different sections, each one looking at a particular subject.""}, {""type"": ""text"", ""value"": ""The MMLU test has 15,908 questions in total, which are split up into smaller sets. There are at least 100 questions about each subject. The questions in the test come from many places, like practice tests for big exams or questions from university courses. The difficulty of the questions varies, some are as easy as elementary school level, while others are as hard as what professionals in a field might know.""}, {""type"": ""text"", ""value"": ""The scores achieved by humans on this test are largely dependent on their level of expertise in the subject matter. Individuals who are not specialists in a given area typically achieve a correctness rate of around 34.5%. However, those with a deep understanding and proficiency in their field, such as doctors sitting for a medical examination, can attain a high score of up to 89.8% on the test.""}]}, {""id"": ""ai-MATH"", ""text"": [{""type"": ""text"", ""value"": ""MATH benchmark""}, {""type"": ""text"", ""value"": ""The MATH benchmark tests the performance of AI systems on mathematical problems. It comprises 12,500 questions sourced from high-school math competitions, each accompanied by a comprehensive, step-by-step solution. These solutions help researchers in training AI systems to generate accurate answers and provide explanations.""}, {""type"": ""text"", ""value"": ""In the MATH benchmark, AI systems are presented with a problem and their task is to produce a sequence, like a fraction (for example, 2/3), that represents the final answer. Each question has one unique correct response which means that the scoring can be based on whether the AI got the exact right answer.""}, {""type"": ""text"", ""value"": ""A computer science PhD student who does not especially like mathematics scores approximately 40% on MATH, while a three-time IMO gold medalist can attain a score of 90%.""}]}, {""id"": ""ai-APPS"", ""text"": [{""type"": ""text"", ""value"": ""APPS benchmark""}, {""type"": ""text"", ""value"": ""The APPS (Automated Programming Progress Standard) benchmark is a tool to test how well AI systems can write code. It has a total of 10,000 coding challenges that the AI needs to solve. To check if the AI's solutions are correct, there are 131,836 test scenarios. There are also 232,444 human-made solutions that serve as the \""correct answers\"" to compare with the AI's output. The accuracy of an AI system in this test is determined by the percentage of test scenarios it gets right.""}, {""type"": ""text"", ""value"": ""Interview level problems are equivalent to those posed in challenging technical interviews. They often deal with certain kinds of data arrangements, like trees or graphs, or require tweaks to common coding techniques. Out of the total 10,000 problems, half of them, or 5,000, are at this 'interview level'. Of these, 3,000 are used for testing purposes.""}, {""type"": ""text"", ""value"": ""Competition level problems are coding challenges that are on par with the highest-tier high school and collegiate programming competitions, such as USACO, IOI, and ACM. Out of the total problems, 1,361 are classified as 'competition level', with 1,000 of these being part of the test set.""}]}, {""id"": ""ai-llms"", ""text"": [{""type"": ""text"", ""value"": ""Large language models""}, {""type"": ""text"", ""value"": ""Large language models, such as GPT-4, are AI systems designed to understand and generate human-like text. These models are \""trained\"" on vast amounts of text data, allowing them to predict the next word in a sentence, which helps them generate coherent and contextually relevant responses or text passages.""}]}, {""id"": ""ai-systems"", ""text"": [{""type"": ""text"", ""value"": ""AI systems""}, {""type"": ""text"", ""value"": ""AI systems, or Artificial Intelligence systems, are computer-based systems that aim to mimic or replicate human intelligence and perform tasks that typically require human intelligence. These systems use various algorithms, data, and models to analyze, interpret, and generate meaningful responses or actions.""}, {""type"": ""text"", ""value"": ""AI systems encompass a broad range of technologies and applications, including machine learning, natural language processing, computer vision, robotics, and expert systems. They can process large amounts of data, learn from patterns and examples, make decisions, solve problems, and interact with humans in a way that simulates human intelligence.""}, {""type"": ""text"", ""value"": ""These systems can be designed for specific tasks, such as language translation, image recognition, speech synthesis, or they can be more general-purpose, capable of performing a wide range of cognitive tasks. They can be found in various domains, including healthcare, finance, transportation, customer service, and many others.""}]}, {""id"": ""ai-inflation"", ""text"": [{""type"": ""text"", ""value"": ""adjusted for inflation""}, {""type"": ""text"", ""value"": ""Reporting a time series of AI investments in nominal prices (i.e., without adjusting for inflation) means it makes little sense to compare observations across time; it is therefore not very useful. To make comparisons across time possible, one has to take into account that prices change (e.g., there is inflation).""}, {""type"": ""text"", ""value"": ""It is not obvious how to adjust this time series for inflation, and we debated it at some length within our team.""}, {""type"": ""text"", ""value"": ""It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services that these investments purchased. This would make it possible to calculate a volume measure of AI investments, and it would tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost for some crucial AI technology has fallen rapidly in price.""}, {""type"": ""text"", ""value"": ""In the absence of a comprehensive price index that captures the price of AI-specific goods and services, one has to rely on one of the available metrics for the price of a bundle of goods and services. In the end we decided to use the US Consumer Price Index (CPI).""}, {""type"": ""text"", ""value"": ""The US CPI does not provide us with a volume measure of AI goods and services, but it does capture the opportunity costs of these investments. The inflation adjustment of this time series of AI investments therefore lets us understand the size of these investments relative to whatever else these sums of money could have purchased.""}]}, {""id"": ""ai-computation"", ""text"": [{""type"": ""text"", ""value"": ""Computation""}, {""type"": ""text"", ""value"": ""Training computation, often measured in total FLOP (floating-point operations), refers to the total number of computer operations used to train an AI system. One FLOP corresponds to a single arithmetic operation, such as addition, subtraction, multiplication, or division, performed with floating-point numbers, and one petaFLOP equals one quadrillion (10^15) FLOP.""}, {""type"": ""text"", ""value"": ""The AI systems shown here were built using machine learning and deep learning methods. These involve complex mathematical calculations that require significant computational resources. Training these systems generally involves feeding large amounts of data through various layers and nodes and adjusting internal system parameters over numerous iterations to optimize the system’s performance.""}, {""type"": ""text"", ""value"": ""The training computation used can vary depending on factors such as the size of the dataset, size and complexity of the system architecture, and the level of parallelism used during training, among other reasons.""}]}, {""id"": ""injuries"", ""text"": [{""type"": ""text"", ""value"": ""Injuries""}, {""type"": ""text"", ""value"": ""Injuries include all types of injury, both intentional and accidental, for example: road accidents, poisonings, falls, fires, drowning, natural disasters, self-harm and interpersonal violence.""}]}, {""id"": ""non-communicable-diseases"", ""text"": [{""type"": ""text"", ""value"": ""Non-communicable diseases""}, {""type"": ""text"", ""value"": ""Noncommunicable diseases (NCDs), also known as chronic diseases, tend to be of long duration and are the result of a combination of genetic, physiological, environmental and behavioural factors. The main types of NCD are cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes.""}]}, {""id"": ""communicable-diseases"", ""text"": [{""type"": ""text"", ""value"": ""Communicable diseases""}, {""type"": ""text"", ""value"": ""Communicable, or infectious diseases, are caused by pathogens such as bacteria, viruses, parasites and fungi, and can spread between people, directly or indirectly.""}, {""type"": ""text"", ""value"": ""Communicable diseases spread through different routes such as direct contact, contaminated food or water, breathing or airborne particles, sexual contact, and more.""}, {""type"": ""text"", ""value"": ""They include HIV/AIDS, tuberculosis (TB), malaria, viral hepatitis, sexually transmitted infections and neglected tropical diseases (NTDs), and are among the leading causes of death and disability in low-income countries and marginalized populations.""}]}, {""id"": ""underlying-cause-of-death"", ""text"": [{""type"": ""text"", ""value"": ""Underlying cause of death""}, {""type"": ""text"", ""value"": ""The ‘underlying cause of death’ is defined by the World Health Organization as: a) the disease or injury which initiated the train of morbid events leading directly to death, or b) the circumstances of the accident or violence which produced the fatal injury.""}, {""type"": ""text"", ""value"": ""All deaths may not be registered with a cause of death, especially if there is a lack of medical records for the person deceased, a lack of doctors, nurses, or hospitals nearby, or if the country has a poorly functioning vital registration system.""}, {""type"": ""text"", ""value"": ""📄 You can read more about how causes of death are determined in our article: How are causes of death registered around the world?""}]}, {""id"": ""crvs"", ""text"": [{""type"": ""text"", ""value"": ""Civil Registration and Vital Statistics system""}, {""type"": ""text"", ""value"": ""A Civil Registration and Vital Statistics system (CRVS) is an administrative system in a country that manages information on births, marriages, deaths and divorces. It generates and stores ‘vital records’ and legal documents such as birth certificates and death certificates.""}, {""type"": ""text"", ""value"": ""📄 You can read more about how deaths are registered around the world in our article: How are causes of death registered around the world?""}]}, {""id"": ""verbal-autopsy"", ""text"": [{""type"": ""text"", ""value"": ""Verbal autopsy""}, {""type"": ""text"", ""value"": ""Verbal autopsies are studies in which the relatives/caregivers of a deceased person are interviewed by trained professionals to determine their cause of death.""}, {""type"": ""text"", ""value"": ""These studies are valuable in countries without doctors, nurses, robust hospital records, or where deaths often occur outside the healthcare system.""}, {""type"": ""text"", ""value"": ""📄 You can read more about verbal autopsies and how deaths are determined around the world in our article: How are causes of death registered around the world?""}]}, {""id"": ""hepatitis-virus"", ""text"": [{""type"": ""text"", ""value"": ""Hepatitis virus""}, {""type"": ""text"", ""value"": ""Hepatitis viruses are a group of viruses that cause inflammation of the liver. This results in symptoms like jaundice, fatigue, and liver dysfunction, and can range from a mild illness lasting a few weeks (acute hepatitis) to a severe, lifelong condition (chronic hepatitis) that can include cirrhosis.""}, {""type"": ""text"", ""value"": ""There are five main hepatitis viruses, which are called types A, B, C, D, and E. Each of these viruses can cause liver inflammation, but they vary in how they spread, their severity, and their geographical distribution. For example, Hepatitis A and E are primarily transmitted through contaminated food and water, while B, C, and D are mainly transmitted through blood and body fluids. Hepatitis B and C viruses are also known to increase the risks of liver cancer.""}]}, {""id"": ""cirrhosis"", ""text"": [{""type"": ""text"", ""value"": ""Cirrhosis""}, {""type"": ""text"", ""value"": ""Cirrhosis describes the late stage of liver scarring caused by various liver diseases and conditions, such as hepatitis virus and chronic alcohol abuse.""}, {""type"": ""text"", ""value"": ""Since the liver has many important functions – including detoxifying harmful substances in the body, producing bile for digestion, synthesizing proteins, storing vitamins and minerals, and regulating blood clotting – cirrhosis can lead to complications that can be life-threatening.""}]}, {""id"": ""acute-chronic"", ""text"": [{""type"": ""text"", ""value"": ""Acute vs chronic diseases""}, {""type"": ""text"", ""value"": ""In a medical context, ‘acute’ refers to conditions that arise suddenly or last a short amount of time, while ‘chronic’ refers to conditions that develop gradually over a long period of time and tend to persist.""}]}, {""id"": ""acute-flaccid-paralysis"", ""text"": [{""type"": ""text"", ""value"": ""Acute flaccid paralysis (AFP)""}, {""type"": ""text"", ""value"": ""Acute flaccid paralysis is a sudden onset of paralysis or weakness of the limbs.""}, {""type"": ""text"", ""value"": ""It can be caused by viral infections like poliovirus and enteroviruses, immune system disorders such as Guillain-Barré syndrome, and environmental toxins. It can affect anyone but is especially concerning in children.""}]}, {""id"": ""who-mort-db-homicide"", ""text"": [{""type"": ""text"", ""value"": ""WHO Mortality Database""}, {""type"": ""text"", ""value"": ""The WHO Mortality Database draws on data from national civil registries which collect death certificates where the cause of death is registered. The WHO Mortality Database only publishes data for countries where at least 65% of deaths are registered with a cause.""}]}, {""id"": ""dhs-survey"", ""text"": [{""type"": ""text"", ""value"": ""Demographic and Health Surveys (DHS)""}, {""type"": ""text"", ""value"": ""The Demographic and Health Surveys (DHS) are large-scale national surveys that collect data on population, health, and nutrition.""}, {""type"": ""text"", ""value"": ""The surveys involve interviewing women, men, and households in selected countries, using standardized questionnaires. The data collected provides insights into topics like fertility, family planning, maternal and child health, and infectious diseases.""}, {""type"": ""text"", ""value"": ""Policymakers, researchers, and global health organizations use this information to make informed decisions and monitor health trends.""}]}, {""id"": ""mics-survey"", ""text"": [{""type"": ""text"", ""value"": ""Multiple Indicator Cluster Surveys (MICS)""}, {""type"": ""text"", ""value"": ""The Multiple Indicator Cluster Surveys (MICS) are international household surveys initiated by UNICEF to gather information on the well-being of children and women.""}, {""type"": ""text"", ""value"": ""Conducted in various countries, these surveys utilize standardized questionnaires to collect data on health, education, and other vital indicators.""}, {""type"": ""text"", ""value"": ""The data helps governments and organizations to evaluate progress towards global targets, such as the United Nations’ Sustainable Development Goals (SDGs).""}]}, {""id"": ""unodc-homicide"", ""text"": [{""type"": ""text"", ""value"": ""UN Office on Drugs and Crime""}, {""type"": ""text"", ""value"": ""The UN Office on Drugs and Crime collates homicide data from two main resources: 1) primarily data from national law enforcement and criminal justice authorities, 2) civil registry data from WHO-MD and national authorities""}]}, {""id"": ""history-of-homicide-database"", ""text"": [{""type"": ""text"", ""value"": ""History of Homicide Database""}, {""type"": ""text"", ""value"": ""The History of Homicide Database combines data from national civil registries with historial studies of reports by law enforcement and criminal justice authorities.""}]}, {""id"": ""ihme-homicide"", ""text"": [{""type"": ""text"", ""value"": ""IHME Global Burden of Disease""}, {""type"": ""text"", ""value"": ""The IHME Global Burden of Disease uses statistical models to produce estimates of homicides.The model inputs include civil registry data from the WHO Mortality Database, law enforcement and criminal justice data from the UN Office of Drugs and Crime, and relevant covariates such as the young male population and alcohol consumption.""}]}, {""id"": ""who-ghe-homicide"", ""text"": [{""type"": ""text"", ""value"": ""WHO Global Health Estimates""}, {""type"": ""text"", ""value"": ""The WHO Global Health Estimates data on homicides draws on three main sources: 1) civil registry data from the WHO Mortality Database; 2) law enforcement and criminal justice data from the UN Office of Drugs and Crime; and 3) statistically modelled estimates from the IHME Global Burden of Disease.""}]}, {""id"": ""wvs-trust-groups"", ""text"": [{""type"": ""text"", ""value"": ""The question asked was""}, {""type"": ""text"", ""value"": ""I’d like to ask you how much you trust people from various groups. Could you tell me for each whether you trust people from this group completely, somewhat, not very much or not at all?""}]}, {""id"": ""wvs-trust-orgs"", ""text"": [{""type"": ""text"", ""value"": ""The question asked was""}, {""type"": ""text"", ""value"": ""I am going to name a number of organizations. For each one, could you tell me how much confidence you have in them: is it a great deal of confidence, quite a lot of confidence, not very much confidence or none at all?""}]}, {""id"": ""ppe"", ""text"": [{""type"": ""text"", ""value"": ""Personal Protective Equipment (PPE)""}, {""type"": ""text"", ""value"": ""Personal Protective Equipment (PPE) refers to specialized gear worn to minimize the risk of exposure to hazardous materials or infectious pathogens.""}, {""type"": ""text"", ""value"": ""It includes items like masks, gloves, goggles, gowns, and respirators, which create a barrier between the wearer and potentially harmful substances, thereby reducing risk in various settings such as healthcare, construction, and industrial environments.""}]}, {""id"": ""highly-pathogenic-avian-influenza"", ""text"": [{""type"": ""text"", ""value"": ""Highly-pathogenic avian influenza (HPAI)""}, {""type"": ""text"", ""value"": ""Highly-pathogenic avian influenza (HPAI) is a severe form of bird flu that can quickly spread and cause deadly disease in poultry.""}, {""type"": ""text"", ""value"": ""Some cases have also been recorded in humans, particularly from H5N1 and H7N9 flu strains, and have had a high case fatality rate.""}]}, {""id"": ""case-fatality-rate"", ""text"": [{""type"": ""text"", ""value"": ""Case fatality rate""}, {""type"": ""text"", ""value"": ""The case fatality rate is a measure of the severity of a disease. It is the share of people who die from a disease, out of those diagnosed.""}, {""type"": ""text"", ""value"": ""The case fatality rate can be an overestimate of the true severity of a disease when many mild or asymptomatic cases go undetected or unreported. On the other hand, it can be an underestimate when deaths are unreported more than cases, or when people die from long-term complications of the disease which may not have been considered.""}]}, {""id"": ""terrorism"", ""text"": [{""type"": ""text"", ""value"": ""Terrorist attack""}, {""type"": ""text"", ""value"": ""The Global Terrorism Database defines a terrorist attack as the threat or use of violence to achieve a political, economic, religious or social goal through intimidation or coercion by an actor that is not the state. For an event to be considered terrorism:""}, {""type"": ""text"", ""value"": ""- The perpetrators must be acting intentionally""}, {""type"": ""text"", ""value"": ""- The perpetrators must threaten or use violence against people or property""}, {""type"": ""text"", ""value"": ""- The perpetrators must not be agents of a state""}, {""type"": ""text"", ""value"": ""Furthermore, at least two of the following three criteria must be met:""}, {""type"": ""text"", ""value"": ""- The actions of the perpetrators are in pursuit of a political, economic, religious, or social goal""}, {""type"": ""text"", ""value"": ""- The actions are aimed at intimidating or coercing more than the immediate victims""}, {""type"": ""text"", ""value"": ""- The actions target civilians""}, {""type"": ""text"", ""value"": ""Learn more:""}, {""type"": ""text"", ""value"": ""The Global Terrorism Database: how do researchers measure terrorism?""}]}, {""id"": ""pre-primary-education"", ""text"": [{""type"": ""text"", ""value"": ""Pre-primary education""}, {""type"": ""text"", ""value"": ""Pre-primary education (International Standard Classification of Education Level 02) focuses on peer and educator interactions to enhance children's language and social skills, while also beginning to develop logical reasoning and verbalized thought processes.""}]}, {""id"": ""primary-education"", ""text"": [{""type"": ""text"", ""value"": ""Primary education""}, {""type"": ""text"", ""value"": ""Primary education (International Standard Classification of Education Level 1) aims to impart fundamental literacy and numeracy skills while providing a solid foundation in key knowledge areas and personal and social development, serving as preparation for lower-secondary education with a focus on basic-level learning and minimal specialization.""}]}, {""id"": ""secondary-education"", ""text"": [{""type"": ""text"", ""value"": ""Secondary education""}, {""type"": ""text"", ""value"": ""Secondary education (International Standard Classification of Education Level 2 and 3) completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.""}]}, {""id"": ""lower-secondary-education"", ""text"": [{""type"": ""text"", ""value"": ""Lower-secondary education""}, {""type"": ""text"", ""value"": ""Lower-secondary education (International Standard Classification of Education Level 2) lays the foundation for lifelong learning and broader educational opportunities through subject-specific theoretical instruction.""}]}, {""id"": ""upper-secondary-education"", ""text"": [{""type"": ""text"", ""value"": ""Upper-secondary education""}, {""type"": ""text"", ""value"": ""Upper-secondary education (International Standard Classification of Education Level 3) prepares students for tertiary education and employment by offering specialized, in-depth instruction that is more varied and differentiated than lower secondary education (International Standard Classification of Education Level 2).""}]}, {""id"": ""tertiary-education"", ""text"": [{""type"": ""text"", ""value"": ""Tertiary education""}, {""type"": ""text"", ""value"": ""Tertiary education (International Standard Classification of Education Level 5 to 8) expands upon secondary education by offering specialized learning activities in various fields. It targets advanced levels of complexity and specialization, encompassing both academic and advanced vocational or professional education.""}]}, {""id"": ""adjusted-gpi"", ""text"": [{""type"": ""text"", ""value"": ""Adjusted gender parity index""}, {""type"": ""text"", ""value"": ""Adjusted gender parity index (GPI) is calculated by dividing the female indicator value by the male value. To ensure symmetry around 1, when the ratio is greater than 1, the adjusted GPI is calculated as the ratio of male to female values and the ratio is subtracted from 2.""}]}, {""id"": ""lays"", ""text"": [{""type"": ""text"", ""value"": ""Learning-adjusted years""}, {""type"": ""text"", ""value"": ""Learning-adjusted years (LAYS) is computed by adjusting the expected years of school based on the quality of learning, as measured by the harmonized test scores from various international student achievement testing programs. The adjustment involves multiplying the expected years of school by the ratio of the most recent harmonized test score to 625. Here, 625 signifies advanced attainment on the TIMSS (Trends in International Mathematics and Science Study) test, with 300 representing minimal attainment. These scores are measured in TIMSS-equivalent units.""}]}, {""id"": ""harmonized-scores"", ""text"": [{""type"": ""text"", ""value"": ""Harmonized test scores""}, {""type"": ""text"", ""value"": ""Harmonized test scores consolidate data from several international student achievement testing programs, enabling a standardized comparison of educational attainment across different educational systems and cultures. These scores are measured in TIMSS (Trends in International Mathematics and Science Study) - equivalent units, with 300 denoting minimal attainment and 625 representing advanced attainment.""}]}, {""id"": ""armed-conflict-ucdp"", ""text"": [{""type"": ""text"", ""value"": ""Armed conflict (UCDP and PRIO)""}, {""type"": ""text"", ""value"": ""A disagreement between organized groups, or between one organized group and civilians, that causes at least 25 deaths during a year. This includes combatant and civilian deaths due to fighting, but excludes deaths due to disease and starvation resulting from the conflict.""}]}, {""id"": ""interstate-ucdp"", ""text"": [{""type"": ""text"", ""value"": ""Interstate conflict (UCDP and PRIO)""}, {""type"": ""text"", ""value"": ""A conflict between states that causes at least 25 deaths during a year. This includes combatant and civilian deaths due to fighting, but excludes deaths due to disease and starvation resulting from the conflict.""}]}, {""id"": ""intrastate-ucdp"", ""text"": [{""type"": ""text"", ""value"": ""Intrastate conflict (UCDP and PRIO)""}, {""type"": ""text"", ""value"": ""A conflict between a state and a non-state armed group inside the state’s territory that causes at least 25 deaths during a year. This includes combatant and civilian deaths due to fighting, but excludes deaths due to disease and starvation resulting from the conflict.""}, {""type"": ""text"", ""value"": ""If a foreign state is involved, it is called “internationalized”, and “non-internationalized” otherwise.""}]}, {""id"": ""extrasystemic-ucdp"", ""text"": [{""type"": ""text"", ""value"": ""Extrasystemic conflict (UCDP and PRIO)""}, {""type"": ""text"", ""value"": ""A conflict between a state and a non-state armed group outside the state’s territory that causes at least 25 deaths during a year. This includes combatant and civilian deaths due to fighting, but excludes deaths due to disease and starvation resulting from the conflict.""}]}, {""id"": ""nonstate-ucdp"", ""text"": [{""type"": ""text"", ""value"": ""Non-state conflict (UCDP)""}, {""type"": ""text"", ""value"": ""A conflict between non-state armed groups, such as rebel groups, criminal organizations, or ethnic groups, that causes at least 25 deaths during a year. This includes combatant and civilian deaths due to fighting, but excludes deaths due to disease and starvation resulting from the conflict.""}]}, {""id"": ""onesided-ucdp"", ""text"": [{""type"": ""text"", ""value"": ""One-sided violence (UCDP)""}, {""type"": ""text"", ""value"": ""The use of armed force by a state or non-state armed group against civilians that causes at least 25 civilian deaths during a year. It excludes deaths due to disease and starvation resulting from the violence.""}]}, {""id"": ""primary-participant-ucdp"", ""text"": [{""type"": ""text"", ""value"": ""Primary participants (UCDP)""}, {""type"": ""text"", ""value"": ""Those participants that have the main disagreement of the conflict.""}]}, {""id"": ""conventional-war-mars"", ""text"": [{""type"": ""text"", ""value"": ""Conventional war (Project Mars)""}, {""type"": ""text"", ""value"": ""A conflict between combatants with differentiated militaries and clear frontlines that causes at least 500 deaths over its duration. This includes combatant deaths due to fighting, but excludes deaths due to disease and starvation resulting from the conflict.""}]}, {""id"": ""civil-war-mars"", ""text"": [{""type"": ""text"", ""value"": ""Civil war (Project Mars)""}, {""type"": ""text"", ""value"": ""A conflict between combatants that were previously part of the same state, but now at least one seeks control or secession. It is fought with differentiated militaries and clear frontlines and causes at least 500 deaths over its duration. This includes combatant deaths due to fighting, but excludes deaths due to disease and starvation resulting from the conflict.""}]}, {""id"": ""interstate-war-mars"", ""text"": [{""type"": ""text"", ""value"": ""Interstate war (Project Mars)""}, {""type"": ""text"", ""value"": ""A conflict between states with differentiated militaries and clear frontlines that causes at least 500 deaths over its duration. This includes combatant deaths due to fighting, but excludes deaths due to disease and starvation resulting from the conflict.""}]}, {""id"": ""major-participant-mars"", ""text"": [{""type"": ""text"", ""value"": ""Major participant (Project Mars)""}, {""type"": ""text"", ""value"": ""A participant that suffers at least 1% of deaths in the conflict overall, or deploys at least 5% of its total combatants.""}]}, {""id"": ""force-mic"", ""text"": [{""type"": ""text"", ""value"": ""Threats, displays, and uses of force (Militarized Interstate Confrontations)""}, {""type"": ""text"", ""value"": ""A threat of force can be unspecified, or entail threatening to declare war or to occupy territory; a display of force can entail shows of force or border violations; a use of force can entail attacks, clashes, or battles.""}]}, {""id"": ""interstate-war-mic"", ""text"": [{""type"": ""text"", ""value"": ""Interstate war (Militarized Interstate Confrontations)""}, {""type"": ""text"", ""value"": ""A conflict between states that causes at least 1,000 deaths during a year. This includes combatant deaths due to fighting, but excludes deaths due to disease and starvation resulting from the conflict.""}]}, {""id"": ""force-cow"", ""text"": [{""type"": ""text"", ""value"": ""Threats, displays, and uses of force (Correlates of War)""}, {""type"": ""text"", ""value"": ""A threat of force can be unspecified, or entail threatening to declare war or to occupy territory; a display of force can entail shows of force or border violations; a use of force can entail attacks, clashes, or battles.""}]}, {""id"": ""war-cow"", ""text"": [{""type"": ""text"", ""value"": ""War (Correlates of War)""}, {""type"": ""text"", ""value"": ""A conflict between armed groups that causes at least 1,000 deaths during a year. This includes combatant deaths due to fighting, disease, and starvation.""}]}, {""id"": ""interstate-war-cow"", ""text"": [{""type"": ""text"", ""value"": ""Interstate war (Correlates of War)""}, {""type"": ""text"", ""value"": ""A conflict between states that causes at least 1,000 deaths during a year. This includes combatant deaths due to fighting, disease, and starvation.""}]}, {""id"": ""intrastate-war-cow"", ""text"": [{""type"": ""text"", ""value"": ""Intrastate war (Correlates of War)""}, {""type"": ""text"", ""value"": ""A conflict between a state and a non-state armed group, or between non-state armed groups, that causes at least 1,000 deaths during a year. This includes combatant deaths due to fighting, disease, and starvation.""}, {""type"": ""text"", ""value"": ""If a foreign state is involved, it is called “internationalized”, and “non-internationalized” otherwise.""}]}, {""id"": ""extrastate-war-cow"", ""text"": [{""type"": ""text"", ""value"": ""Extrastate war (Correlates of War)""}, {""type"": ""text"", ""value"": ""A conflict between a state and a non-state region it seeks to control, or between a state and a colony that seeks policy change or independence, that causes at least 1,000 deaths during a year. This includes combatant deaths due to fighting, disease, and starvation.""}]}, {""id"": ""nonstate-war-cow"", ""text"": [{""type"": ""text"", ""value"": ""Non-state war (Correlates of War)""}, {""type"": ""text"", ""value"": ""A conflict between non-state armed groups within a region without a state or across state borders, that causes at least 1,000 deaths during a year. This includes combatant deaths due to fighting, disease, and starvation.""}]}, {""id"": ""violent-political-conflict-catalog"", ""text"": [{""type"": ""text"", ""value"": ""Violent political conflict (Conflict Catalog)""}, {""type"": ""text"", ""value"": ""A conflict between states, a state and a non-state armed group, between non-state armed groups, or between an armed group and civilians, that causes at least 32 deaths during a year. This includes combatant and civilian deaths due to fighting, disease, and starvation.""}]}, {""id"": ""war"", ""text"": [{""type"": ""text"", ""value"": ""War""}, {""type"": ""text"", ""value"": ""An especially deadly conflict, commonly defined to have at least 500 or 1,000 annual or total deaths.""}]}, {""id"": ""interstate-conflict"", ""text"": [{""type"": ""text"", ""value"": ""Interstate conflict""}, {""type"": ""text"", ""value"": ""A conflict between states.""}]}, {""id"": ""intrastate-conflict"", ""text"": [{""type"": ""text"", ""value"": ""Intrastate conflict""}, {""type"": ""text"", ""value"": ""A conflict between a state and a non-state armed group within the state’s territory. If a foreign state is involved, it is sometimes called “internationalized”, and “non-internationalized” otherwise.""}]}, {""id"": ""extrastate-conflict"", ""text"": [{""type"": ""text"", ""value"": ""Extrastate conflict""}, {""type"": ""text"", ""value"": ""A conflict between a state and a non-state armed group outside the state’s territory.""}]}, {""id"": ""statebased-conflict"", ""text"": [{""type"": ""text"", ""value"": ""State-based conflict""}, {""type"": ""text"", ""value"": ""A conflict either between states (interstate), between a state and a non-state armed group inside its territory (intrastate), or between a state and a non-state armed group outside its territory (extrastate).""}]}, {""id"": ""nonstate-conflict"", ""text"": [{""type"": ""text"", ""value"": ""Non-state conflict""}, {""type"": ""text"", ""value"": ""A conflict between non-state armed groups.""}]}, {""id"": ""onesided-violence"", ""text"": [{""type"": ""text"", ""value"": ""One-sided violence""}, {""type"": ""text"", ""value"": ""The use of armed force by a state or non-state armed group against civilians.""}]}, {""id"": ""peace-diehl"", ""text"": [{""type"": ""text"", ""value"": ""Relationships between two countries""}, {""type"": ""text"", ""value"": ""Severe rivalry: important unresolved issues, frequent and severe violence, hostile diplomacy, and limited communication.""}, {""type"": ""text"", ""value"": ""Lesser rivalry: important unresolved issues, infrequent and limited violence, hostile diplomacy, and limited communication.""}, {""type"": ""text"", ""value"": ""Negative peace: some important issues resolved, rarely use violence, maintain plans for war, predominantly use diplomacy, and common communication between governments.""}, {""type"": ""text"", ""value"": ""Warm peace: main issues resolved, violence unthinkable, firm diplomatic and societal relations, and coordinate some policies.""}, {""type"": ""text"", ""value"": ""Security community: resolved main issues, violence unthinkable, joint war plans, coordinate diplomacy, harmonize policies, and share identities and values.""}]}, {""id"": ""severe-rivalry"", ""text"": [{""type"": ""text"", ""value"": ""Severe rivalry""}, {""type"": ""text"", ""value"": ""Two countries have important unresolved issues, and handle them with frequent and severe violence. Their diplomacy is hostile, and their communication is limited.""}]}, {""id"": ""lesser-rivalry"", ""text"": [{""type"": ""text"", ""value"": ""Lesser rivalry""}, {""type"": ""text"", ""value"": ""Two countries have important unresolved issues, and handle them with infrequent and limited violence. Their diplomacy is hostile, and their communication is limited.""}]}, {""id"": ""negative-peace"", ""text"": [{""type"": ""text"", ""value"": ""Negative peace""}, {""type"": ""text"", ""value"": ""Two countries have resolved some of their important issues, and rarely handle the remaining ones with violence, but they maintain plans for war. They predominantly use diplomacy, and communication between their governments is common.""}]}, {""id"": ""warm-peace"", ""text"": [{""type"": ""text"", ""value"": ""Warm peace""}, {""type"": ""text"", ""value"": ""Two countries have resolved their main issues and violence between them is unthinkable. They have firm diplomatic and societal relations, and coordinate some of their policies.""}]}, {""id"": ""security-community"", ""text"": [{""type"": ""text"", ""value"": ""Security community""}, {""type"": ""text"", ""value"": ""Two countries have resolved their main issues, violence between them is unthinkable, and the war plans they maintain they make together. They coordinate their diplomacy, harmonize their policies, and share identities and values.""}]}, {""id"": ""oecd-regions"", ""text"": [{""type"": ""text"", ""value"": ""OECD regions""}, {""type"": ""text"", ""value"": ""The definitions of regions, as stipulated by the OECD, are:""}, {""type"": ""text"", ""value"": ""- Other OECD America: Chile, Colombia, Costa Rica, Mexico""}, {""type"": ""text"", ""value"": ""- OECD EU countries : Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden""}, {""type"": ""text"", ""value"": ""- OECD Non-EU countries: Iceland, Israel, Norway, Switzerland, Turkey, United Kingdom""}, {""type"": ""text"", ""value"": ""- OECD Oceania: Australia, New Zealand""}, {""type"": ""text"", ""value"": ""- OECD Asia: Japan, Korea""}, {""type"": ""text"", ""value"": ""- Latin America: Non-OECD Latin American and Caribbean countries""}, {""type"": ""text"", ""value"": ""- Other EU: Bulgaria, Croatia, Cyprus, Malta, Romania""}, {""type"": ""text"", ""value"": ""- Other Eurasia: Non-OECD European and Caspian countries, including Russian Federation""}, {""type"": ""text"", ""value"": ""- Middle East & North Africa: Algeria, Bahrain, Egypt, Iraq, Islamic Rep. of Iran, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Tunisia, United Arab Emirates, Syrian Arab Rep., Western Sahara, Yemen""}, {""type"": ""text"", ""value"": ""- Other Africa: Sub-Saharan Africa""}, {""type"": ""text"", ""value"": ""- China: People’s Republic of China, Hong Kong (China)""}, {""type"": ""text"", ""value"": ""- Other non-OECD Asia: Other non-OECD Asian and Pacific countries""}]}, {""id"": ""period-life-expectancy"", ""text"": [{""type"": ""text"", ""value"": ""Period life expectancy""}, {""type"": ""text"", ""value"": ""Period life expectancy is a metric that summarizes death rates across all age groups in one particular year.""}, {""type"": ""text"", ""value"": ""For a given year, it represents the average lifespan for a hypothetical group of people, if they experienced the same age-specific death rates throughout their whole lives as the age-specific death rates seen in that particular year.""}, {""type"": ""text"", ""value"": ""Learn more in our article: “Life expectancy” – What does this actually mean?""}]}, {""id"": ""cohort-life-expectancy"", ""text"": [{""type"": ""text"", ""value"": ""Cohort life expectancy""}, {""type"": ""text"", ""value"": ""Cohort life expectancy is the average lifespan of a group of people, usually a birth cohort – people born in the same year.""}, {""type"": ""text"", ""value"": ""It is calculated by tracking individuals from that cohort throughout their lives until death, and calculating their average lifespan.""}, {""type"": ""text"", ""value"": ""Learn more in our article: “Life expectancy” – What does this actually mean?""}]}, {""id"": ""period-measure"", ""text"": [{""type"": ""text"", ""value"": ""Period measure""}, {""type"": ""text"", ""value"": ""Period measures provide a snapshot of data across various age groups at a specific point in time. They reflect the impact of immediate events and conditions.""}, {""type"": ""text"", ""value"": ""Learn more in our article: Period versus cohort measures: what’s the difference?""}]}, {""id"": ""cohort-measure"", ""text"": [{""type"": ""text"", ""value"": ""Cohort measure""}, {""type"": ""text"", ""value"": ""Cohort measures track specific groups – like birth cohorts – over time, capturing their experiences and outcomes.""}, {""type"": ""text"", ""value"": ""They provide insights into generational effects and historical experiences, although they require long-term data collection before they can be calculated.""}, {""type"": ""text"", ""value"": ""Learn more in our article: Period versus cohort measures: what’s the difference?""}]}, {""id"": ""infant"", ""text"": [{""type"": ""text"", ""value"": ""Infant""}, {""type"": ""text"", ""value"": ""Infancy is defined as being under the age of one year old.""}, {""type"": ""text"", ""value"": ""Read more in our article: How do statistical organizations define age periods for children?""}]}, {""id"": ""preterm-birth-complications"", ""text"": [{""type"": ""text"", ""value"": ""Preterm birth complications""}, {""type"": ""text"", ""value"": ""Preterm birth complications are conditions including impaired respiration, difficulty in feeding, poor body temperature regulation, and higher risk of infection, which preterm infants are more vulnerable to.""}]}, {""id"": ""newborn"", ""text"": [{""type"": ""text"", ""value"": ""Newborn""}, {""type"": ""text"", ""value"": ""A newborn is defined as a baby born alive, and usually refers to neonates – under 28 days old.""}, {""type"": ""text"", ""value"": ""Read more in our article: How do statistical organizations define age periods for children?""}]}, {""id"": ""neonatal"", ""text"": [{""type"": ""text"", ""value"": ""Neonatal""}, {""type"": ""text"", ""value"": ""Neonatal is defined as the period of age under 28 days old.""}, {""type"": ""text"", ""value"": ""Within this age period, there are two categories: early neonatal (under 7 days) and late neonatal (7–27 days).""}, {""type"": ""text"", ""value"": ""Read more in our article: How do statistical organizations define age periods for children?""}]}, {""id"": ""stillbirth"", ""text"": [{""type"": ""text"", ""value"": ""Stillbirth""}, {""type"": ""text"", ""value"": ""A stillbirth is defined as a baby born with no signs of life after a specific time of gestation.""}, {""type"": ""text"", ""value"": ""However, the precise definition varies between organizations.""}, {""type"": ""text"", ""value"": ""For example, the International Classification of Diseases 11th Revision (ICD-11) defines stillbirths as babies born with no signs of life at 22 or more completed weeks of gestation. They also have two sub-categories of stillbirths: ‘early gestation stillbirth’ (at 22 to 27 completed weeks of gestation) and ‘late gestation stillbirth’ (at 28 or more completed weeks of gestation).""}, {""type"": ""text"", ""value"": ""In contrast, the WHO and UN IGME define stillbirths as those at 28 or more completed weeks of gestation, which corresponds to what the ICD-11 calls late gestation stillbirths.""}, {""type"": ""text"", ""value"": ""Read more in our article: How do statistical organizations define age periods for children?""}]}, {""id"": ""cholesterol"", ""text"": [{""type"": ""text"", ""value"": ""Cholesterol""}, {""type"": ""text"", ""value"": ""Cholesterol is a waxy, fat-like substance found in all cells of the body. It’s crucial for creating cell membranes, certain hormones, vitamin D, and substances that help with digestion. The body naturally produces all the cholesterol it needs, with additional intake from food.""}, {""type"": ""text"", ""value"": ""Cholesterol travels through the bloodstream while attached to proteins called lipoproteins. These come in two types:""}, {""type"": ""text"", ""value"": ""1. High-Density Lipoprotein (HDL), often known as “good cholesterol”, transports cholesterol from various parts of the body back to the liver, which removes it from the body. This prevents cholesterol from accumulating in blood vessels, thereby reducing the risk of heart disease.""}, {""type"": ""text"", ""value"": ""2. Low-Density Lipoprotein (LDL) and Very-Low-Density Lipoprotein (VLDL), collectively known as non-HDL or “bad cholesterol”, transport cholesterol to the arteries. High levels can build up in the walls of blood vessels, which is called atherosclerosis, and narrows the space for blood to travel through and limits blood flow to organs. This increases the risk of heart disease and other cardiovascular diseases.""}]}, {""id"": ""defined-daily-doses"", ""text"": [{""type"": ""text"", ""value"": ""Defined Daily Doses""}, {""type"": ""text"", ""value"": ""Defined Daily Doses (DDDs) is a metric to describe the usage of a medication in a population.""}, {""type"": ""text"", ""value"": ""It’s calculated by taking the total amount of the medication used and dividing it by the total population. This helps to standardize and compare medication use in different places and times, which can be useful for healthcare research and policy decisions.""}]}, {""id"": ""statins"", ""text"": [{""type"": ""text"", ""value"": ""Statins""}, {""type"": ""text"", ""value"": ""Statins are medicines that help lower high cholesterol in the blood. They are often given to people with heart diseases, high cholesterol or those at risk of heart problems. Statins reduce levels of non-HDL cholesterol, and are commonly prescribed alongside lifestyle changes, such as a healthy diet and exercise.""}]}, {""id"": ""mri-imaging"", ""text"": [{""type"": ""text"", ""value"": ""Magnetic Resonance Imaging (MRI)""}, {""type"": ""text"", ""value"": ""Magnetic Resonance Imaging (MRI) is a medical imaging technique that utilizes powerful magnets and radio waves to produce detailed images of internal body structures. MRI is known for its safety and is used for diagnosing various medical conditions, including those affecting the brain, spine, joints, liver, kidneys, breasts, heart, and blood vessels.""}]}, {""id"": ""gamma-camera-imaging"", ""text"": [{""type"": ""text"", ""value"": ""Gamma Camera""}, {""type"": ""text"", ""value"": ""A gamma camera is a specialized medical device used in nuclear medicine. It is used to create images of internal processes in the body, by detecting small amounts of radioactivity that is naturally present in the body.""}, {""type"": ""text"", ""value"": ""Gamma cameras are used in diagnosing medical conditions, detecting tumours, evaluating heart function and bone structure.""}]}, {""id"": ""nuclear-medicine-imaging"", ""text"": [{""type"": ""text"", ""value"": ""Nuclear Medicine""}, {""type"": ""text"", ""value"": ""Nuclear medicine is a medical imaging approach that produces images of internal body functions.""}, {""type"": ""text"", ""value"": ""It has a wide range of applications in diagnosing medical conditions, detecting tumours, evaluating heart function and bone structure.""}]}, {""id"": ""pet-imaging"", ""text"": [{""type"": ""text"", ""value"": ""Positron Emission Tomography (PET)""}, {""type"": ""text"", ""value"": ""Positron Emission Tomography (PET) is a medical imaging technique captures images of the body, by using controlled amounts of radioactive materials.""}, {""type"": ""text"", ""value"": ""It is commonly used in diagnosing medical conditions, especially for detecting tumours, and evaluating the function of our organs.""}]}, {""id"": ""ct-imaging"", ""text"": [{""type"": ""text"", ""value"": ""Computed Tomography (CT)""}, {""type"": ""text"", ""value"": ""Computed Tomography (CT) is a medical imaging method that produces detailed X-ray images from multiple angles.""}, {""type"": ""text"", ""value"": ""It visualizes internal structures, such as bones, organs, and blood vessels. CT scans are widely used for diagnosing various health conditions, from fractures to internal diseases.""}]}, {""id"": ""implantable-cardioverter-defibrillators"", ""text"": [{""type"": ""text"", ""value"": ""Implantable cardioverter-defibrillators (ICDs)""}, {""type"": ""text"", ""value"": ""Implantable cardioverter-defibrillators are medical devices that are surgically implanted in the chest and are designed to monitor heart rhythms and deliver electric shocks to restore normal heartbeats in the event of life-threatening arrhythmias or sudden cardiac arrest.""}]}, {""id"": ""pacemaker"", ""text"": [{""type"": ""text"", ""value"": ""Pacemaker""}, {""type"": ""text"", ""value"": ""A pacemaker is a medical device implanted into the chest or abdomen to regulate and control heart rhythms by sending electrical impulses.""}]}, {""id"": ""artery-vein"", ""text"": [{""type"": ""text"", ""value"": ""Arteries and veins""}, {""type"": ""text"", ""value"": ""Arteries are a type of blood vessel that generally carries oxygen-filled blood away from our heart and towards organs. A simple way to remember this is that A is for “artery” and “away from the heart”.""}, {""type"": ""text"", ""value"": ""Veins generally do the opposite: they carry oxygen-depleted blood (which carries carbon dioxide instead) from our organs to our heart.""}, {""type"": ""text"", ""value"": ""An exception is in the lung’s blood circulation, where pulmonary arteries carry oxygen-depleted blood to the lungs, and pulmonary veins carry oxygen-rich blood back to the heart.""}]}, {""id"": ""peripheral-artery-disease"", ""text"": [{""type"": ""text"", ""value"": ""Peripheral artery disease""}, {""type"": ""text"", ""value"": ""Peripheral artery disease is a condition that affects blood vessels supplying the arms and legs.""}, {""type"": ""text"", ""value"": ""It commonly arises due to atherosclerosis – the build-up of fat, cholesterol and other substances in the walls of blood vessels. If left untreated, peripheral artery disease can cause damage to parts of the arms and legs, and in severe cases, can lead to amputation.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""atherosclerosis"", ""text"": [{""type"": ""text"", ""value"": ""Atherosclerosis""}, {""type"": ""text"", ""value"": ""Atherosclerosis is when there is a build-up of fats, cholesterol & other substances in the walls of blood vessels, usually in arteries. This build-up narrows the space for blood to travel through, and limits blood flow to organs.""}, {""type"": ""text"", ""value"": ""Sometimes, the build up can be unstable and it can break off from the blood vessel, which can be dangerous. It can travel through the bloodstream and cause a blood clot to form, which can get stuck in smaller blood vessels, and cut off blood flow to parts of the body.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""aneurysm"", ""text"": [{""type"": ""text"", ""value"": ""Aneurysm""}, {""type"": ""text"", ""value"": ""An aneurysm is when the wall of a blood vessel weakens, which makes it bulge out, and stretch its walls thinner.""}, {""type"": ""text"", ""value"": ""Aneurysms can develop due to high blood pressure, atherosclerosis, injuries, and other risk factors.""}, {""type"": ""text"", ""value"": ""In serious cases, an aneurysm can lead to the tearing or breaking of a blood vessel, which leads to bleeding in the surrounding area and reduces blood flow to other organs, which can cause organ damage.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""ischemic-heart-disease"", ""text"": [{""type"": ""text"", ""value"": ""Ischaemic heart disease""}, {""type"": ""text"", ""value"": ""Ischaemic heart disease, which is also known as coronary artery disease, develops when there is a blockage of blood flow to the heart, often due to atherosclerosis.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""hypertensive-heart-disease"", ""text"": [{""type"": ""text"", ""value"": ""Hypertensive heart diseases""}, {""type"": ""text"", ""value"": ""Hypertensive heart diseases are diseases caused by long-term high blood pressure (‘hypertension’), which puts pressure on the heart.""}, {""type"": ""text"", ""value"": ""When this happens, the heart’s structure changes – for example, the heart’s muscles thicken – and its ability to pump blood worsens.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""endocarditis"", ""text"": [{""type"": ""text"", ""value"": ""Endocarditis""}, {""type"": ""text"", ""value"": ""Endocarditis is an inflammation of the heart's inner lining. It typically arises from infections – usually by Streptococcus and Staphylococcus bacteria – that can enter the bloodstream and adhere to the heart.""}, {""type"": ""text"", ""value"": ""These infections can arise naturally, but can also be associated with hospital or medical environments. Staphylococcus bacteria are often found on the skin and in wounds, and they can be transmitted with implants and long-term intravenous insertions, especially without adequate sterilization practices.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""myocarditis"", ""text"": [{""type"": ""text"", ""value"": ""Myocarditis""}, {""type"": ""text"", ""value"": ""Myocarditis is a medical condition caused by inflammation of the middle muscles of the heart. It can result from infections, toxins, drugs, autoimmune diseases, and other immune conditions.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""cardiomyopathy"", ""text"": [{""type"": ""text"", ""value"": ""Cardiomyopathy""}, {""type"": ""text"", ""value"": ""Cardiomyopathies are a range of abnormalities of the heart muscle – for example, conditions that cause them to stretch, thicken or become rigid.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""rheumatic-heart-disease"", ""text"": [{""type"": ""text"", ""value"": ""Rheumatic heart diseases""}, {""type"": ""text"", ""value"": ""Rheumatic heart diseases can develop as a consequence of rheumatic fever. This is caused by untreated Strep throat and scarlet fever infections, from group A Streptococcus bacteria.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""congenital-heart-disease"", ""text"": [{""type"": ""text"", ""value"": ""Congenital heart diseases""}, {""type"": ""text"", ""value"": ""Congenital heart diseases develop from heart abnormalities that are present at birth, when the heart structure doesn’t form properly during fetal development. They can range from simple defects with no symptoms to complex problems with severe, life-threatening symptoms.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""pulmonary-heart-disease"", ""text"": [{""type"": ""text"", ""value"": ""Pulmonary heart diseases""}, {""type"": ""text"", ""value"": ""Pulmonary heart diseases are medical conditions that affect the heart’s ability to pump blood to the lungs. They can develop from diseases of the lung’s arteries, or from other heart or lung conditions.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""cerebrovascular-diseases"", ""text"": [{""type"": ""text"", ""value"": ""Cerebrovascular diseases""}, {""type"": ""text"", ""value"": ""Cerebrovascular diseases are the broad category of diseases that affect the brain's blood vessels. This includes aneurysms and strokes.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""ischemic-stroke"", ""text"": [{""type"": ""text"", ""value"": ""Ischaemic stroke""}, {""type"": ""text"", ""value"": ""Ischemic stroke is when blood supply to the brain, spinal cord or retina is reduced or blocked by a clot. This includes clots that developed in the brain’s arteries, or clots that traveled to the brain from other parts of the body.""}, {""type"": ""text"", ""value"": ""A stroke can be a serious life-threatening medical condition, because it can affect the brain’s critical functions – including involuntary processes, like the control of our breathing and heart rate – and many other functions of the brain, including control of movement, speech and language, cognitive abilities, perception and hormone balance.""}, {""type"": ""text"", ""value"": ""In survivors, a stroke can lead to long-term complications, which can be mitigated with treatment and rehabilitation.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""hemorrhagic-stroke"", ""text"": [{""type"": ""text"", ""value"": ""Hemorrhagic stroke""}, {""type"": ""text"", ""value"": ""Hemorrhagic stroke is when blood supply to the brain is reduced by bleeding, when a blood vessel tears or breaks. The leaked blood puts pressure on brain cells and damages them.""}, {""type"": ""text"", ""value"": ""A stroke can be a serious life-threatening medical condition, because it can affect the brain’s critical functions – including involuntary processes, like the control of our breathing and heart rate – and many other functions of the brain, including control of movement, speech and language, cognitive abilities, perception and hormone balance.""}, {""type"": ""text"", ""value"": ""In survivors, a stroke can lead to long-term complications, which can be partly managed with treatment and rehabilitation.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""heart-attack"", ""text"": [{""type"": ""text"", ""value"": ""Heart attacks""}, {""type"": ""text"", ""value"": ""Heart attacks, which are formally called “myocardial infarctions” in medicine, are when heart muscle cells die from a lack of blood flow, inflammation, or injury to the heart, and can lead to heart failure and death.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""deep-vein-thrombosis"", ""text"": [{""type"": ""text"", ""value"": ""Deep vein thrombosis (DVT)""}, {""type"": ""text"", ""value"": ""Deep vein thrombosis is a medical condition that develops from blood clots in a deep vein, usually in the legs.""}, {""type"": ""text"", ""value"": ""This can cause pain and swelling, but it can also be a serious risk if the blood clot breaks off and travels to other parts of the body – especially if it travels to the lungs and blocks off its blood supply.""}, {""type"": ""text"", ""value"": ""📃 Read more in our article: What are the different types of cardiovascular diseases, and how many deaths do they cause?""}]}, {""id"": ""catheterization-labs"", ""text"": [{""type"": ""text"", ""value"": ""Catheterization labs""}, {""type"": ""text"", ""value"": ""Catheterization labs (also known as “cath labs”) are specialized medical labs that are equipped with advanced imaging technology, which can be used to diagnose and treat various cardiovascular conditions.""}]}, {""id"": ""leishmaniasis"", ""text"": [{""type"": ""text"", ""value"": ""Leishmaniasis""}, {""type"": ""text"", ""value"": ""Leishmaniasis is a neglected tropical disease (NTD) caused by Leishmania parasites, and spread by sandflies. Over 90 sandfly species are known to transmit Leishmania parasites.""}, {""type"": ""text"", ""value"": ""There are three main forms of the disease:""}, {""type"": ""text"", ""value"": ""Cutaneous leishmaniasis: this most common form causes skin ulcers at the bite site, usually on exposed parts of the body like the face, arms, and legs.""}, {""type"": ""text"", ""value"": ""Mucocutaneous leishmaniasis: this form causes ulcers of the nose, mouth, and throat areas.""}, {""type"": ""text"", ""value"": ""Visceral leishmaniasis (also known as Kala-azar): this is the most severe form, and affects the liver, spleen, and bone marrow, causing symptoms such as fever, weight loss, anemia, and swelling of these organs.""}]}, {""id"": ""leprosy"", ""text"": [{""type"": ""text"", ""value"": ""Leprosy""}, {""type"": ""text"", ""value"": ""Leprosy (also known as Hansen’s disease) is a long-term infectious disease that affects the skin, nerves, respiratory system, eyes, and can lead to skin sores, numbness and deformities.""}, {""type"": ""text"", ""value"": ""It is a neglected tropical disease (NTD) and is caused by the bacteria Mycobacterium leprae.""}, {""type"": ""text"", ""value"": ""It develops gradually, and is known for its ability to cause significant nerve damage and disability if left untreated.""}]}, {""id"": ""lymphatic-filariasis"", ""text"": [{""type"": ""text"", ""value"": ""Lymphatic filariasis""}, {""type"": ""text"", ""value"": ""Lymphatic filariasis (“elephantiasis”) is an infectious disease that causes extreme swelling of the arms, legs, or other body parts.""}, {""type"": ""text"", ""value"": ""It is spread by mosquito bites that transfer tiny parasitic worms into the body. These worms live in the lymph system, which is a network of vessels and nodes that help fight infections.""}, {""type"": ""text"", ""value"": ""Over time, the worms disrupt the normal flow of the lymph fluid, causing it to build up and make the affected limbs or body areas incredibly swollen and deformed.""}, {""type"": ""text"", ""value"": ""If untreated, lymphatic filariasis can lead to permanent disability and disfigurement.""}, {""type"": ""text"", ""value"": ""Lymphatic filariasis is classified as a neglected tropical disease.""}]}, {""id"": ""onchocerciasis"", ""text"": [{""type"": ""text"", ""value"": ""River blindness (“onchocerciasis”)""}, {""type"": ""text"", ""value"": ""River blindness, which has the scientific name onchocerciasis, is a major infectious cause of blindness.""}, {""type"": ""text"", ""value"": ""It is classified as a neglected tropical disease.""}, {""type"": ""text"", ""value"": ""It is caused by a parasitic worm (Onchocerca volvulus) that spreads to humans through repeated bites of infected black flies, which breed in rivers.""}, {""type"": ""text"", ""value"": ""People infected can experience intense itching, rashes, skin discoloration, visual impairment and eye disease, which can eventually lead to permanent blindness.""}]}, {""id"": ""african-trypanosomiasis"", ""text"": [{""type"": ""text"", ""value"": ""Sleeping sickness""}, {""type"": ""text"", ""value"": ""African trypanosomiasis, or “sleeping sickness”, is a severe infectious disease that causes disruptions in sleep, confusion, sensory problems and can lead to death if untreated.""}, {""type"": ""text"", ""value"": ""It is caused by tiny parasites called trypanosomes that are transmitted by tsetse flies in sub-Saharan Africa.""}, {""type"": ""text"", ""value"": ""In the early stage, the disease causes fever, headaches, joint pains and itching. As the parasites cross into the brain, it leads to disruption of sleep cycles, confusion and other neurological issues.""}, {""type"": ""text"", ""value"": ""Without treatment, the diseases consequences grow gradually and can ultimately result in coma and death.""}, {""type"": ""text"", ""value"": ""The disease can be acute (short-term) or chronic (long-term), and both are fatal. These are caused by different parasites.""}, {""type"": ""text"", ""value"": ""T. b. rhodesiense, which is found in eastern and southern Africa, causes the short-term (acute) form of the disease.""}, {""type"": ""text"", ""value"": ""T. b. gambiense, which is found mostly in west and central Africa, causes the long-term (chronic) form of the disease.""}, {""type"": ""text"", ""value"": ""Sleeping sickness is classified as a neglected tropical disease.""}]}, {""id"": ""trachoma"", ""text"": [{""type"": ""text"", ""value"": ""Trachoma""}, {""type"": ""text"", ""value"": ""Trachoma is an eye infection that can potentially lead to blindness if not treated. It is classified as a neglected tropical disease.""}, {""type"": ""text"", ""value"": ""It is spread through contact with discharge from an infected person's eyes or via flies.""}, {""type"": ""text"", ""value"": ""In the early stages, called “follicular trachoma”, the disease causes the eyelids to become inflamed and scarred.""}, {""type"": ""text"", ""value"": ""As the scarring builds up over time from repeated infections, the eyelids turn inwards. This makes the eyelashes rub against and scratch the eyeball, damaging the clear front part of the eye called the cornea. This advanced stage is called “trachomatous trichiasis”.""}, {""type"": ""text"", ""value"": ""The scratching and scarring to the cornea can eventually lead to irreversible blindness.""}, {""type"": ""text"", ""value"": ""Learn more in our article: Trachoma: how a common cause of blindness can be prevented worldwide""}]}, {""id"": ""trachoma-elimination"", ""text"": [{""type"": ""text"", ""value"": ""Elimination of trachoma as a public health problem""}, {""type"": ""text"", ""value"": ""The elimination of trachoma as a public health problem is defined as:""}, {""type"": ""text"", ""value"": ""• <5% of children aged 1–9 years in each formerly endemic district have early-stage trachoma""}, {""type"": ""text"", ""value"": ""• <0.2% of people aged over 15 years in each formerly-endemic district have advanced trachoma that is “unknown to the health system”""}, {""type"": ""text"", ""value"": ""• There is written evidence that the health system is able to identify and manage incident cases of advanced trachoma, with evidence of sufficient finances to implement those strategies""}, {""type"": ""Note"", ""value"": ""Countries listed as having “suspected elimination” have not submitted evidence for certification. Countries listed as having a “suspected public health problem” have not had sufficient testing to determine the prevalence of the disease.""}]}, {""id"": ""snakebite-envenoming"", ""text"": [{""type"": ""text"", ""value"": ""Snakebite envenoming""}, {""type"": ""text"", ""value"": ""Snakebite envenoming is when venom from a snake enters the body. It can be injected by snakes through a bite, or sprayed into the eyes by some snake species that can spit venom.""}, {""type"": ""text"", ""value"": ""Different snake venoms cause different effects, but common symptoms include severe pain, swelling, bruising, nausea, difficulty breathing, and bleeding conditions.""}, {""type"": ""text"", ""value"": ""Without rapid treatment, the venom can damage tissues, cause paralysis of the nervous system and respiratory failure, and potentially lead to fatal complications.""}, {""type"": ""text"", ""value"": ""Snakebite envenoming is classified as a neglected tropical disease.""}, {""type"": ""text"", ""value"": ""Snakebite envenoming disproportionately affects rural agricultural workers, fishermen, hunters, and herders, who live or work in areas where snakes tend to live.""}, {""type"": ""text"", ""value"": ""Deaths from snakebite envenoming tend to be more common in places where hospitals are sparse or lack antivenom, which is an immediate treatment for venomous bites.""}]}, {""id"": ""tuberculosis"", ""text"": [{""type"": ""text"", ""value"": ""Tuberculosis""}, {""type"": ""text"", ""value"": ""Tuberculosis (TB) is an infectious disease that most often affects the lungs and is caused by a type of bacteria (Mycobacterium tuberculosis). It spreads through the air when infected people cough, sneeze or spit.""}, {""type"": ""text"", ""value"": ""Despite being a preventable and curable disease it is one of the leading causes of deaths from infectious diseases. It is the leading cause of death of people with HIV and also a major contributor to antimicrobial resistance.""}, {""type"": ""text"", ""value"": ""📖 Read more on our page on Tuberculosis.""}]}, {""id"": ""drug-resistant-tuberculosis"", ""text"": [{""type"": ""text"", ""value"": ""Drug resistant tuberculosis""}, {""type"": ""text"", ""value"": ""Drug resistant tuberculosis is a form of tuberculosis that does not get better with the standard antibiotic treatment for the disease.""}, {""type"": ""text"", ""value"": ""The standard treatment for tuberculosis (also known as the first-line treatment) is typically a combination of the antibiotics: rifampicin, isoniazid, pyrazinamide and ethambutol.""}, {""type"": ""text"", ""value"": ""If tuberculosis has developed resistance against these antibiotics, it is more challenging to treat – it requires longer treatment with other drugs, which can be less effective, more toxic, or more expensive.""}, {""type"": ""text"", ""value"": ""There are two classifications of drug resistant tuberculosis: multidrug resistant tuberculosis and extensively drug resistant tuberculosis.""}, {""type"": ""text"", ""value"": ""In multidrug resistant tuberculosis, patients do not respond to treatment from the two main first-line drugs (rifampicin and isoniazid) used to treat tuberculosis.""}, {""type"": ""text"", ""value"": ""In extensively drug resistant tuberculosis, patients meet the criteria for multidrug resistant tuberculosis, and also do not respond to treatment from any fluoroquinolone (the second-line treatment for tuberculosis), and at least one other second-line drug.""}]}, {""id"": ""fast-track-unaids-hiv"", ""text"": [{""type"": ""text"", ""value"": ""Fast-Track approach to HIV/AIDS""}, {""type"": ""text"", ""value"": ""The Fast-Track approach is a roadmap developed by UNAIDS to end the AIDS epidemic as a public health concern by 2030.""}, {""type"": ""text"", ""value"": ""It is based on scaling up and maintaining implementation of HIV prevention, treatment, care and support for patients. It aims to reduce new HIV infections and AIDS-related deaths by 90% from 2010 to 2030, by using biomedical, behavioral and enabling interventions and rapidly scaling up antiretroviral treatment known as 90-90-90.""}]}, {""id"": ""pandemic"", ""text"": [{""type"": ""text"", ""value"": ""Pandemic""}, {""type"": ""text"", ""value"": ""Pandemics generally refer to diseases with a vast geographic range – such as spreading across a continent or multiple continents. In addition, they tend to describe outbreaks that are rapidly growing or expanding in range; highly infectious; affecting a large number of people; and caused by novel pathogens against which there is little or no pre-existing immunity.""}]}, {""id"": ""latent-tuberculosis"", ""text"": [{""type"": ""text"", ""value"": ""Latent tuberculosis""}, {""type"": ""text"", ""value"": ""Latent tuberculosis is a condition where an individual is infected with tuberculosis, but the bacteria are inactive and they have no symptoms.""}, {""type"": ""text"", ""value"": ""These individuals are not contagious and may not know they have the infection until it becomes active, potentially months or years later.""}]}, {""id"": ""relapsed"", ""text"": [{""type"": ""text"", ""value"": ""Relapsed""}, {""type"": ""text"", ""value"": ""Relapse is when a disease, or its symptoms, return after a period of recovery. This can require re-treatment.""}]}, {""id"": ""bcg-vaccine"", ""text"": [{""type"": ""text"", ""value"": ""BCG vaccine""}, {""type"": ""text"", ""value"": ""The BCG vaccine, short for Bacillus Calmette-Guérin vaccine, is a vaccine used against childhood tuberculosis.""}]}, {""id"": ""first-line-treatment"", ""text"": [{""type"": ""text"", ""value"": ""First line treatment""}, {""type"": ""text"", ""value"": ""First-line treatment refers to the first and standard treatment recommended for a particular disease.""}, {""type"": ""text"", ""value"": ""Second-line treatment refers to the next standard treatment recommended, if the first-line treatments have not worked or cause unacceptable side effects.""}]}, {""id"": ""rifampicin"", ""text"": [{""type"": ""text"", ""value"": ""Rifampicin""}, {""type"": ""text"", ""value"": ""Rifampicin is an antibiotic that is used to treat bacterial infections such as tuberculosis and leprosy. It’s often used as part of the first-line treatment for tuberculosis, along with other antibiotics.""}]}, {""id"": ""lymphatic-system"", ""text"": [{""type"": ""text"", ""value"": ""Lymphatic system""}, {""type"": ""text"", ""value"": ""The lymphatic system refers to a particular network of tissues and organs – including lymph nodes, vessels, and fluid – in the body.""}, {""type"": ""text"", ""value"": ""The lymphatic system helps get rid of toxins, waste, and other unwanted materials in the body. It plays a key role in the immune system by transporting lymph, a fluid that contains white blood cells which fight infections, throughout the body.""}]}, {""id"": ""global-fund"", ""text"": [{""type"": ""text"", ""value"": ""Global Fund""}, {""type"": ""text"", ""value"": ""The Global Fund is an international financial organization that provides funding and resources to countries to accelerate the end of AIDS, tuberculosis, and malaria as epidemics.""}]}, {""id"": ""usaid"", ""text"": [{""type"": ""text"", ""value"": ""USAID""}, {""type"": ""text"", ""value"": ""The United States Agency for International Development (USAID), is an independent agency of the United States government that administers foreign aid and development assistance to countries around the world.""}]}, {""id"": ""tuberculosis-skin-test"", ""text"": [{""type"": ""text"", ""value"": ""Tuberculosis skin test""}, {""type"": ""text"", ""value"": ""The tuberculosis skin test often involves the injection of a small amount of substance called PPD tuberculin. This triggers an immune response in people who have been previously infected with TB bacteria.""}, {""type"": ""text"", ""value"": ""If the immune system recognizes the tuberculin, it results in swelling around the injection site. This reaction is measured to determine if a person has been exposed to TB bacteria.""}, {""type"": ""text"", ""value"": ""The tuberculosis skin test can be affected by false positives, especially in people who have been vaccinated with the BCG vaccine or have an infection by a different mycobacterium. False negatives can also occur, especially in people with weakened immune systems or recent TB exposure.""}]}, {""id"": ""watt-hours"", ""text"": [{""type"": ""text"", ""value"": ""Watt-hour""}, {""type"": ""text"", ""value"": ""A watt-hour is the energy delivered by one watt of power for one hour. Since one watt is equivalent to one joule per second, a watt-hour is equivalent to 3600 joules of energy.""}, {""type"": ""text"", ""value"": ""Metric prefixes are used for multiples of the unit, usually:""}, {""type"": ""text"", ""value"": ""- kilowatt-hours (kWh), or a thousand watt-hours.""}, {""type"": ""text"", ""value"": ""- Megawatt-hours (MWh), or a million watt-hours.""}, {""type"": ""text"", ""value"": ""- Gigawatt-hours (GWh), or a billion watt-hours.""}, {""type"": ""text"", ""value"": ""- Terawatt-hours (TWh), or a trillion watt-hours.""}]}, {""id"": ""exoplanet_transit"", ""text"": [{""type"": ""text"", ""value"": ""Transit method""}, {""type"": ""text"", ""value"": ""The transit method detects exoplanets by observing the dimming of a star as an orbiting planet passes between the star and the observer. This transit results in a periodic and slight dip in the star's brightness, which can be measured to infer the planet's presence, size, and orbit.""}]}, {""id"": ""exoplanet_radial_velocity"", ""text"": [{""type"": ""text"", ""value"": ""Radial velocity method""}, {""type"": ""text"", ""value"": ""Also known as Doppler spectroscopy, the radial velocity method measures changes in the star's spectrum due to the gravitational pull of an orbiting planet. As the planet orbits, it causes the star to move in a small orbit in response, which leads to slight shifts in the star's spectral lines due to the Doppler effect. These shifts can reveal the presence of a planet and provide information about its mass and orbit.""}]}, {""id"": ""exoplanet_microlensing"", ""text"": [{""type"": ""text"", ""value"": ""Microlensing method""}, {""type"": ""text"", ""value"": ""Gravitational microlensing occurs when the gravitational field of a star (and potentially its planet) acts as a lens, magnifying the light of a background star that happens to pass behind it from the observer's perspective. The presence of a planet can be detected through the specific characteristics of the light curve produced by this event.""}]}, {""id"": ""equivalent_megaton"", ""text"": [{""type"": ""text"", ""value"": ""Equivalent megaton""}, {""type"": ""text"", ""value"": ""A megaton is the explosive energy released by one million tons of TNT. For comparison, the bombs dropped on Hiroshima and Nagasaki were 0.015 and 0.021 megatons, respectively.""}, {""type"": ""text"", ""value"": ""An equivalent megaton is a way of making the explosive energy of different warheads comparable. It weighs small warheads — in this case those with at most one megaton — more than large ones, because small warheads are relatively more destructive.""}]}, {""id"": ""half-life"", ""text"": [{""type"": ""text"", ""value"": ""Half-life""}, {""type"": ""text"", ""value"": ""The “half-life” of a substance refers to the time it takes for that substance to halve in quantity.""}, {""type"": ""text"", ""value"": ""For example, in physics and chemistry, it often refers to the time it takes for half of the atoms in a particular radioactive sample to decay.""}, {""type"": ""text"", ""value"": ""However, the concept is widely used in various fields, including geology, finance, and pharmacology, to describe how quickly quantities degrade or react.""}]}, {""id"": ""treaty-non-proliferation"", ""text"": [{""type"": ""text"", ""value"": ""Nuclear Non-Proliferation Treaty""}, {""type"": ""text"", ""value"": ""The treaty’s objective is to prevent the spread of nuclear weapons, to promote cooperation in the peaceful uses of nuclear energy, and to pursue nuclear and general disarmament.""}]}, {""id"": ""treaty-nuclear-test-partial"", ""text"": [{""type"": ""text"", ""value"": ""Partial Nuclear-Test-Ban Treaty""}, {""type"": ""text"", ""value"": ""The treaty’s objective is to stop nuclear weapons tests in the atmosphere, in outer space, and under water.""}]}, {""id"": ""treaty-nuclear-test-comprehensive"", ""text"": [{""type"": ""text"", ""value"": ""Comprehensive Nuclear-Test-Ban Treaty""}, {""type"": ""text"", ""value"": ""The treaty’s objective is to stop nuclear weapons tests in the atmosphere, in outer space, under water, and underground.""}]}, {""id"": ""treaty-nuclear-prohibition"", ""text"": [{""type"": ""text"", ""value"": ""Nuclear Prohibition Treaty""}, {""type"": ""text"", ""value"": ""The treaty’s objective is to stop developing, testing, producing, acquiring, possessing, stockpiling, deploying, using, and threatening to use nuclear weapons, as well as assisting other countries in these actions.""}]}, {""id"": ""treaty-status-signed"", ""text"": [{""type"": ""text"", ""value"": ""Country status \""Signed\"" on a treaty""}, {""type"": ""text"", ""value"": ""This status means that the country accepts a treaty without a legal commitment.""}]}, {""id"": ""treaty-status-committed"", ""text"": [{""type"": ""text"", ""value"": ""Country status \""Approved\"" on a treaty""}, {""type"": ""text"", ""value"": ""This status means that the country has legally committed to a treaty.""}, {""type"": ""text"", ""value"": ""This status comprises one of the following actions defined by UNODA:""}, {""type"": ""text"", ""value"": ""- \""Ratification\"" is an act of formal confirmation, where a country consents to be legally bound to a treaty.""}, {""type"": ""text"", ""value"": ""- \""Accession\"" means that a country accepts to become a party to a treaty that has been signed by other states, not by the country itself. It has the same legal effect as ratification.""}, {""type"": ""text"", ""value"": ""- \""Succession\"" means that a newly formed country adopts the treaty obligations of a predecessor state.""}]}, {""id"": ""treaty-status-not-signed"", ""text"": [{""type"": ""text"", ""value"": ""Country status \""Not signed\"" on a treaty""}, {""type"": ""text"", ""value"": ""This status means that the country has neither signed nor committed to a treaty.""}]}, {""id"": ""treaty-status-withdrawn"", ""text"": [{""type"": ""text"", ""value"": ""Country status \""Withdrawn\"" on a treaty""}, {""type"": ""text"", ""value"": ""This status means that the country has withdrawn from its legal commitment to a treaty.""}]}, {""id"": ""improved-water-source"", ""text"": [{""type"": ""text"", ""value"": ""Improved drinking water sources""}, {""type"": ""text"", ""value"": ""Improved drinking water sources are those that have the potential to deliver safe water by nature of their design and construction, and include: piped water, boreholes or tubewells, protected dug wells, protected springs, rainwater, and packaged or delivered water.""}]}, {""id"": ""water-safely-managed"", ""text"": [{""type"": ""text"", ""value"": ""Safely managed drinking water services""}, {""type"": ""text"", ""value"": ""Drinking water from an improved water source that is accessible on premises, available when needed and free from faecal and priority chemical contamination.""}]}, {""id"": ""water-basic"", ""text"": [{""type"": ""text"", ""value"": ""Basic drinking water services""}, {""type"": ""text"", ""value"": ""Drinking water from an improved source, provided collection time is not more than 30 minutes for a roundtrip including queuing.""}]}, {""id"": ""water-limited"", ""text"": [{""type"": ""text"", ""value"": ""Limited drinking water services""}, {""type"": ""text"", ""value"": ""Drinking water from an improved source for which collection time exceeds 30 minutes for a roundtrip including queuing.""}]}, {""id"": ""water-unimproved"", ""text"": [{""type"": ""text"", ""value"": ""Unimproved drinking water services""}, {""type"": ""text"", ""value"": ""Drinking water from an unprotected dug well or unprotected spring.""}]}, {""id"": ""water-surface-water"", ""text"": [{""type"": ""text"", ""value"": ""Unimproved drinking water services""}, {""type"": ""text"", ""value"": ""Drinking water directly from a river, dam, lake, pond, stream, canal or irrigation canal.""}]}, {""id"": ""sanitation-safely-managed"", ""text"": [{""type"": ""text"", ""value"": ""Safely managed sanitation services""}, {""type"": ""text"", ""value"": ""Use of improved facilities that are not shared with other households and where excreta are safely disposed of in situ or removed and treated offsite.""}]}, {""id"": ""sanitation-basic"", ""text"": [{""type"": ""text"", ""value"": ""Basic sanitation services""}, {""type"": ""text"", ""value"": ""Use of improved facilities which are not shared with other households""}]}, {""id"": ""sanitation-limited"", ""text"": [{""type"": ""text"", ""value"": ""Limited sanitation services""}, {""type"": ""text"", ""value"": ""Use of improved facilities shared between two or more households.""}]}, {""id"": ""sanitation-unimproved"", ""text"": [{""type"": ""text"", ""value"": ""Unimproved sanitation services""}, {""type"": ""text"", ""value"": ""Use of pit latrines without a slab or platform, hanging latrines or bucket latrines.""}]}, {""id"": ""sanitation-open-defecation"", ""text"": [{""type"": ""text"", ""value"": ""Unimproved sanitation services""}, {""type"": ""text"", ""value"": ""Disposal of human faeces in fields, forests, bushes, open bodies of water, beaches and other open spaces or with solid waste.""}]}, {""id"": ""cities-degurba"", ""text"": [{""type"": ""text"", ""value"": ""Cities""}, {""type"": ""text"", ""value"": ""Cities are settlements that have a population of at least 50,000 inhabitants in contiguous dense grid cells with more than 1,500 inhabitants per km².""}]}, {""id"": ""towns-suburbs-degurba"", ""text"": [{""type"": ""text"", ""value"": ""Towns and suburbs""}, {""type"": ""text"", ""value"": ""Towns and suburbs are settlements that have a population of at least 5,000 inhabitants in contiguous grid cells with a density of at least 300 inhabitants per km².""}]}, {""id"": ""rural-areas-degurba"", ""text"": [{""type"": ""text"", ""value"": ""Rural areas""}, {""type"": ""text"", ""value"": ""Rural areas consist mostly of settlements with low-density grid cells with fewer than 300 inhabitants per km².""}]}, {""id"": ""eu-excellent-bathing-water"", ""text"": [{""type"": ""text"", ""value"": ""Excellent bathing water quality""}, {""type"": ""text"", ""value"": ""In order for bathing sites to be classified as ‘excellent’ they must have low levels of both Escherichia coli (E. coli) and intestinal enterococci bacteria, as these are indicative of sewage pollution.""}, {""type"": ""text"", ""value"": ""The acceptable levels vary depending on the location of the bathing sites.""}, {""type"": ""text"", ""value"": ""At coastal bathing sites, E. coli must not exceed 250 cfu (colony-forming units) per 100 ml of water, and intestinal enterococci must not exceed 100 cfu per 100 ml in more than 95% of the samples taken over a period of four bathing seasons. At inland bathing sites, these values are 500 cfu per 100ml and 200 cfu per 100ml, respectively.""}]}, {""id"": ""ivs-important-in-life"", ""text"": [{""type"": ""text"", ""value"": ""The question asked was:""}, {""type"": ""text"", ""value"": ""For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all?""}]}, {""id"": ""ivs-most-serious-problem"", ""text"": [{""type"": ""text"", ""value"": ""The question asked was:""}, {""type"": ""text"", ""value"": ""I’m going to read out some problems. Please indicate which of the following problems you consider the most serious one for the world as a whole?\""""}, {""type"": ""text"", ""value"": ""The problems listed were: \""People living in poverty and need\"", \""Discrimination against girls and women\"", \""Poor sanitation and infectious diseases\"", \""Inadequate education\"", and \""Environmental pollution\"".""}]}, {""id"": ""ivs-justifiable"", ""text"": [{""type"": ""text"", ""value"": ""The question asked was:""}, {""type"": ""text"", ""value"": ""\""Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card\"".""}]}, {""id"": ""ivs-schwartz-questions"", ""text"": [{""type"": ""text"", ""value"": ""The question is structured following Shalom H. Schwartz’s questionnaire. Respondents are asked for the degree of identification they have with a hypothetical person described to them:""}, {""type"": ""text"", ""value"": ""\""Now I will briefly describe some people. Using this card, would you please indicate for each description whether that person is very much like you, like you, somewhat like you, a little like you, not like you, or not at all like you?\""""}]}, {""id"": ""ivs-neighbors"", ""text"": [{""type"": ""text"", ""value"": ""The question asked was:""}, {""type"": ""text"", ""value"": ""\""On this list are various groups of people. Could you please mention any that you would not like to have as neighbors?\"".""}]}, {""id"": ""enlightenment"", ""text"": [{""type"": ""text"", ""value"": ""Age of Enlightenment""}, {""type"": ""text"", ""value"": ""The Age of Enlightenment, rooted in 17th and 18th century Western Europe, celebrated reason and individualism, profoundly reshaping education by promoting literacy and public access to knowledge. This shift laid the groundwork for enlightened, democratic societies in the modern world.""}]}, {""id"": ""wildfires"", ""text"": [{""type"": ""text"", ""value"": ""Wildfires""}, {""type"": ""text"", ""value"": ""A wildfire, characterized by its uncontrolled and rapid spread, can occur in various types of vegetation and wildlands, including forests, savannahs, grasslands, and various other vegetation types. These incidents are identified using satellite imagery, which detects thermal anomalies as indicators of active burning areas.""}]}, {""id"": ""regimes-of-the-world"", ""text"": [{""type"": ""text"", ""value"": ""Regimes of the World’s regime classification""}, {""type"": ""text"", ""value"": ""Closed autocracy: citizens do not have the right to choose either the chief executive of the government or the legislature through multi-party elections.""}, {""type"": ""text"", ""value"": ""Electoral autocracy: citizens have the right to choose the chief executive and the legislature through multi-party elections; but they lack some freedoms, such as the freedoms of association or expression that make the elections meaningful, free, and fair.""}, {""type"": ""text"", ""value"": ""Electoral democracy: citizens have the right to choose the chief executive and the legislature in meaningful, free and fair, and multi-party elections.""}, {""type"": ""text"", ""value"": ""Liberal democracy: electoral democracy and citizens enjoy individual and minority rights, are equal before the law, and the actions of the executive are constrained by the legislative and the courts.""}, {""type"": ""text"", ""value"": ""📄 Read more in our article: The ‘Regimes of the World’ data: how do researchers measure democracy?""}]}, {""id"": ""regimes-lexical-index"", ""text"": [{""type"": ""text"", ""value"": ""Lexical Index regime classification""}, {""type"": ""text"", ""value"": ""Non-electoral autocracy: citizens do not have the right to elect the chief executive or the legislature one-party autocracy: some citizens have the right to choose the chief executive or the legislature, but only have one choice.""}, {""type"": ""text"", ""value"": ""Multiparty autocracy without elected executive: some citizens have the right to choose the legislature and have more than one choice, but chief executive not elected.""}, {""type"": ""text"", ""value"": ""Multiparty autocracy: some citizens have the right to choose the chief executive and the legislature and have more than one choice, but election outcome is certain.""}, {""type"": ""text"", ""value"": ""Exclusive democracy: citizens have the right to choose the chief executive and the legislature in multi-party, uncertain elections, but suffrage is restricted.""}, {""type"": ""text"", ""value"": ""Male democracy: citizens have the right to choose the chief executive and the legislature in multi-party, uncertain elections, but suffrage is restricted to men.""}, {""type"": ""text"", ""value"": ""Electoral democracy: citizens have the right to choose the chief executive and the legislature in multi-party, uncertain elections.""}, {""type"": ""Polyarchy"", ""value"": ""citizens have the right to choose the chief executive and the legislature in multi-party, uncertain elections, and enjoy freedoms of expression, assembly, and association.""}, {""type"": ""text"", ""value"": ""📄 Read more in our article: Democracy data: how sources differ and when to use which one""}]}, {""id"": ""democracy-boix-miller-rosato"", ""text"": [{""type"": ""text"", ""value"": ""Boix et al.’s democracy classification""}, {""type"": ""Democracy"", ""value"": ""a majority of adult men have the right to choose the chief executive and the legislature in free and fair elections.""}, {""type"": ""text"", ""value"": ""📄 Read more in our article: Democracy data: how sources differ and when to use which one""}]}, {""id"": ""democracy-boix-miller-rosato-women"", ""text"": [{""type"": ""text"", ""value"": ""Boix et al.’s democracy classification (including women’s right to vote)""}, {""type"": ""Democracy"", ""value"": ""a majority of adult men and women have the right to choose the chief executive and the legislature in free and fair elections.""}, {""type"": ""text"", ""value"": ""📄 Read more in our article: Democracy data: how sources differ and when to use which one""}]}, {""id"": ""regimes-polity"", ""text"": [{""type"": ""text"", ""value"": ""Polity’s regime classification""}, {""type"": ""Democracy"", ""value"": ""mostly democratic characteristics — open, multi-party, and competitive elections choose chief executive, who faces comprehensive institutional constraints, and political participation is competitive.""}, {""type"": ""Anocracy"", ""value"": ""neither clearly democratic nor autocratic characteristics.""}, {""type"": ""Autocracy"", ""value"": ""mostly autocratic characteristics — hereditary succession chooses chief executive who faces no institutional constraints, and political participation is restricted and suppressed.""}, {""type"": ""text"", ""value"": ""📄 Read more in our article: Democracy data: how sources differ and when to use which one""}]}, {""id"": ""regimes-fh"", ""text"": [{""type"": ""text"", ""value"": ""Freedom House’s regime classification""}, {""type"": ""text"", ""value"": ""Free country: citizens have many political rights (free and fair elections, political pluralism and participation, functioning government) and civil liberties (freedoms of expression and association, rule of law, personal autonomy).""}, {""type"": ""text"", ""value"": ""Partly free country: citizens have some political rights and civil liberties.""}, {""type"": ""text"", ""value"": ""Not free country: citizens have few political rights and civil liberties.""}, {""type"": ""text"", ""value"": ""📄 Read more in our article: Democracy data: how sources differ and when to use which one""}]}, {""id"": ""democracy-fh"", ""text"": [{""type"": ""text"", ""value"": ""Freedom House’s democracy classification""}, {""type"": ""text"", ""value"": ""Electoral democracy: citizens have the right to choose chief executive and legislature in broadly free and fair elections and have substantial other political rights and civil liberties.""}, {""type"": ""text"", ""value"": ""📄 Read more in our article: Democracy data: how sources differ and when to use which one""}]}, {""id"": ""regimes-bertelsmann"", ""text"": [{""type"": ""text"", ""value"": ""Bertelsmann Transformation Index’s regime classification""}, {""type"": ""text"", ""value"": ""Consolidating democracy: comprehensive democratic features and minimum democratic characteristics (citizens can choose political leaders in free and fair elections and enjoy freedoms of association, expression and some further civil liberties, political power is separated, and leaders can effectively govern a state that fulfils basic functions).""}, {""type"": ""text"", ""value"": ""Defective democracy: minimum democratic characteristics, but limited other democratic features.""}, {""type"": ""text"", ""value"": ""Very defective democracy: minimum democratic characteristics, but very limited other democratic features.""}, {""type"": ""text"", ""value"": ""Moderate autocracy: no minimum democratic characteristics, but possibly other broadly democratic features.""}, {""type"": ""text"", ""value"": ""Hard-line autocracy: no minimum democratic characteristics, and few other democratic features.""}, {""type"": ""text"", ""value"": ""📄 Read more in our article: Democracy data: how sources differ and when to use which one""}]}, {""id"": ""regimes-economist"", ""text"": [{""type"": ""text"", ""value"": ""Economist Intelligence Unit’s regime classification""}, {""type"": ""text"", ""value"": ""Full democracy: comprehensive extent of democracy (extent to which citizens can choose their political leaders in free and fair elections, enjoy civil liberties, prefer democracy over other political systems, can and do participate in politics, and have a functioning government that acts on their behalf), few weaknesses.""}, {""type"": ""text"", ""value"": ""Flawed democracy: some weaknesses in democratic institutions and culture.""}, {""type"": ""text"", ""value"": ""Hybrid regime: serious weaknesses in democratic institutions and culture.""}, {""type"": ""text"", ""value"": ""Authoritarian regime: few democratic institutions and little democratic culture.""}, {""type"": ""text"", ""value"": ""📄 Read more in our article: Democracy data: how sources differ and when to use which one""}]}, {""id"": ""regimes-episodes-regime-transformation"", ""text"": [{""type"": ""text"", ""value"": ""Episodes of Regime Transformations’ regime classification""}, {""type"": ""text"", ""value"": ""Autocratizing country: a country that is becoming less electorally democratic, and has seen a substantial decrease in its degree of electoral democracy.""}, {""type"": ""text"", ""value"": ""Democratizing country: a country that is becoming more electorally democratic, and has seen a substantial increase in its degree of electoral democracy.""}, {""type"": ""text"", ""value"": ""📄 Read more in our article: The world has recently become less democratic""}]}, {""id"": ""regimes-episodes-regime-transformation-expanded"", ""text"": [{""type"": ""text"", ""value"": ""Episodes of Regime Transformations’ expanded regime classification""}, {""type"": ""text"", ""value"": ""Hardening autocracy: an autocracy that is becoming less electorally democratic, and has seen a substantial decrease in its degree of electoral democracy.""}, {""type"": ""text"", ""value"": ""Stable autocracy: an autocracy that is neither autocratizing nor democratizing.""}, {""type"": ""text"", ""value"": ""Liberalizing autocracy: an autocracy that is becoming more electorally democratic, and has seen a substantial increase in its degree of electoral democracy.""}, {""type"": ""text"", ""value"": ""Eroding democracy: a democracy that is becoming less electorally democratic, and has seen a substantial decrease in its degree of electoral democracy.""}, {""type"": ""text"", ""value"": ""Stable democracy: a democracy that is neither autocratizing nor democratizing.""}, {""type"": ""text"", ""value"": ""Deepening democracy: a democracy that is becoming more electorally democratic, and has seen a substantial increase in its degree of electoral democracy.""}, {""type"": ""text"", ""value"": ""Autocracies and democracies are very similarly defined to Regimes of the World’s classification.""}, {""type"": ""text"", ""value"": ""📄 Read more in our article: The world has recently become less democratic""}]}, {""id"": ""manufactured-cigarettes"", ""text"": [{""type"": ""text"", ""value"": ""Manufactured cigarettes""}, {""type"": ""text"", ""value"": ""Factory-made packeted cigarettes, with or without a filter.""}]}, {""id"": ""who-air-quality-guidelines"", ""text"": [{""type"": ""text"", ""value"": ""Air Quality Guidelines for PM2.5""}, {""type"": ""text"", ""value"": """"}, {""type"": ""text"", ""value"": ""PM2.5 refers to particulate matter that is 2.5 micrometers in diameter or smaller. These fine particles pose significant health risks, leading the World Health Organization (WHO) to establish Air Quality Guidelines (AQG) and Interim Targets.""}, {""type"": ""text"", ""value"": ""These guidelines provide health-based recommendations for managing air quality, aimed at reducing exposure to air pollution and mitigating its adverse health impacts. Recognizing air pollution as a major environmental threat, the AQGs serve as a tool for governments and civil society to improve air quality and public health.""}, {""type"": ""text"", ""value"": ""PM2.5 Annual Average Guidelines and Interim Targets:""}, {""type"": ""text"", ""value"": ""- Interim Target-1 (IT-1): 35 µg/m³""}, {""type"": ""text"", ""value"": ""- Interim Target-2 (IT-2): 25 µg/m³""}, {""type"": ""text"", ""value"": ""- Interim Target-3 (IT-3): 15 µg/m³""}, {""type"": ""text"", ""value"": ""- Interim Target-4 (IT-4): 10 µg/m³""}, {""type"": ""text"", ""value"": ""- AQG Level: 5 µg/m³""}, {""type"": ""text"", ""value"": ""Each step towards achieving the AQG represents progress in minimizing the health risks associated with PM2.5 pollution.""}]}, {""id"": ""cantril-ladder"", ""text"": [{""type"": ""text"", ""value"": ""Cantril Ladder""}, {""type"": ""text"", ""value"": ""The Cantril Ladder is a metric used to determine individuals life satisfaction, based on the following question:""}, {""type"": ""text"", ""value"": ""“Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you, and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”""}]}, {""id"": ""gender-marker-change"", ""text"": [{""type"": ""text"", ""value"": ""Gender marker change""}, {""type"": ""text"", ""value"": ""Changing one’s gender in official national documents, such as one’s personal identification document.""}]}, {""id"": ""opioids"", ""text"": [{""type"": ""text"", ""value"": ""Opioids""}, {""type"": ""text"", ""value"": ""Opioid drugs are medications that relieve pain by blocking pain signals in the brain and affecting the nervous system. They can be natural, like morphine, or synthetic, like fentanyl. While they are very effective for severe pain, they can also be highly addictive and dangerous if misused, leading to serious health problems or even death.""}]}, {""id"": ""influenza"", ""text"": [{""type"": ""text"", ""value"": ""Influenza""}, {""type"": ""text"", ""value"": ""Influenza (commonly known as the flu) is a contagious illness caused by the influenza virus.""}, {""type"": ""text"", ""value"": ""In severe cases, it can cause respiratory disease, such as pneumonia, and cardiovascular complications, such as heart attacks and strokes.""}, {""type"": ""text"", ""value"": ""Influenza viruses evolve gradually in a process called “antigenic drift”, which causes annual seasonal outbreaks.""}, {""type"": ""text"", ""value"": ""It can evolve suddenly, if different influenza viruses combine with each other to make new strains, which is called “antigenic shift”. This can result in strains that are more infectious and/or lethal, leading to pandemic influenza.""}, {""type"": ""text"", ""value"": ""📖 Read more on our page on influenza.""}]}, {""id"": ""pertussis"", ""text"": [{""type"": ""text"", ""value"": ""Pertussis""}, {""type"": ""text"", ""value"": ""Pertussis (commonly known as “whooping cough”) is a highly contagious illness caused by the bacterium Bordetella pertussis.""}, {""type"": ""text"", ""value"": ""It can lead to serious respiratory complications such as pneumonia and difficulty breathing, especially in infants and young children.""}, {""type"": ""text"", ""value"": ""The bacteria produce toxins that damage the respiratory tract and cause severe coughing fits, which include a \""whooping\"" sound during inhalation.""}, {""type"": ""text"", ""value"": ""Infections of pertussis can be reduced by vaccination with the pertussis vaccine.""}]}, {""id"": ""biological-weapons"", ""text"": [{""type"": ""text"", ""value"": ""Biological weapons""}, {""type"": ""text"", ""value"": ""Biological weapons are organisms or toxins used to cause death or harm through their poisonous properties.""}]}, {""id"": ""chemical-weapons"", ""text"": [{""type"": ""text"", ""value"": ""Chemical weapons""}, {""type"": ""text"", ""value"": ""Chemical weapons are chemicals used to cause death or harm through their poisonous properties.""}]}, {""id"": ""tree-cover-loss-commodity-driven"", ""text"": [{""type"": ""text"", ""value"": ""Commidity driven deforestation""}, {""type"": ""text"", ""value"": ""Large-scale deforestation linked primarily to commercial agricultural expansion.""}]}, {""id"": ""tree-cover-loss-forestry"", ""text"": [{""type"": ""text"", ""value"": ""Forestry""}, {""type"": ""text"", ""value"": ""Temporary loss from plantation and natural forest harvesting, with some deforestation of primary forests.""}]}, {""id"": ""tree-cover-loss-shifting-agriculture"", ""text"": [{""type"": ""text"", ""value"": ""Shifting agriculture""}, {""type"": ""text"", ""value"": ""Temporary loss or permanent deforestation due to small- and medium-scale agriculture.""}]}, {""id"": ""tree-cover-loss-wildfires"", ""text"": [{""type"": ""text"", ""value"": ""Wildfires""}, {""type"": ""text"", ""value"": ""Temporary loss, does not include fire clearing for agriculture.""}]}, {""id"": ""tree-cover-loss-urbanization"", ""text"": [{""type"": ""text"", ""value"": ""Urbanization""}, {""type"": ""text"", ""value"": ""Deforestation for expansion of urban centers.""}]}], ""excerpt"": ""Source document for OWID’s details on demand snippets"", ""dateline"": ""April 12, 2023""}",1,2023-05-05 13:49:25,2023-05-08 22:27:00,2024-03-22 15:23:16,unlisted,ALBJ4Lv5Ielzw-KD7uUEqt1kH_SXKoG5DGQCxzmFO1v-sBIfDuPyu73HHjQlFLMwn_fQH1QDiwhYgm7fKTYO_w,,,Details on Demand 1ppx0jcy-PKs56l587pllO9_MtETIQTngRudb0VfoVbs,covid-testing-data-archived,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""We started building our COVID-19 testing dataset over two years ago, in March 2020. At the time, only data on confirmed cases and deaths was available. However, these figures are highly dependent on how much a country has actually tested."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Countries with low testing capacities will miss most cases and therefore fail to give an accurate count of deaths caused by the disease."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This dataset provided essential context for the data on confirmed cases and deaths, as well as insight into the pandemic and how it spread. You can read more about our COVID-19 testing dataset in our paper:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://rdcu.be/b8eoK"", ""type"": ""prominent-link"", ""title"": ""A cross-country database of COVID-19 testing"", ""description"": """", ""parseErrors"": []}, {""text"": [{""text"": ""Why are we stopping updates?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As of 23 June 2022, we will no longer add new data points to our COVID-19 testing dataset. There are several reasons for this."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Firstly, reporting methods have become very different between countries, and the type of tests (PCR, antigen, at-home self-tests) included in the data is increasingly unclear."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Secondly, testing and case confirmation criteria have diverged significantly between countries: the type of test necessary to confirm a case; if a referral from a GP is required for laboratory tests; if laboratory tests are free or subsidized; if at-home self-tests are available; whether vaccination status influences these parameters. These factors have made harmonizing data definitions across countries difficult to sustain."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Furthermore, with very high infection rates since the emergence of the Omicron variant, the main focus of public health authorities has shifted from case metrics to other endpoints, such as hospital and ICU admissions, deaths, and excess mortality (we will continue to collect and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/coronavirus-data-explorer"", ""children"": [{""text"": ""publish data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" on all of these important metrics). As a result, countries have begun to report testing data less frequently."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Alternative sources of testing data"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""All historical data collected to date will remain available in our "", ""spanType"": ""span-simple-text""}, {""url"": ""https://github.com/owid/covid-19-data/tree/master/public/data"", ""children"": [{""text"": ""dataset"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", on our "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/explorers/coronavirus-data-explorer"", ""children"": [{""text"": ""charts"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", as well as on our "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/coronavirus-testing"", ""children"": [{""text"": ""testing entry"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". Future testing data is still available directly from various regional statistical agencies:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.ecdc.europa.eu/en/publications-data/covid-19-testing"", ""children"": [{""text"": ""European CDC"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" continues to report the number of tests performed each week in its member states, including at the subnational level for some countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""url"": ""https://africacdc.maps.arcgis.com/apps/dashboards/a5603222b29c49539df3af45bac16bcc"", ""children"": [{""text"": ""Africa CDC"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" maintains a dashboard that reports data related to COVID-19, including the number of tests performed; note that for some countries the data is updated infrequently."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""url"": ""https://app.powerbi.com/view?r=eyJrIjoiN2ExNWI3ZGQtZDk3My00YzE2LWFjYmQtNGMwZjk0OWQ1MjFhIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9"", ""children"": [{""text"": ""WHO Eastern Mediterranean Region"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" also currently maintains a COVID-19 dashboard that reports the number of tests performed in its constituent countries; likewise, the testing data for some countries is updated infrequently."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports"", ""children"": [{""text"": ""WHO South East Asia Region"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" publishes weekly COVID-19 situational reports, which include the testing positivity rate for each country."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""These sources, however, face the same limitations as our dataset in terms of cross-country comparability."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""We thank the many readers, researchers, journalists, and government officials, who contributed to this effort since March 2020 by sending us valuable information and useful feedback on our dataset."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Acknowledgments"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""article"", ""title"": ""Ending our COVID-19 testing data updates"", ""authors"": [""Edouard Mathieu"", ""Cameron Appel"", ""Lucas Rodés-Guirao""], ""excerpt"": ""As of 23 June 2022, we will no longer add new data points to our COVID-19 testing dataset. We will continue updates of all other metrics in our COVID-19 dataset."", ""subtitle"": ""As of 23 June 2022, we will no longer add new data points to our COVID-19 testing dataset. We will continue updates of all other metrics in our COVID-19 dataset."", ""sidebar-toc"": false, ""featured-image"": ""Coronavirus-Testing.png""}",1,2024-03-06 13:33:16,2022-05-31 10:19:15,2024-03-06 13:39:08,listed,ALBJ4LvODDO0lcmqoa4PCeEJlLh-evdkWD8H8kA6oxhvrxfVfuakapA0qLJ3VVlJFeBASoTfL8rnw_WBkaehSQ,,"We started building our COVID-19 testing dataset over two years ago, in March 2020. At the time, only data on confirmed cases and deaths was available. However, these figures are highly dependent on how much a country has actually tested. Countries with low testing capacities will miss most cases and therefore fail to give an accurate count of deaths caused by the disease. This dataset provided essential context for the data on confirmed cases and deaths, as well as insight into the pandemic and how it spread. You can read more about our COVID-19 testing dataset in our paper: ### A cross-country database of COVID-19 testing https://rdcu.be/b8eoK # Why are we stopping updates? As of 23 June 2022, we will no longer add new data points to our COVID-19 testing dataset. There are several reasons for this. Firstly, reporting methods have become very different between countries, and the type of tests (PCR, antigen, at-home self-tests) included in the data is increasingly unclear. Secondly, testing and case confirmation criteria have diverged significantly between countries: the type of test necessary to confirm a case; if a referral from a GP is required for laboratory tests; if laboratory tests are free or subsidized; if at-home self-tests are available; whether vaccination status influences these parameters. These factors have made harmonizing data definitions across countries difficult to sustain. Furthermore, with very high infection rates since the emergence of the Omicron variant, the main focus of public health authorities has shifted from case metrics to other endpoints, such as hospital and ICU admissions, deaths, and excess mortality (we will continue to collect and [publish data](https://ourworldindata.org/explorers/coronavirus-data-explorer) on all of these important metrics). As a result, countries have begun to report testing data less frequently. # Alternative sources of testing data All historical data collected to date will remain available in our [dataset](https://github.com/owid/covid-19-data/tree/master/public/data), on our [charts](https://ourworldindata.org/explorers/coronavirus-data-explorer), as well as on our [testing entry](https://ourworldindata.org/coronavirus-testing). Future testing data is still available directly from various regional statistical agencies: * The [European CDC](https://www.ecdc.europa.eu/en/publications-data/covid-19-testing) continues to report the number of tests performed each week in its member states, including at the subnational level for some countries. * The [Africa CDC](https://africacdc.maps.arcgis.com/apps/dashboards/a5603222b29c49539df3af45bac16bcc) maintains a dashboard that reports data related to COVID-19, including the number of tests performed; note that for some countries the data is updated infrequently. * The [WHO Eastern Mediterranean Region](https://app.powerbi.com/view?r=eyJrIjoiN2ExNWI3ZGQtZDk3My00YzE2LWFjYmQtNGMwZjk0OWQ1MjFhIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9) also currently maintains a COVID-19 dashboard that reports the number of tests performed in its constituent countries; likewise, the testing data for some countries is updated infrequently. * The [WHO South East Asia Region](https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports) publishes weekly COVID-19 situational reports, which include the testing positivity rate for each country. These sources, however, face the same limitations as our dataset in terms of cross-country comparability. --- ",Ending our COVID-19 testing data updates 1pWACzd00Cc74sAUvFqszD36GP-zmsJMZWfLLkW6hvxI,reserve-vs-resource,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""The terms “reserves” and “resources” are often used interchangeably when it comes to minerals. But there is an important distinction between the two. The chart explains this distinction visually."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Every reserve is indeed a resource, but not every resource is a reserve."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Two requirements determine whether a mineral resource becomes a reserve. The first is the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""degree of certainty that it exists"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "": the planet likely has many mineral resources that we have not yet discovered. So to be defined as a reserve, we must have either a proven, probable, or possible understanding of its existence."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The second criterion relates to the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""economic feasibility"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-bold""}, {""text"": "" of accessing and extracting the mineral resource. To be defined as a reserve, it must be economically and technologically viable to recover. If the economics are subeconomic (i.e. would result in a net loss) or marginal, a mineral resource is not a reserve."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Whilst the original source of this concept — the American geologist Vincent McKelvey — visualized it as a static box, this transition between resources and reserve classifications is dynamic."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As we discover previously unknown resources and develop improved extraction technologies for economic recovery, this reserve box can grow with time. It can also shrink if we consume them at a faster rate than they’re discovered."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""filename"": ""reserves-vs-resources.png"", ""hasOutline"": false, ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""article"", ""title"": ""What’s the difference between mineral reserves and resources?"", ""authors"": [""Hannah Ritchie"", ""Max Roser""], ""excerpt"": ""Every reserve is a resource, but not every resource is a reserve."", ""dateline"": ""July 29, 2024"", ""subtitle"": ""Every reserve is a resource, but not every resource is a reserve."", ""featured-image"": ""reserve-resource-thumbnail.png""}",1,2024-07-29 13:38:33,2024-07-29 11:00:00,1970-01-01 00:00:00,unlisted,ALBJ4LuUhUYeVRcrVTQpekrCQPJbMGL0asWnnRlbwxXltnQPkiQ8brDTxnYq_t48Vlk0G1Xgb2TSip2tHd3t0w,,,What’s the difference between mineral reserves and resources? 1pU8nVBchpI_LYZmNOo4Dag9lHvQkzG-r8JD_57_-1rw,esteban-ortiz-ospina,author,"{""bio"": [{""type"": ""text"", ""value"": [{""text"": ""Esteban is the Executive Co-Director of Our World in Data alongside Max Roser. 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""@eortizospina"", ""type"": ""threads""}], ""parseErrors"": []}, ""featured-image"": ""Este.jpg""}",1,2024-05-23 12:56:02,2024-05-28 12:20:31,1970-01-01 00:00:00,unlisted,ALBJ4LsNSIEV8a2cd1ySeojFyplkvhW8jA0ez3bcVALtTGDfUaqoaxpqyTUzvyO6XFF-8h97IoxF50-oerF3Cg,,,Esteban Ortiz-Ospina 1pRAxZhIjvgSDkKHws4lcG3cXIT5fpwFW8ekkucWVG2M,polio-testing,article,"{""toc"": [{""slug"": ""how-do-countries-monitor-for-polio"", ""text"": ""How do countries monitor for polio?"", ""title"": ""How do countries monitor for polio?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-many-potential-cases-of-polio-are-being-identified-through-screening"", ""text"": ""How many potential cases of polio are being identified through screening?"", ""title"": ""How many potential cases of polio are being identified through screening?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""how-many-potential-cases-are-being-tested-for-polio"", ""text"": ""How many potential cases are being tested for polio?"", ""title"": ""How many potential cases are being tested for polio?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""which-countries-are-screening-and-testing-insufficiently-for-polio"", ""text"": ""Which countries are screening and testing insufficiently for polio?"", ""title"": ""Which countries are screening and testing insufficiently for polio?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""improving-the-monitoring-of-polio-is-crucial-to-achieve-global-eradication"", ""text"": ""Improving the monitoring of polio is crucial to achieve global eradication"", ""title"": ""Improving the monitoring of polio is crucial to achieve global eradication"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""The world is very close to eradicating polio."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It's "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/number-of-estimated-paralytic-polio-cases-by-world-region"", ""children"": [{""text"": ""estimated"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that there were between 300,000 to 400,000 cases of paralytic polio every year in the early 1980s. Since then, the number has declined dramatically. In 2020 there were less than 2,000."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Until recently, there were three strains of wild poliovirus. The world has eradicated two of them, and just one is left in circulation today."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Despite being so close to the finishing line, we are in danger of moving backward."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There has been a disruption in polio testing and reporting during the "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/coronavirus"", ""children"": [{""text"": ""COVID-19 pandemic"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""; this means there's a risk that cases have gone undetected, and the poliovirus has spread further in recent years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Cases have been rising in the few countries where wild poliovirus remains endemic: Afghanistan and Pakistan. We see this in the chart, which shows that cases have risen since 2017."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Worryingly, there is the risk that cases are spreading in countries that have been declared free of wild poliovirus. Malawi and Mozambique each reported a case in 2021 and 2022 respectively, after three decades of seeing no cases of wild polio."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Several countries have also reported cases of "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/polio#vaccine-induced-polio"", ""children"": [{""text"": ""vaccine-derived polio"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", which can be combatted with the novel oral poliovirus vaccine (nOPV2)."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To get back on track to eradicate this virus, the world needs two things: more vaccination and better monitoring of potential outbreaks. Here we focus on the latter: looking at where countries are falling short on monitoring, and what needs to be done."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""left"": [{""url"": ""https://ourworldindata.org/grapher/the-number-of-reported-paralytic-polio-cases?tab=map&country=NGA~AFG~PAK~MWI"", ""type"": ""chart"", ""parseErrors"": []}], ""type"": ""side-by-side"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/the-number-of-reported-paralytic-polio-cases?tab=chart&country=NGA~AFG~PAK~MWI"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""How do countries monitor for polio?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To identify which countries are falling behind on monitoring for polio, we first need to understand how a case of polio is identified."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The primary sign of polio is a type of paralysis, which is called acute flaccid paralysis (AFP)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is how potential cases of polio are identified: when someone suddenly develops AFP, they are considered a potential case of polio."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But polio is not the only illness that can cause AFP – other illnesses, such as Guillain-Barré syndrome, can also cause it. This means that, when a case of AFP is identified, healthcare workers need to test the patient for the presence of poliovirus."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The World Health Organization (WHO) recommends that potential cases should be reported immediately and investigated within 48 hours. To test them, they need to have two stool samples taken within 14 days of the onset of paralysis."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""How many potential cases of polio are being identified through screening?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We need to look at two measures to assess which countries are doing enough monitoring for polio. The first is how much "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""screening"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" they’re doing for potential cases. The second is how much "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""testing"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" they’re doing to confirm the presence of poliovirus."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""If a country is detecting very few cases of AFP overall, then it suggests it’s not screening enough for paralysis. So, to measure how much screening is ongoing, we can look at how many cases of AFP that are "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""not "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""caused by polio are being detected. This is called the non-polio AFP rate."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This rate is measured per 100,000 children. The higher the rate, the more likely that potential cases of polio are being identified. The GPEI recommends that this rate should be at least 2."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, we see the rate of non-polio AFP cases over time. You can view this metric for more countries by clicking the \""Add country\"" button."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In Afghanistan and Pakistan, where polio remains endemic, the rate has improved considerably and has been well above the minimum recommendation, even during the COVID-19 pandemic, when the rate fell slightly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, in Malawi, which recently reported a case of wild polio, the rate has fluctuated above and below the minimum recommendation. In other Eastern African countries such as Rwanda, Burundi, and Zimbabwe, the rate has been just slightly above the minimum recommendation for several years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""With low screening in Malawi recently, it’s possible that polio cases could be more widespread."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/rate-of-acute-flaccid-paralysis-from-non-polio-causes?country=AFG~PAK~MWI~MOZ~BWA~RWA~KEN~IDN~BDI~ZWE"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""How many potential cases are being tested for polio?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The second step is to assess how many potential cases "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""are being tested for poliovirus"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To do this, we look at the percentage of paralysis cases that have stool samples taken to test for polio. The higher this percentage, the more potential cases are being tested to confirm whether they are infected by the poliovirus."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this chart, we can see the testing rate of AFP cases. The WHO recommends that this percentage should be at least 80%."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 2020, in Afghanistan and Pakistan, where polio remains endemic, the testing rate of AFP cases was above the minimum recommendation. Testing has been consistently above the minimum for more than a decade."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Testing rates in Malawi and Mozambique fell below the minimum recommendation during the COVID-19 pandemic. This is particularly concerning, as they each recently reported a case of wild polio, which may mean that more cases are being missed."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In other Eastern African countries, such as Zambia, Tanzania, and the Democratic Republic of Congo, testing rates have also fallen well below the minimum recommendation. Similarly, in Nigeria, which has recently reported cases of vaccine-derived polioviruses, testing has fallen dramatically during the pandemic."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As these countries have had inadequate testing of potential cases in recent years, there is a risk that cases of polio in these countries may have been missed."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/share-of-potential-polio-cases-with-adequate-stool-collection?country=AFG~PAK~NGA~MWI~MOZ~IDN~ZMB~TZA~BWA"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Which countries are screening and testing insufficiently for polio?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Both of these metrics – screening to identify "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""potential"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" cases of polio, and testing to confirm it – are important."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In this map, you can see how countries are performing on both of these metrics."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Some countries have their own screening and testing procedures and are "", ""spanType"": ""span-simple-text""}, {""url"": ""https://polioeradication.org/wp-content/uploads/2022/05/GPSAP-2022-2024-EN.pdf"", ""children"": [{""text"": ""assessed"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" to have a low risk for polio by the Global Polio Eradication Initiative. These are shown in light blue and labeled 'low risk' in the chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 2021, the countries shown in dark blue, such as Afghanistan and Pakistan, which are endemic for polio, had adequate screening and testing for polio."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But in the purple countries – Ukraine, Myanmar, and Cambodia – there was inadequate "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""screening"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" to identify potential cases of polio. This is distressing as Ukraine had reported two cases of vaccine-derived polio in 2021. Although a large vaccination campaign had begun in late 2021, this was halted "", ""spanType"": ""span-simple-text""}, {""url"": ""http://ourworldindata.org/ukraine-war"", ""children"": [{""text"": ""by the war"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Meanwhile, in the countries shown in yellow – India, the Philippines, and Malaysia, as well as many countries in Africa – there was inadequate "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""testing"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" to confirm whether potential cases had polio."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Further, in the red countries – Guinea-Bissau, Laos, and Indonesia – there was both inadequate screening "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""and "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""inadequate testing. This is particularly worrying because it's possible that these countries may be missing cases of vaccine-derived polio, which can be averted using the novel oral poliovirus vaccine."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Low rates of screening and testing make it challenging to direct efforts to contain the disease and they keep us back from achieving polio eradication worldwide."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/polio-screening-and-testing?country=PAK~NGA~MWI~AFG~IND"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Improving the monitoring of polio is crucial to achieve global eradication"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""By not testing for poliovirus sufficiently, we risk letting more cases go undetected and potentially spread to new regions."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Testing is crucial to track the spread of the poliovirus, guide vaccination campaigns and identify people with paralytic polio who can be treated."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To combat this, the Global Polio Eradication Initiative has launched a "", ""spanType"": ""span-simple-text""}, {""url"": ""https://polioeradication.org/wp-content/uploads/2022/05/GPSAP-2022-2024-EN.pdf"", ""children"": [{""text"": ""surveillance action plan"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" that aims to improve the quality and speed of testing, as part of their "", ""spanType"": ""span-simple-text""}, {""url"": ""https://polioeradication.org/news-post/global-polio-eradication-initiative-calls-for-renewed-commitments-to-achieve-promise-of-a-polio-free-world/"", ""children"": [{""text"": ""new strategy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" to achieve a polio-free world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Without increased efforts, we risk rolling back our progress in achieving the global eradication of this terrible disease."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""type"": ""text"", ""value"": [{""text"": ""We would like to thank Hannah Ritchie, Max Roser, Edouard Mathieu and Bastian Herre for reading drafts of this post and their very helpful suggestions to improve it."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Acknowledgements"", ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""071d5f24a59e3b51d6debb74cf206f7029be4cc0"": {""id"": ""071d5f24a59e3b51d6debb74cf206f7029be4cc0"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Global Polio Eradication Initiative. (2019, October 24). Two out of three wild poliovirus strains were eradicated. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""News Stories"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". https://web.archive.org/web/20191024163941/http://polioeradication.org/news-post/two-out-of-three-wild-poliovirus-strains-eradicated/"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""2e21bfaf51bbbc373e6eadb82d6b7444a62d0a35"": {""id"": ""2e21bfaf51bbbc373e6eadb82d6b7444a62d0a35"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The cases in Malawi and Mozambique were found to be genetic descendants of polioviruses seen in Pakistan, even though they had no relevant travel history. Although this has not changed their status of polio-free certification, it suggests that there may be other undetected spread of the poliovirus in the region."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""World Health Organization. (2022, March 3). Wild poliovirus type 1 (WPV1) - Malawi. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Disease Outbreak News"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". 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"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Preparing for a Polio-Free World"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""58307fa918d6a057cf3d42a1702bb5e617658247"": {""id"": ""58307fa918d6a057cf3d42a1702bb5e617658247"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Global Polio Eradication Initiative. (2021, Oct). Preparing for nOPV2 Use: An overview of requirements for countries. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://polioeradication.org/wp-content/uploads/2021/10/nOPV2-Overview-Guidance.pdf"", ""children"": [{""text"": ""https://polioeradication.org/wp-content/uploads/2021/10/nOPV2-Overview-Guidance.pdf"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""6aa1ebc74a51eeb119efa11fb0907e215331b070"": {""id"": ""6aa1ebc74a51eeb119efa11fb0907e215331b070"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Their stool samples should then be processed in a GPEI-accredited laboratory for the presence of the poliovirus. If they test positive, they are considered a 'confirmed case' of polio. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""WHO-recommended surveillance standard of poliomyelitis"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". (n.d.). World Health Organization."", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20210423203212/https://www.who.int/immunization/monitoring_surveillance/burden/vpd/surveillance_type/active/poliomyelitis_standards/en/"", ""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""url"": ""https://web.archive.org/web/20210423203212/https://www.who.int/immunization/monitoring_surveillance/burden/vpd/surveillance_type/active/poliomyelitis_standards/en"", ""children"": [{""text"": ""https://web.archive.org/web/20210423203212/https://www.who.int/immunization/monitoring_surveillance/burden/vpd/surveillance_type/active/poliomyelitis_standards/en/"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Around 1 in 200 infections by the poliovirus result in paralysis. However, only infections that result in paralysis are considered cases of polio in the literature. To avoid confusion, these are labeled cases of paralytic polio."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""729e8ba87e76d17627383d2d4827a379d61e3b64"": {""id"": ""729e8ba87e76d17627383d2d4827a379d61e3b64"", ""index"": 6, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Centers for Disease Control and Prevention (CDC. (2006). Resurgence of wild poliovirus type 1 transmission and consequences of importation--21 countries, 2002-2005. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""MMWR. Morbidity and mortality weekly report"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""55"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(6), 145-150. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://pubmed.ncbi.nlm.nih.gov/16484977/"", ""children"": [{""text"": ""https://pubmed.ncbi.nlm.nih.gov/16484977/"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""8db414dba16753e2191f1d9920698231e04dba45"": {""id"": ""8db414dba16753e2191f1d9920698231e04dba45"", ""index"": 5, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Global Polio Eradication Initiative. (2022, Mar). GPEI deeply concerned for health of Ukrainian people amid escalating crisis. "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""News Stories. "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""url"": ""https://web.archive.org/web/20220324043721/https://polioeradication.org/news-post/global-polio-eradication-initiative-deeply-concerned-for-health-of-ukrainian-people-amid-escalating-crisis/"", ""children"": [{""text"": ""https://web.archive.org/web/20220324043721/https://polioeradication.org/news-post/global-polio-eradication-initiative-deeply-concerned-for-health-of-ukrainian-people-amid-escalating-crisis/"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""ba272ce5db4997b1b624cf586d39a59cf4f7a2cf"": {""id"": ""ba272ce5db4997b1b624cf586d39a59cf4f7a2cf"", ""index"": 4, ""content"": [{""type"": ""text"", ""value"": [{""children"": [{""text"": ""WHO-recommended surveillance standard of poliomyelitis"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "". (n.d.). World Health Organization."", ""spanType"": ""span-simple-text""}, {""url"": ""https://web.archive.org/web/20210423203212/https://www.who.int/immunization/monitoring_surveillance/burden/vpd/surveillance_type/active/poliomyelitis_standards/en/"", ""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""https://web.archive.org/web/20210423203212/https://www.who.int/immunization/monitoring_surveillance/burden/vpd/surveillance_type/active/poliomyelitis_standards/en/"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""We need more testing to eradicate polio worldwide"", ""authors"": [""Saloni Dattani"", ""Fiona Spooner""], ""excerpt"": ""The world is close to eradicating polio, but has been set back in the last few years. To achieve the goal of global eradication, it's crucial to improve testing."", ""dateline"": ""June 9, 2022"", ""subtitle"": ""The world is close to eradicating polio, but has been set back in the last few years. To achieve the goal of global eradication, it's crucial to improve testing."", ""sidebar-toc"": false, ""featured-image"": ""Polio-surveillance-thumbnail.png""}",1,2024-02-07 11:01:41,2022-06-09 10:00:00,2024-03-18 15:41:59,listed,ALBJ4LvZTuvIv9f6nSqeHFYdou29iQIleT7IY79oxpKAu1OHMfUvoSv8bs-H3HAKyfKMv6BDHHbjlEyOrcRyDA,,"The world is very close to eradicating polio. It's [estimated](https://ourworldindata.org/grapher/number-of-estimated-paralytic-polio-cases-by-world-region) that there were between 300,000 to 400,000 cases of paralytic polio every year in the early 1980s. Since then, the number has declined dramatically. In 2020 there were less than 2,000. Until recently, there were three strains of wild poliovirus. The world has eradicated two of them, and just one is left in circulation today.1 Despite being so close to the finishing line, we are in danger of moving backward. There has been a disruption in polio testing and reporting during the [COVID-19 pandemic](http://ourworldindata.org/coronavirus); this means there's a risk that cases have gone undetected, and the poliovirus has spread further in recent years. Cases have been rising in the few countries where wild poliovirus remains endemic: Afghanistan and Pakistan. We see this in the chart, which shows that cases have risen since 2017. Worryingly, there is the risk that cases are spreading in countries that have been declared free of wild poliovirus. Malawi and Mozambique each reported a case in 2021 and 2022 respectively, after three decades of seeing no cases of wild polio.2 Several countries have also reported cases of [vaccine-derived polio](https://ourworldindata.org/polio#vaccine-induced-polio), which can be combatted with the novel oral poliovirus vaccine (nOPV2).3 To get back on track to eradicate this virus, the world needs two things: more vaccination and better monitoring of potential outbreaks. Here we focus on the latter: looking at where countries are falling short on monitoring, and what needs to be done. ## How do countries monitor for polio? To identify which countries are falling behind on monitoring for polio, we first need to understand how a case of polio is identified. The primary sign of polio is a type of paralysis, which is called acute flaccid paralysis (AFP). This is how potential cases of polio are identified: when someone suddenly develops AFP, they are considered a potential case of polio. But polio is not the only illness that can cause AFP – other illnesses, such as Guillain-Barré syndrome, can also cause it. This means that, when a case of AFP is identified, healthcare workers need to test the patient for the presence of poliovirus. The World Health Organization (WHO) recommends that potential cases should be reported immediately and investigated within 48 hours. To test them, they need to have two stool samples taken within 14 days of the onset of paralysis.4 ## How many potential cases of polio are being identified through screening? We need to look at two measures to assess which countries are doing enough monitoring for polio. The first is how much _screening_ they’re doing for potential cases. The second is how much _testing_ they’re doing to confirm the presence of poliovirus. If a country is detecting very few cases of AFP overall, then it suggests it’s not screening enough for paralysis. So, to measure how much screening is ongoing, we can look at how many cases of AFP that are _not _caused by polio are being detected. This is called the non-polio AFP rate. This rate is measured per 100,000 children. The higher the rate, the more likely that potential cases of polio are being identified. The GPEI recommends that this rate should be at least 2. In the chart, we see the rate of non-polio AFP cases over time. You can view this metric for more countries by clicking the ""Add country"" button. In Afghanistan and Pakistan, where polio remains endemic, the rate has improved considerably and has been well above the minimum recommendation, even during the COVID-19 pandemic, when the rate fell slightly. However, in Malawi, which recently reported a case of wild polio, the rate has fluctuated above and below the minimum recommendation. In other Eastern African countries such as Rwanda, Burundi, and Zimbabwe, the rate has been just slightly above the minimum recommendation for several years. With low screening in Malawi recently, it’s possible that polio cases could be more widespread. ## How many potential cases are being tested for polio? The second step is to assess how many potential cases _are being tested for poliovirus_. To do this, we look at the percentage of paralysis cases that have stool samples taken to test for polio. The higher this percentage, the more potential cases are being tested to confirm whether they are infected by the poliovirus. In this chart, we can see the testing rate of AFP cases. The WHO recommends that this percentage should be at least 80%.5 In 2020, in Afghanistan and Pakistan, where polio remains endemic, the testing rate of AFP cases was above the minimum recommendation. Testing has been consistently above the minimum for more than a decade. Testing rates in Malawi and Mozambique fell below the minimum recommendation during the COVID-19 pandemic. This is particularly concerning, as they each recently reported a case of wild polio, which may mean that more cases are being missed. In other Eastern African countries, such as Zambia, Tanzania, and the Democratic Republic of Congo, testing rates have also fallen well below the minimum recommendation. Similarly, in Nigeria, which has recently reported cases of vaccine-derived polioviruses, testing has fallen dramatically during the pandemic. As these countries have had inadequate testing of potential cases in recent years, there is a risk that cases of polio in these countries may have been missed. ## Which countries are screening and testing insufficiently for polio? Both of these metrics – screening to identify _potential_ cases of polio, and testing to confirm it – are important. In this map, you can see how countries are performing on both of these metrics. Some countries have their own screening and testing procedures and are [assessed](https://polioeradication.org/wp-content/uploads/2022/05/GPSAP-2022-2024-EN.pdf) to have a low risk for polio by the Global Polio Eradication Initiative. These are shown in light blue and labeled 'low risk' in the chart. In 2021, the countries shown in dark blue, such as Afghanistan and Pakistan, which are endemic for polio, had adequate screening and testing for polio. But in the purple countries – Ukraine, Myanmar, and Cambodia – there was inadequate _screening_ to identify potential cases of polio. This is distressing as Ukraine had reported two cases of vaccine-derived polio in 2021. Although a large vaccination campaign had begun in late 2021, this was halted [by the war](http://ourworldindata.org/ukraine-war).6 Meanwhile, in the countries shown in yellow – India, the Philippines, and Malaysia, as well as many countries in Africa – there was inadequate _testing_ to confirm whether potential cases had polio. Further, in the red countries – Guinea-Bissau, Laos, and Indonesia – there was both inadequate screening _and _inadequate testing. This is particularly worrying because it's possible that these countries may be missing cases of vaccine-derived polio, which can be averted using the novel oral poliovirus vaccine. Low rates of screening and testing make it challenging to direct efforts to contain the disease and they keep us back from achieving polio eradication worldwide. ## Improving the monitoring of polio is crucial to achieve global eradication By not testing for poliovirus sufficiently, we risk letting more cases go undetected and potentially spread to new regions.7 Testing is crucial to track the spread of the poliovirus, guide vaccination campaigns and identify people with paralytic polio who can be treated. To combat this, the Global Polio Eradication Initiative has launched a [surveillance action plan](https://polioeradication.org/wp-content/uploads/2022/05/GPSAP-2022-2024-EN.pdf) that aims to improve the quality and speed of testing, as part of their [new strategy](https://polioeradication.org/news-post/global-polio-eradication-initiative-calls-for-renewed-commitments-to-achieve-promise-of-a-polio-free-world/) to achieve a polio-free world. Without increased efforts, we risk rolling back our progress in achieving the global eradication of this terrible disease. Global Polio Eradication Initiative. (2019, October 24). Two out of three wild poliovirus strains were eradicated. _News Stories_. https://web.archive.org/web/20191024163941/http://polioeradication.org/news-post/two-out-of-three-wild-poliovirus-strains-eradicated/ The cases in Malawi and Mozambique were found to be genetic descendants of polioviruses seen in Pakistan, even though they had no relevant travel history. Although this has not changed their status of polio-free certification, it suggests that there may be other undetected spread of the poliovirus in the region. World Health Organization. (2022, March 3). Wild poliovirus type 1 (WPV1) - Malawi. _Disease Outbreak News_. [https://web.archive.org/web/20220310133953/https://www.who.int/emergencies/disease-outbreak-news/item/wild-poliovirus-type-1-(WPV1)-malawi](https://web.archive.org/web/20220310133953/https://www.who.int/emergencies/disease-outbreak-news/item/wild-poliovirus-type-1-(WPV1)-malawi) Global Polio Eradication Initiative. (2022, May 18). [GPEI statement on Mozambique WPV1 detection](https://web.archive.org/web/20220519023216/https://polioeradication.org/news-post/gpei-statement-on-mozambique-wpv1-detection/). _News Stories._ Global Polio Eradication Initiative. (n.d.). [Certification](https://web.archive.org/web/20220309151432/https://polioeradication.org/polio-today/preparing-for-a-polio-free-world/certification/). _Preparing for a Polio-Free World_. Global Polio Eradication Initiative. (2021, Oct). Preparing for nOPV2 Use: An overview of requirements for countries. [https://polioeradication.org/wp-content/uploads/2021/10/nOPV2-Overview-Guidance.pdf](https://polioeradication.org/wp-content/uploads/2021/10/nOPV2-Overview-Guidance.pdf) Their stool samples should then be processed in a GPEI-accredited laboratory for the presence of the poliovirus. If they test positive, they are considered a 'confirmed case' of polio. _WHO-recommended surveillance standard of poliomyelitis_. (n.d.). World Health Organization.[ ](https://web.archive.org/web/20210423203212/https://www.who.int/immunization/monitoring_surveillance/burden/vpd/surveillance_type/active/poliomyelitis_standards/en/)[https://web.archive.org/web/20210423203212/https://www.who.int/immunization/monitoring_surveillance/burden/vpd/surveillance_type/active/poliomyelitis_standards/en/](https://web.archive.org/web/20210423203212/https://www.who.int/immunization/monitoring_surveillance/burden/vpd/surveillance_type/active/poliomyelitis_standards/en) Around 1 in 200 infections by the poliovirus result in paralysis. However, only infections that result in paralysis are considered cases of polio in the literature. To avoid confusion, these are labeled cases of paralytic polio. _WHO-recommended surveillance standard of poliomyelitis_. (n.d.). World Health Organization.[ ](https://web.archive.org/web/20210423203212/https://www.who.int/immunization/monitoring_surveillance/burden/vpd/surveillance_type/active/poliomyelitis_standards/en/)https://web.archive.org/web/20210423203212/https://www.who.int/immunization/monitoring_surveillance/burden/vpd/surveillance_type/active/poliomyelitis_standards/en/ Global Polio Eradication Initiative. (2022, Mar). GPEI deeply concerned for health of Ukrainian people amid escalating crisis. _News Stories. _[https://web.archive.org/web/20220324043721/https://polioeradication.org/news-post/global-polio-eradication-initiative-deeply-concerned-for-health-of-ukrainian-people-amid-escalating-crisis/](https://web.archive.org/web/20220324043721/https://polioeradication.org/news-post/global-polio-eradication-initiative-deeply-concerned-for-health-of-ukrainian-people-amid-escalating-crisis/) Centers for Disease Control and Prevention (CDC. (2006). Resurgence of wild poliovirus type 1 transmission and consequences of importation--21 countries, 2002-2005. _MMWR. Morbidity and mortality weekly report_, _55_(6), 145-150. [https://pubmed.ncbi.nlm.nih.gov/16484977/](https://pubmed.ncbi.nlm.nih.gov/16484977/)",We need more testing to eradicate polio worldwide 1pMaqo9GHPCIwsXMOzQ3fW2c2oz4HBn3RK8QwOL2ff_k,pneumonia,linear-topic-page,"{""toc"": [{""slug"": ""burden-of-pneumonia"", ""text"": ""Burden of pneumonia"", ""title"": ""Burden of pneumonia"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""number-of-people-dying-from-pneumonia-by-age"", ""text"": ""Number of people dying from pneumonia by age"", ""title"": ""Number of people dying from pneumonia by age"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""pneumonia-mortality-rates-by-age"", ""text"": ""Pneumonia mortality rates by age"", ""title"": ""Pneumonia mortality rates by age"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""where-do-people-die-from-pneumonia"", ""text"": ""Where do people die from pneumonia?"", ""title"": ""Where do people die from pneumonia?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""pneumonia-death-rates-all-ages"", ""text"": ""Pneumonia death rates — all ages"", ""title"": ""Pneumonia death rates — all ages"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""where-do-children-die-from-pneumonia"", ""text"": ""Where do children die from pneumonia"", ""title"": ""Where do children die from pneumonia"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""how-is-pneumonia-defined-in-global-estimates"", ""text"": ""How is pneumonia defined in global estimates?"", ""title"": ""How is pneumonia defined in global estimates?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""what-are-the-biggest-risks-for-developing-pneumonia"", ""text"": ""What are the biggest risks for developing pneumonia?"", ""title"": ""What are the biggest risks for developing pneumonia?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""why-are-children-dying-from-pneumonia"", ""text"": ""Why are children dying from pneumonia?"", ""title"": ""Why are children dying from pneumonia?"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""pneumonia-risk-factors-for-people-aged-70-and-older"", ""text"": ""Pneumonia risk factors for people aged 70 and older"", ""title"": ""Pneumonia risk factors for people aged 70 and older"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""how-can-we-reduce-the-number-of-people-dying-from-pneumonia"", ""text"": ""How can we reduce the number of people dying from pneumonia?"", ""title"": ""How can we reduce the number of people dying from pneumonia?"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""oxygen-therapy"", ""text"": ""Oxygen therapy"", ""title"": ""Oxygen therapy"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""pneumococcal-vaccines"", ""text"": ""Pneumococcal vaccines"", ""title"": ""Pneumococcal vaccines"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""haemophilus-influenzae-type-b-vaccines"", ""text"": ""Haemophilus influenzae type b vaccines"", ""title"": ""Haemophilus influenzae type b vaccines"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""reducing-air-pollution"", ""text"": ""Reducing air pollution"", ""title"": ""Reducing air pollution"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""access-to-healthcare-and-treatment"", ""text"": ""Access to healthcare and treatment"", ""title"": ""Access to healthcare and treatment"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""access-to-antibiotic-treatment"", ""text"": ""Access to antibiotic treatment"", ""title"": ""Access to antibiotic treatment"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""promoting-breastfeeding"", ""text"": ""Promoting breastfeeding"", ""title"": ""Promoting breastfeeding"", ""supertitle"": """", ""isSubheading"": true}, {""slug"": ""all-charts"", ""text"": ""Interactive charts on pneumonia"", ""title"": ""Interactive charts on pneumonia"", ""isSubheading"": false}, {""slug"": ""article-endnotes"", ""text"": ""Endnotes"", ""title"": ""Endnotes"", ""isSubheading"": false}, {""slug"": ""article-citation"", ""text"": ""Citation"", ""title"": ""Citation"", ""isSubheading"": false}, {""slug"": ""article-licence"", ""text"": ""Licence"", ""title"": ""Licence"", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Pneumonia is one of the most common causes of death worldwide. 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In 1990, more than two million children died from pneumonia every year. 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The death "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""rate"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" from pneumonia in this age group fell slightly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/pneumonia-and-lower-respiratory-diseases-deaths"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Pneumonia mortality rates by age"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows the annual death rate from pneumonia per 100,000 people in different age groups."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Among children under five, the death rate from pneumonia has declined substantially since 1990."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 2019, the highest pneumonia death rates were among people aged 70 and older."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/pneumonia-mortality-by-age"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Where do people die from pneumonia?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Pneumonia death rates — all ages"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The map below shows the annual death rate from pneumonia, per 100,000 people in the population."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The death rates are "", ""spanType"": ""span-simple-text""}, {""id"": ""age_standardized"", ""children"": [{""text"": ""age-standardized"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "", which helps make comparisons between populations of different age structures."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As you can see, the difference between death rates in different world regions is very large. The death rate from pneumonia is highest in sub-Saharan Africa and South East Asia, and much lower in Europe and North America."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/pneumonia-death-rates-70"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Where do children die from pneumonia"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The map below shows the annual death rate from pneumonia among children under five specifically."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It shows that children are most likely to die from pneumonia across Sub-Saharan Africa and South Asia."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/pneumonia-death-rates-in-children-under-5?tab=map"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For this reason, Kevin Watkins and Devi Sridhar called pneumonia “the ultimate disease of poverty” in a 2018 comment in the journal "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""The Lancet"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There is a very strong correlation between a country’s income and the child mortality rate from pneumonia as the scatter plot shows."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The disease is most common in poor places where healthcare infrastructure is lacking and people are least able to afford the treatment."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Read more in our article:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1Gp13RZqqMbpLG68T01Qex5jMIGaR5gheRoJS5Ve0ggs/edit"", ""type"": ""prominent-link"", ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/death-rates-from-pneumonia-and-other-lower-respiratory-infections-vs-gdp-per-capita?xScale=linear&yScale=linear"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""How is pneumonia defined in global estimates?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Ideally, people with pneumonia would receive a specific diagnosis for the condition — to identify that the alveoli in their lungs are affected — and also be diagnosed with the specific infectious cause of pneumonia."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But, in many cases, this doesn’t occur, especially when people lack healthcare access and advanced diagnostic capacity."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is why academic studies use the terms “clinical pneumonia” or “WHO pneumonia”, which refers to cases of pneumonia based on "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""symptoms"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" alone — most importantly, fast breathing and coughing. This definition inevitably means that other diseases with similar symptoms may be counted as cases of pneumonia."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As a consequence, the terms “pneumonia” and “lower respiratory infections” are often used interchangeably."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The Institute for Health Metrics and Evaluation"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(IHME), for example, provides data on deaths from lower respiratory infections, including pneumonia caused by a range of different pathogens as well as bronchiolitis (a lower respiratory tract infection that mostly affects very young children) in the same category."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" "", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""While cases of bronchiolitis are quite common, they are milder, and estimates of the number of deaths from lower respiratory infections generally refer to deaths from infections that cause pneumonia and other lower respiratory infections."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""What are the biggest risks for developing pneumonia?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Why are children dying from pneumonia?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""left"": [{""type"": ""text"", ""value"": [{""text"": ""Despite progress against pneumonia, hundreds of thousands of children still die from the condition each year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To understand how we can reduce the number of children dying from pneumonia we need to understand both prevention and treatment."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows the estimated number of pneumonia deaths caused by different risk factors, in children."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Undernutrition is the major contributor to pneumonia mortality"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart shows that childhood undernutrition — especially \"""", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-children-with-a-weight-too-low-for-their-height-wasting"", ""children"": [{""text"": ""child wasting"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""\"" (children who have a weight too low for their height) — is a major risk factor for pneumonia in children."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Without sufficient energy intake, the body cannot cope with the increased energy demands required to fight off infections."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A literature review of pneumonia in malnourished children by Mohammod Jobayer Chisti and colleagues found that undernourished children are between two to four times more likely to be admitted to hospital due to pneumonia, and up to 15 times more likely to die from it."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""Air pollution and second-hand smoke increase the risk of getting pneumonia"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Air pollution is also a major risk factor for pneumonia, as you can see in the chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This includes both "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/indoor-air-pollution"", ""children"": [{""text"": ""indoor air pollution"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/outdoor-air-pollution"", ""children"": [{""text"": ""outdoor air pollution"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Studies have shown that high indoor air pollution in households can double the chances that children develop pneumonia and make recovery more difficult."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One reason is that "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/air-pollution#exposure-to-particulate-matter"", ""children"": [{""text"": ""small polluting particles"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" impair the immune system’s ability to fight and clear the infection."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Laura Jones et al."", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(2011) reviewed studies on the impact of secondhand "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/smoking"", ""children"": [{""text"": ""smoke"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" on children, and concluded that children who live in households with smoking parents are more likely to acquire pneumonia as well as other respiratory illnesses."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-9"", ""children"": [{""children"": [{""text"": ""9"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The data shows estimates of the global number of pneumonia deaths attributed to secondhand smoke."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""The risk of pneumonia is higher for children with HIV"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Other infectious diseases, such as "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/vaccination#measles-global-vaccination-coverage-and-decline-of-measles"", ""children"": [{""text"": ""measles"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/hiv-aids"", ""children"": [{""text"": ""HIV"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", also increase the risk of pneumonia in children."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When children who are infected with "", ""spanType"": ""span-simple-text""}, {""id"": ""hiv"", ""children"": [{""text"": ""HIV"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" develop "", ""spanType"": ""span-simple-text""}, {""id"": ""hiv_aids"", ""children"": [{""text"": ""AIDS"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-dod""}, {""text"": "" — which weakens their immune system — their chances of dying from pneumonia increase substantially."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A study by Evropi Theodoratou et "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""al., "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""published in "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Lancet Infectious Diseases,"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" found that children with HIV have a seven times greater risk of dying from pneumonia than those without it."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-10"", ""children"": [{""children"": [{""text"": ""10"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Read more on our page on HIV/AIDS:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://docs.google.com/document/d/1w9rFMftjTzNXOeVgseDsxxkX40o-hEFgC2cHAhuuT9E/edit"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""Overcrowding facilitates pneumonia transmission"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Pathogens that cause pneumonia tend to be more easily transmitted between people who are living together — through close contact, respiratory droplets, or airborne particles."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Therefore, overcrowding — too many people living in one space — also increases the risks of pneumonia."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This is yet another reason why pneumonia is a disease of poverty, children in low and middle-income countries are more likely to live in overcrowded households."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-11"", ""children"": [{""children"": [{""text"": ""11"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""url"": ""https://ourworldindata.org/grapher/pneumonia-risk-factors?time=latest"", ""type"": ""chart"", ""parseErrors"": []}], ""parseErrors"": []}, {""text"": [{""text"": ""Pneumonia risk factors for people aged 70 and older"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The risk factors for developing pneumonia in people aged 70 and older are similar to the risk factors that lead to pneumonia in children."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/outdoor-air-pollution"", ""children"": [{""text"": ""Outdoor air pollution"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" — small particulate matter air pollution — is a major risk factor for dying from pneumonia, as you can see in the chart."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In addition, smoking and exposure to secondhand smoke are also important risk factors."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/deaths-from-pneumonia-in-people-aged-70-and-older-by-risk-factor?time=latest"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""How can we reduce the number of people dying from pneumonia?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""text"": [{""text"": ""Oxygen therapy"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One of the main risks of pneumonia is breathing difficulties, and people become unable to take in oxygen properly."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""When pneumonia develops, the alveoli in the lungs get filled with pus and fluid, which prevents oxygen from being transferred to the blood."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Consequently, a condition known as hypoxemia — which means a lack of oxygen in the blood — can develop."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A study by Marzia Lazzerini et al. finds that children with pneumonia who develop hypoxemia have a five times higher risk of death."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-12"", ""children"": [{""children"": [{""text"": ""12"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To treat this condition, oxygen therapy — supplying oxygen-enriched air to the patient — can be very important."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-13"", ""children"": [{""children"": [{""text"": ""13"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since 2017, the WHO has included oxygen in its “List of Essential Medicines”."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-14"", ""children"": [{""children"": [{""text"": ""14"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""Pneumococcal vaccines"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since pneumonia is caused by different infections, a range of vaccines can be used to protect against it, including pneumococcal vaccines, Hib vaccines, influenza vaccines, respiratory syncytial virus (RSV) vaccines, and more."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Pneumococcal vaccines protect against pneumonia from the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Streptococcus pneumoniae"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" bacteria, which is one of the major causes of pneumonia in children under 5."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-15"", ""children"": [{""children"": [{""text"": ""15"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are several versions of "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-one-year-olds-who-received-the-final-dose-of-pneumococcal-vaccine"", ""children"": [{""text"": ""pneumococcal conjugate vaccine"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" (PCV), which target different serotypes of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""S. pneumoniae"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The PCV vaccine is given to children younger than 24 months. According to a study by Cheryl Cohen et al. (2017), PVC13 — a common version of the PCV vaccine — reduces the risk of invasive pneumococcal infections by 85%, for the strains"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": "" "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""included in the vaccine formulation."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-16"", ""children"": [{""children"": [{""text"": ""16"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Despite this efficacy, the coverage of pneumococcal vaccines still lags behind other vaccines."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""text"": [{""text"": ""Coverage of pneumococcal vaccination"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since the World Health Organisation (WHO) started recommending including pneumococcal vaccines in national immunisation programmes for children in 2007, there has been a progressive increase in the number of countries using the vaccine, which you can see in the map, by pressing the \""Play timelapse\"" button."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/pneumococcal-conjugate-vaccine-immunization-schedule"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But the coverage of pneumococcal vaccines is still low in many countries, as you can see in the map."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 2021, around half of one-year-olds worldwide received the third dose of pneumococcal vaccines."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This means that millions of children who could be protected against pneumonia — one of the leading causes of death — were still not vaccinated against it."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-17"", ""children"": [{""children"": [{""text"": ""17"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/share-of-one-year-olds-who-received-the-final-dose-of-pneumococcal-vaccine"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""How do pneumococcal vaccines work?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""children"": [{""text"": ""Streptococcus pneumonia"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", often simply referred to as pneumococcus, is a bacterium that is often found in the upper respiratory tract of healthy people."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Generally, the bacterium is harmless or causes milder illnesses such as bronchitis, sinusitis, and ear infections."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Pneumococcal vaccines are effective against these milder illnesses as well, but importantly also protects from what is called “pneumococcal invasive disease”, which is more severe. This severe form of the disease develops when the bacteria moves from the upper respiratory tract to other parts of the body that such as the blood, cerebrospinal fluid or pleural cavity (fluid-filled space surrounding the lungs) — which are usually free of pathogens."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-18"", ""children"": [{""children"": [{""text"": ""18"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""By invading these parts of the body, the bacteria leads to life-threatening diseases such as sepsis, meningitis and severe pneumonia."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart below shows the number of deaths in children from the different diseases that the bacteria can cause."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/child-deaths-from-streptococcus-by-disease"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are two types of pneumococcal vaccines available: conjugated polysaccharide pneumococcal vaccine (PCV) and non-conjugated polysaccharide pneumococcal vaccine (PPSV)."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Both types stimulate an immune response against multiple serotypes of the "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""S. pneumoniae "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""bacteria. There are many serotypes of the bacteria — which are defined by the different immune responses to the sugars found on the bacterial surface"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-19"", ""children"": [{""children"": [{""text"": ""19"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" — but only a limited subset of them are responsible for most cases of invasive pneumococcal disease currently."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-20"", ""children"": [{""children"": [{""text"": ""20"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""How effective are pneumococcal vaccines?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Pneumococcal vaccines are highly effective at reducing the risk of severe pneumonia."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In clinical trials, the PCV vaccines have reduced the risk of invasive pneumococcal disease by 80%, among the serotypes included in the vaccine formulation. In addition, vaccinated children are 27% less likely to be diagnosed with pneumonia overall and 11% less likely to die from it."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-21"", ""children"": [{""children"": [{""text"": ""21"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As of 2023, it’s recommended that young children — under the age of two — receive the PCV vaccines, because the PPSV vaccines are not effective at such a young age."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-22"", ""children"": [{""children"": [{""text"": ""22"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""text"": [{""text"": ""How many child deaths could be averted by pneumococcal vaccines?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Several studies have attempted to estimate how many lives PCV vaccination has saved and could save."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""A study by Brian Wahl et al. (2018) that the number of childhood deaths caused by pneumococcus fell from 600,000 to 294,000 across 120 countries between 2000 and 2015, which is a decline of 54%."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Most of this decline was attributed to the PCV vaccines: over this period, it’s estimated these vaccines saved the lives of 250,000 children. The majority of these deaths would have been caused by pneumonia, but the vaccine also prevented deaths from pneumococcal meningitis and other diseases."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-23"", ""children"": [{""children"": [{""text"": ""23"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart below shows how many more lives the pneumococcal vaccine could save."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It is based on a study by Cynthia Chen et al. (2019), which estimated the number of lives that would be saved if vaccination rates for PCV3 matched vaccination rates of DTP3, which is a vaccine "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/share-of-children-immunized-dtp3"", ""children"": [{""text"": ""against diphtheria, tetanus, and pertussis"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""With this approach, they estimate that, in total, around 400,000 children under 5 could be saved annually."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-24"", ""children"": [{""children"": [{""text"": ""24"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" In addition, they estimated that around 54.6 million cases of pneumonia could be averted annually."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""It’s important to note that these numbers estimate the impact of the PCV vaccination relative to a world "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""without"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" that vaccine. Since the vaccine is already used, some of these lives are already being saved by PCV vaccination."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/pneumococcal-vaccination-averted-deaths"", ""type"": ""chart"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, in many countries, PCV vaccination rates still fall far below the DTP3 rates, as you can see below. This makes it clear that we still haven’t used the pneumococcal vaccine to its full potential."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""url"": ""https://ourworldindata.org/grapher/diphtheria-tetanus-pertussis-vaccine-vs-pneumococcal-vaccine-coverage"", ""type"": ""chart"", ""parseErrors"": []}, {""text"": [{""text"": ""What can we do to improve the coverage and effectiveness of pneumococcal vaccines?"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 3, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To make full potential of pneumococcal vaccines, it’s important to have an increased introduction and coverage of the vaccines in countries around the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The cost of PCV vaccines ranges widely: from $3.05 per dose in countries supported by Gavi, the vaccine alliance, which subsidizes the cost of vaccination in low-income countries,"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-25"", ""children"": [{""children"": [{""text"": ""25"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" to $169 in high-income countries, such as the United States."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-26"", ""children"": [{""children"": [{""text"": ""26"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""For low-middle-income countries who are transitioning away from GAVI support, the increasing future costs of vaccination could place a strain on national healthcare budgets."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-27"", ""children"": [{""children"": [{""text"": ""27"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But, because of the high impact of pneumonia, PCV vaccines are considered to be very cost-effective, with an estimated return of investment in low- and middle-income countries of around 3."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-28"", ""children"": [{""children"": [{""text"": ""28"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Another way to make full use of the potential of pneumococcal vaccines is by collecting data on which serotypes of "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""S. pneumoniae"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" are common in countries, and adapting vaccines to target these serotypes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""As mentioned previously, PCV vaccines include a limited number of pneumococcal serotypes. The distribution of pneumococcal serotypes is known to vary between countries, and PCV vaccines include the ones that are most common globally."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But the serotypes that are most common in a particular country may affect the potential for a particular vaccine’s impact."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""However, not all countries collect data on the serotypes common in countries, which reduces the efficacy of pneumococcal vaccination."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-29"", ""children"": [{""children"": [{""text"": ""29"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Since the PCV vaccine was introduced, cases of invasive pneumococcal disease have reduced substantially. But now, the remaining cases tend to be from the less common serotypes, which the vaccine doesn’t protect against."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-30"", ""children"": [{""children"": [{""text"": ""30"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This highlights that it’s important to continually collect data on the serotypes affecting a country, and adapt vaccines against a wider range of serotypes. 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The study suggested that exposure to secondhand smoke led to 165,000 deaths among children under 5 from lower respiratory diseases that year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""linear-topic-page"", ""title"": ""Pneumonia"", ""authors"": [""Bernadeta Dadonaite"", ""Max Roser""], ""excerpt"": ""Pneumonia is the leading cause of death for children younger than 5 years."", ""dateline"": ""This page was first published in November 2019, and last revised in February 2024."", ""subtitle"": """", ""sidebar-toc"": true, ""featured-image"": ""pneumonia-thumbnail.png""}",1,2023-11-10 16:02:14,2019-11-01 15:04:16,2024-02-29 11:07:22,unlisted,ALBJ4Ltyqe7oEsf5aJ3pgniFVkURwkILaK3KtYx_HeP89JgrD7ahSA96Y89LtEFxrQ9KA1L5mAqcXwo60Aj9Zg,,"Pneumonia is one of the most common causes of death worldwide. It is a condition of the inflammation of the lungs, specifically in the alveoli, which are millions of tiny air sacs that help us take in oxygen. In pneumonia, these alveoli become filled with pus and fluid, which makes breathing painful and reduces our ability to take in oxygen from the air we breathe and exhale carbon dioxide. Pneumonia is one of the leading [causes of death](https://ourworldindata.org/causes-of-death) worldwide. It can develop from a range of different infections, which are caused by different pathogens, including viruses, bacteria, and fungi. This includes, for example, _Streptococcus pneumoniae_,_ Haemophilus influenzae_,_ Staphylococcus aureus_, [influenza](https://ourworldindata.org/influenza) (flu), respiratory syncytial virus (RSV), and more. These pathogens are contagious and can spread when a person coughs or sneezes. On this topic page, we look at who is suffering from pneumonia and why — and what can we do to reduce the number of people dying from this disease, with interventions such as vaccination, oxygen therapy, reducing air pollution, and more. **[See all interactive charts on pneumonia ↓](#all-charts)** ### Related topics ### undefined undefined https://docs.google.com/document/d/1h37-KpdZ3qAJ9xSA4OhbmGfmLr3Ydc_z9oHtDTszBuQ/edit ### undefined undefined https://docs.google.com/document/d/1lmIiF_8Vlm2_ysg5UXeRPmQ_M8XbmXqzRs7g2uGwGyc/edit **Other research and writing on pneumonia on Our World in Data:** * [Pneumonia — no child should die from a disease we can prevent](https://ourworldindata.org/child-deaths-from-pneumonia) # Burden of pneumonia ## Number of people dying from pneumonia by age The chart here shows the global number of deaths from pneumonia1 by age group. The number of children dying from pneumonia has decreased substantially over the past three decades. In 1990, more than two million children died from pneumonia every year. By 2019, this number had fallen by almost two-thirds. Reductions in the major risk factors such as [childhood wasting](https://ourworldindata.org/hunger-and-undernourishment#too-little-weight-for-height-wasting), [air pollution](https://ourworldindata.org/air-pollution), and [poor sanitation](https://ourworldindata.org/grapher/death-rate-from-unsafe-sanitation), as well as falling [global poverty](https://ourworldindata.org/extreme-poverty) and better availability of health technology, such as pneumococcal vaccines and antibiotics, have all contributed to this decline. The number of deaths among those aged 70 and older increased — from around 600,000 in 1990 to over 1 million in 2019. This is largely because of a [growing and aging population](https://ourworldindata.org/age-structure). The death _rate_ from pneumonia in this age group fell slightly. ## Pneumonia mortality rates by age The chart shows the annual death rate from pneumonia per 100,000 people in different age groups. Among children under five, the death rate from pneumonia has declined substantially since 1990. In 2019, the highest pneumonia death rates were among people aged 70 and older. # Where do people die from pneumonia? ## Pneumonia death rates — all ages The map below shows the annual death rate from pneumonia, per 100,000 people in the population. The death rates are age-standardized, which helps make comparisons between populations of different age structures. As you can see, the difference between death rates in different world regions is very large. The death rate from pneumonia is highest in sub-Saharan Africa and South East Asia, and much lower in Europe and North America. ## Where do children die from pneumonia The map below shows the annual death rate from pneumonia among children under five specifically. It shows that children are most likely to die from pneumonia across Sub-Saharan Africa and South Asia. For this reason, Kevin Watkins and Devi Sridhar called pneumonia “the ultimate disease of poverty” in a 2018 comment in the journal _The Lancet_.2 There is a very strong correlation between a country’s income and the child mortality rate from pneumonia as the scatter plot shows. The disease is most common in poor places where healthcare infrastructure is lacking and people are least able to afford the treatment.3 Read more in our article: ### undefined undefined https://docs.google.com/document/d/1Gp13RZqqMbpLG68T01Qex5jMIGaR5gheRoJS5Ve0ggs/edit ## How is pneumonia defined in global estimates? Ideally, people with pneumonia would receive a specific diagnosis for the condition — to identify that the alveoli in their lungs are affected — and also be diagnosed with the specific infectious cause of pneumonia. But, in many cases, this doesn’t occur, especially when people lack healthcare access and advanced diagnostic capacity. This is why academic studies use the terms “clinical pneumonia” or “WHO pneumonia”, which refers to cases of pneumonia based on _symptoms_ alone — most importantly, fast breathing and coughing. This definition inevitably means that other diseases with similar symptoms may be counted as cases of pneumonia. As a consequence, the terms “pneumonia” and “lower respiratory infections” are often used interchangeably. The Institute for Health Metrics and Evaluation_ _(IHME), for example, provides data on deaths from lower respiratory infections, including pneumonia caused by a range of different pathogens as well as bronchiolitis (a lower respiratory tract infection that mostly affects very young children) in the same category.4 5 While cases of bronchiolitis are quite common, they are milder, and estimates of the number of deaths from lower respiratory infections generally refer to deaths from infections that cause pneumonia and other lower respiratory infections. # What are the biggest risks for developing pneumonia? ## Why are children dying from pneumonia? Despite progress against pneumonia, hundreds of thousands of children still die from the condition each year. To understand how we can reduce the number of children dying from pneumonia we need to understand both prevention and treatment. The chart shows the estimated number of pneumonia deaths caused by different risk factors, in children. ### Undernutrition is the major contributor to pneumonia mortality The chart shows that childhood undernutrition — especially ""[child wasting](https://ourworldindata.org/grapher/share-of-children-with-a-weight-too-low-for-their-height-wasting)"" (children who have a weight too low for their height) — is a major risk factor for pneumonia in children.6 Without sufficient energy intake, the body cannot cope with the increased energy demands required to fight off infections. A literature review of pneumonia in malnourished children by Mohammod Jobayer Chisti and colleagues found that undernourished children are between two to four times more likely to be admitted to hospital due to pneumonia, and up to 15 times more likely to die from it.7 ### Air pollution and second-hand smoke increase the risk of getting pneumonia Air pollution is also a major risk factor for pneumonia, as you can see in the chart. This includes both [indoor air pollution](https://ourworldindata.org/indoor-air-pollution) and [outdoor air pollution](https://ourworldindata.org/outdoor-air-pollution). Studies have shown that high indoor air pollution in households can double the chances that children develop pneumonia and make recovery more difficult.8 One reason is that [small polluting particles](https://ourworldindata.org/air-pollution#exposure-to-particulate-matter) impair the immune system’s ability to fight and clear the infection. Laura Jones et al._ _(2011) reviewed studies on the impact of secondhand [smoke](https://ourworldindata.org/smoking) on children, and concluded that children who live in households with smoking parents are more likely to acquire pneumonia as well as other respiratory illnesses.9 The data shows estimates of the global number of pneumonia deaths attributed to secondhand smoke. ### The risk of pneumonia is higher for children with HIV Other infectious diseases, such as [measles](https://ourworldindata.org/vaccination#measles-global-vaccination-coverage-and-decline-of-measles) and [HIV](https://ourworldindata.org/hiv-aids), also increase the risk of pneumonia in children. When children who are infected with HIV develop AIDS — which weakens their immune system — their chances of dying from pneumonia increase substantially. A study by Evropi Theodoratou et _al., _published in _Lancet Infectious Diseases,_ found that children with HIV have a seven times greater risk of dying from pneumonia than those without it.10 Read more on our page on HIV/AIDS: ### Overcrowding facilitates pneumonia transmission Pathogens that cause pneumonia tend to be more easily transmitted between people who are living together — through close contact, respiratory droplets, or airborne particles. Therefore, overcrowding — too many people living in one space — also increases the risks of pneumonia. This is yet another reason why pneumonia is a disease of poverty, children in low and middle-income countries are more likely to live in overcrowded households.11 ## Pneumonia risk factors for people aged 70 and older The risk factors for developing pneumonia in people aged 70 and older are similar to the risk factors that lead to pneumonia in children. [Outdoor air pollution](https://ourworldindata.org/outdoor-air-pollution) — small particulate matter air pollution — is a major risk factor for dying from pneumonia, as you can see in the chart. In addition, smoking and exposure to secondhand smoke are also important risk factors. # How can we reduce the number of people dying from pneumonia? ## Oxygen therapy One of the main risks of pneumonia is breathing difficulties, and people become unable to take in oxygen properly. When pneumonia develops, the alveoli in the lungs get filled with pus and fluid, which prevents oxygen from being transferred to the blood. Consequently, a condition known as hypoxemia — which means a lack of oxygen in the blood — can develop. A study by Marzia Lazzerini et al. finds that children with pneumonia who develop hypoxemia have a five times higher risk of death.12 To treat this condition, oxygen therapy — supplying oxygen-enriched air to the patient — can be very important.13 Since 2017, the WHO has included oxygen in its “List of Essential Medicines”.14 ## Pneumococcal vaccines Since pneumonia is caused by different infections, a range of vaccines can be used to protect against it, including pneumococcal vaccines, Hib vaccines, influenza vaccines, respiratory syncytial virus (RSV) vaccines, and more. Pneumococcal vaccines protect against pneumonia from the _Streptococcus pneumoniae_ bacteria, which is one of the major causes of pneumonia in children under 5.15 There are several versions of [pneumococcal conjugate vaccine](https://ourworldindata.org/grapher/share-of-one-year-olds-who-received-the-final-dose-of-pneumococcal-vaccine) (PCV), which target different serotypes of _S. pneumoniae_. The PCV vaccine is given to children younger than 24 months. According to a study by Cheryl Cohen et al. (2017), PVC13 — a common version of the PCV vaccine — reduces the risk of invasive pneumococcal infections by 85%, for the strains_ _included in the vaccine formulation.16 Despite this efficacy, the coverage of pneumococcal vaccines still lags behind other vaccines. ### Coverage of pneumococcal vaccination Since the World Health Organisation (WHO) started recommending including pneumococcal vaccines in national immunisation programmes for children in 2007, there has been a progressive increase in the number of countries using the vaccine, which you can see in the map, by pressing the ""Play timelapse"" button. But the coverage of pneumococcal vaccines is still low in many countries, as you can see in the map. In 2021, around half of one-year-olds worldwide received the third dose of pneumococcal vaccines. This means that millions of children who could be protected against pneumonia — one of the leading causes of death — were still not vaccinated against it.17 ### How do pneumococcal vaccines work? _Streptococcus pneumonia_, often simply referred to as pneumococcus, is a bacterium that is often found in the upper respiratory tract of healthy people. Generally, the bacterium is harmless or causes milder illnesses such as bronchitis, sinusitis, and ear infections. Pneumococcal vaccines are effective against these milder illnesses as well, but importantly also protects from what is called “pneumococcal invasive disease”, which is more severe. This severe form of the disease develops when the bacteria moves from the upper respiratory tract to other parts of the body that such as the blood, cerebrospinal fluid or pleural cavity (fluid-filled space surrounding the lungs) — which are usually free of pathogens.18 By invading these parts of the body, the bacteria leads to life-threatening diseases such as sepsis, meningitis and severe pneumonia. The chart below shows the number of deaths in children from the different diseases that the bacteria can cause. There are two types of pneumococcal vaccines available: conjugated polysaccharide pneumococcal vaccine (PCV) and non-conjugated polysaccharide pneumococcal vaccine (PPSV). Both types stimulate an immune response against multiple serotypes of the _S. pneumoniae _bacteria. There are many serotypes of the bacteria — which are defined by the different immune responses to the sugars found on the bacterial surface19 — but only a limited subset of them are responsible for most cases of invasive pneumococcal disease currently.20 ### How effective are pneumococcal vaccines? Pneumococcal vaccines are highly effective at reducing the risk of severe pneumonia. In clinical trials, the PCV vaccines have reduced the risk of invasive pneumococcal disease by 80%, among the serotypes included in the vaccine formulation. In addition, vaccinated children are 27% less likely to be diagnosed with pneumonia overall and 11% less likely to die from it.21 As of 2023, it’s recommended that young children — under the age of two — receive the PCV vaccines, because the PPSV vaccines are not effective at such a young age.22 ### How many child deaths could be averted by pneumococcal vaccines? Several studies have attempted to estimate how many lives PCV vaccination has saved and could save. A study by Brian Wahl et al. (2018) that the number of childhood deaths caused by pneumococcus fell from 600,000 to 294,000 across 120 countries between 2000 and 2015, which is a decline of 54%. Most of this decline was attributed to the PCV vaccines: over this period, it’s estimated these vaccines saved the lives of 250,000 children. The majority of these deaths would have been caused by pneumonia, but the vaccine also prevented deaths from pneumococcal meningitis and other diseases.23 The chart below shows how many more lives the pneumococcal vaccine could save. It is based on a study by Cynthia Chen et al. (2019), which estimated the number of lives that would be saved if vaccination rates for PCV3 matched vaccination rates of DTP3, which is a vaccine [against diphtheria, tetanus, and pertussis](https://ourworldindata.org/grapher/share-of-children-immunized-dtp3). With this approach, they estimate that, in total, around 400,000 children under 5 could be saved annually.24 In addition, they estimated that around 54.6 million cases of pneumonia could be averted annually. It’s important to note that these numbers estimate the impact of the PCV vaccination relative to a world _without_ that vaccine. Since the vaccine is already used, some of these lives are already being saved by PCV vaccination. However, in many countries, PCV vaccination rates still fall far below the DTP3 rates, as you can see below. This makes it clear that we still haven’t used the pneumococcal vaccine to its full potential. ### What can we do to improve the coverage and effectiveness of pneumococcal vaccines? To make full potential of pneumococcal vaccines, it’s important to have an increased introduction and coverage of the vaccines in countries around the world. The cost of PCV vaccines ranges widely: from $3.05 per dose in countries supported by Gavi, the vaccine alliance, which subsidizes the cost of vaccination in low-income countries,25 to $169 in high-income countries, such as the United States.26 For low-middle-income countries who are transitioning away from GAVI support, the increasing future costs of vaccination could place a strain on national healthcare budgets.27 But, because of the high impact of pneumonia, PCV vaccines are considered to be very cost-effective, with an estimated return of investment in low- and middle-income countries of around 3.28 Another way to make full use of the potential of pneumococcal vaccines is by collecting data on which serotypes of _S. pneumoniae_ are common in countries, and adapting vaccines to target these serotypes. As mentioned previously, PCV vaccines include a limited number of pneumococcal serotypes. The distribution of pneumococcal serotypes is known to vary between countries, and PCV vaccines include the ones that are most common globally. But the serotypes that are most common in a particular country may affect the potential for a particular vaccine’s impact. However, not all countries collect data on the serotypes common in countries, which reduces the efficacy of pneumococcal vaccination.29 Since the PCV vaccine was introduced, cases of invasive pneumococcal disease have reduced substantially. But now, the remaining cases tend to be from the less common serotypes, which the vaccine doesn’t protect against.30 This highlights that it’s important to continually collect data on the serotypes affecting a country, and adapt vaccines against a wider range of serotypes. Some fo these vaccines are already in development.31 ## _Haemophilus influenzae_ type b vaccines Another vaccine widely used to protect children against pneumonia is the [Hib vaccine](https://ourworldindata.org/grapher/hib-vaccine), which protects children against _Haemophilus influenzae_ type b, a major cause of meningitis in children. Researchers estimate that the Hib vaccine reduces the risk of Hib-related pneumonia by around 70% and the risk of meningitis by around 84%, in children.32 The chart below shows the share of children vaccinated against _Haemophilus influenzae _type b. Aside from oxygen therapy and vaccination, other interventions can also help protect children against pneumonia. ## Reducing air pollution There has been significant progress in reducing [air pollution](https://ourworldindata.org/air-pollution) levels in recent decades, particularly [indoor air pollution](https://ourworldindata.org/indoor-air-pollution). Death rates from indoor air pollution have fallen as a result of [improved access](https://ourworldindata.org/grapher/access-to-clean-fuels-and-technologies-for-cooking?tab=chart&time=2000..2016&country=OWID_WRL) to cleaner fuels for heating and cooking. But there is still much progress to be made, especially in Sub-Saharan Africa, where most households [lack access to clean fuels for cooking](https://ourworldindata.org/grapher/access-to-clean-fuels-and-technologies-for-cooking). And, although progress has been made against indoor air pollution, high [outdoor pollution](https://ourworldindata.org/grapher/pm25-air-pollution) remains a problem across many countries. Reducing air pollution levels would have also many other benefits: it would also reduce the incidence of other diseases such as asthma in children, for example.33 Read more on our page on air pollution: ### undefined undefined https://docs.google.com/document/d/1IxPaKLVC-rm0doT1JYDB2uFGR2TKNQ7Bq3d6riHCBuU/edit ## Access to healthcare and treatment A child with a suspected case of pneumonia — who has difficulty breathing and has been coughing — should be taken to a healthcare provider so that proper immediate treatment can be provided. Delays in seeking treatment can increase the risk of a child dying.34 But, as the map shows, seeking healthcare is still not as common as it should be. Globally, less than two-thirds of children with symptoms of pneumonia were taken to a healthcare provider in 2016. This figure is even lower in places where healthcare is most needed — just 47% in Sub-Saharan Africa.35 As the map shows, the share of children with symptoms of pneumonia that are taken to a health provider is still low in many countries. ## Access to antibiotic treatment Given that most cases of pneumonia are caused by bacteria, antibiotics are the general course of treatment. Due to the lack of resources, in places where pneumonia cases are most common, a quick diagnosis of the cause of the disease is not always possible. Because of the risk of death from untreated pneumonia, the World Health Organisation (WHO) recommends treatment with antibiotics – depending on the person’s symptoms and their severity — before the cause of disease is known. Amoxicillin, ampicillin, and gentamicin are the most commonly-used antibiotics to treat pneumonia.36 Antibiotics are a relatively cheap and effective treatment. 37 38 ## Promoting breastfeeding Encouraging mothers to breastfeed during the first 6 months of a child’s life can have a positive impact on reducing child undernutrition, and help protect them from some infectious pathogens, such as those that can lead to pneumonia. According to a study by Laura Lamberti et al._ _(2013), children in developing countries who are not breastfed in the first five months of their lives have a much higher risk of dying from pneumonia than those who exclusively received their mother’s milk.39 As the map shows, the number of infants who are exclusively breastfed is still low in many countries.40 We use the term pneumonia here as a broad term for lower respiratory infections, as explained earlier on the page. Watkins, K., & Sridhar, D. (2018). [Pneumonia: a global cause without champions.](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(18)31666-0/fulltext)_ The Lancet_, _392_(10149), 718-719. The Lancet Global Health Editorial (2018). [The disgraceful neglect of childhood pneumonia.](https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(18)30495-9/fulltext)_ The Lancet. Global health_, _6_(12), e1253. Institute for Health Metrics and Evaluation (IMHE). (2014). [Pushing the Pace: Progress and Challenges in Fighting Childhood Pneumonia.](http://www.healthdata.org/sites/default/files/files/policy_report/2014/PolicyReport_IHME_PushingthePace_2014.pdf) McAllister, D. A., Liu, L., Shi, T., Chu, Y., Reed, C., Burrows, J., ... & Nair, H. (2019). [Global, regional, and national estimates of pneumonia morbidity and mortality in children younger than 5 years between 2000 and 2015: a systematic analysis.](https://www.thelancet.com/cms/10.1016/S2214-109X(18)30408-X/attachment/41ca9e56-e528-4788-bb38-1d4c9f76e6d4/mmc1.pdf)_The Lancet Global Health_, _7_(1), e47-e57. Troeger, C., Blacker, B., Khalil, I. A., Rao, P. C., Cao, J., Zimsen, S. R., … & Adetifa, I. M. O. (2018). [Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(18)30310-4/fulltext)_The Lancet Infectious Diseases_, _18_(11), 1191-1210. Chisti, M. J., Tebruegge, M., La Vincente, S., Graham, S. M., & Duke, T. (2009). [Pneumonia in severely malnourished children in developing countries–mortality risk, aetiology and validity of WHO clinical signs: a systematic review.](https://www.ncbi.nlm.nih.gov/pubmed/19772545?dopt=Abstract)_ __Tropical medicine & international health_, _14_(10), 1173-1189. Dherani, M., Pope, D., Mascarenhas, M., Smith, K. R., Weber, M., & Bruce, N. (2008). [Indoor air pollution from unprocessed solid fuel use and pneumonia risk in children aged under five years: a systematic review and meta-analysis.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2647443/) _Bulletin of the World Health Organization_ (2006). [Air quality guidelines: global update 2005.](http://www.euro.who.int/__data/assets/pdf_file/0005/78638/E90038.pdf) p123-124. Nel, A. (2005). [Air pollution-related illness: effects of particles](https://science.sciencemag.org/content/308/5723/804). _Science_, _308_(5723), 804-806. Öberg, M., Jaakkola, M. S., Woodward, A., Peruga, A., & Prüss-Ustün, A. (2011). [Worldwide burden of disease from exposure to second-hand smoke: a retrospective analysis of data from 192 countries](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(10)61388-8/fulltext)_. The Lancet_, _377_(9760), 139-146. The study suggested that exposure to secondhand smoke led to 165,000 deaths among children under 5 from lower respiratory diseases that year. Theodoratou, E., McAllister, D. A., Reed, C., Adeloye, D. O., Rudan, I., Muhe, L. M., … & Nair, H. (2014). [Global, regional, and national estimates of pneumonia burden in HIV-infected children in 2010: a meta-analysis and modelling study.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4242006/)_ The Lancet Infectious Diseases_, _14_(12), 1250-1258. Supplement to: McAllister DA, Liu L, Shi T, et al. [Global, regional, and national estimates of pneumonia morbidity and mortality in children younger than 5 years between 2000 and 2015: a systematic analysis. ](https://www.thelancet.com/cms/10.1016/S2214-109X(18)30408-X/attachment/41ca9e56-e528-4788-bb38-1d4c9f76e6d4/mmc1.pdf)Lancet Glob Health 2018; published online Nov 26. Lazzerini, M., Sonego, M., & Pellegrin, M. C. (2015). [Hypoxaemia as a mortality risk factor in acute lower respiratory infections in children in low and middle-income countries: systematic review and meta-analysis. ](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4570717/)_PLoS One_, _10_(9), e0136166. The air we breathe contains 21% of oxygen gas, but it is possible to concentrate this gas using special oxygen concentrators. The oxygen-enriched air can then be supplied to a person with pneumonia via a breathing mask, in this way compensating for reduced oxygen exchange in the lungs. World Health Organization. (2016). [Oxygen therapy for children: a manual for health workers.](https://www.who.int/maternal_child_adolescent/documents/child-oxygen-therapy/en/) World Health Organization. (2019). [WHO model list of essential medicines: 7th list](https://www.who.int/medicines/publications/essentialmedicines/en/), August 2019. Delarosa, J., Hayes, J., Pantjushenko, E., Keith, B., Ambler, G. and Lawrence, C. (2017). _[Oxygen Is Essential: A Policy and Advocacy Primer](https://path.azureedge.net/media/documents/DRG_Oxygen_Primer.pdf)_. [online] PATH. [Accessed 5 Sep. 2019]. Troeger, C., Blacker, B., Khalil, I. A., Rao, P. C., Cao, J., Zimsen, S. R., ... & Adetifa, I. M. O. (2018). [Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(18)30310-4/fulltext)_ The Lancet Infectious Diseases_, _18_(11), 1191-1210. Cohen, C., Von Mollendorf, C., De Gouveia, L., Lengana, S., Meiring, S., Quan, V., ... & Madhi, S. A. (2017). [Effectiveness of the 13-valent pneumococcal conjugate vaccine against invasive pneumococcal disease in South African children: a case-control study. ](https://www.ncbi.nlm.nih.gov/pubmed/28139443/)_The Lancet Global Health_, _5_(3), e359-e369. Lucero, M. G., Dulalia, V. E., Nillos, L. T., Williams, G., Parreño, R. A. N., Nohynek, H., ... & Makela, H. (2009). [Pneumococcal conjugate vaccines for preventing vaccine‐type invasive pneumococcal disease and X‐ray defined pneumonia in children less than two years of age.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6464899/)_Cochrane Database of Systematic Reviews_, (4). Moore, M. R., Link-Gelles, R., Schaffner, W., Lynfield, R., Holtzman, C., Harrison, L. H., ... & Thomas, A. (2016). [Effectiveness of 13-valent pneumococcal conjugate vaccine for prevention of invasive pneumococcal disease in children in the USA: a matched case-control study.](https://www.sciencedirect.com/science/article/abs/pii/S2213260016000527?via%3Dihub)_The Lancet Respiratory Medicine_, _4_(5), 399-406. Who.int. (2019) — _[Immunization coverage](https://www.who.int/news-room/fact-sheets/detail/immunization-coverage)_. [online] [Accessed 10 Sep. 2019]. [http://view-hub.org/viz/](http://view-hub.org/viz/) (Go to PCV —>  PCV - Vaccine Access —> Children without Access) Hanada, S., Pirzadeh, M., Carver, K. Y., & Deng, J. C. (2018). [Respiratory Viral Infection-Induced Microbiome Alterations and Secondary Bacterial Pneumonia.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6250824/)_Frontiers in immunology_, _9_, 2640. Song, J. Y., Nahm, M. H., & Moseley, M. A. (2013). [Clinical implications of pneumococcal serotypes: invasive disease potential, clinical presentations, and antibiotic resistance](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3546102/). _Journal of Korean medical science_, _28_(1), 4-15. The number of serotypes included in the vaccine is generally indicated in its name, e.g. PCV13 is pneumococcal conjugate vaccine effective against 13 bacterial serotypes. Vaccines including progressively more serotypes have been introduced over the years, PCV7 was introduced in 2000 and today the most commonly used PCV13 was introduced in 2010. Hausdorff, W. P., Feikin, D. R., & Klugman, K. P. (2005). [Epidemiological differences among pneumococcal serotypes.](https://www.ncbi.nlm.nih.gov/pubmed/15680778/)_The Lancet infectious diseases_, _5_(2), 83-93. The 27% refers to X-ray-defined cases of pneumonia. For clinically defined pneumonia, a less accurate diagnosis than X-ray-defined cases, the number is 6%. Both of these indicators refer to cases of pneumonia caused by any pathogen not only pneumococcus.  Lucero, M. G., Dulalia, V. E., Nillos, L. T., Williams, G., Parreño, R. A. N., Nohynek, H., ... & Makela, H. (2009). [Pneumococcal conjugate vaccines for preventing vaccine‐type invasive pneumococcal disease and X‐ray defined pneumonia in children less than two years of age](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6464899/). _Cochrane Database of Systematic Reviews_, (4). The current non-conjugate vaccine, PPSV23, is generally only given to adults or as a single dose following two immunisations with PCV13 in children older than 2. Centers for Disease Control and PRevention (2023). Pneumococcal Vaccination: Summary of Who and When to Vaccinate. Available [online](https://www.cdc.gov/vaccines/vpd/pneumo/hcp/who-when-to-vaccinate.html). Golos, M., Eliakim‐Raz, N., Stern, A., Leibovici, L., & Paul, M. (2016). [Conjugated pneumococcal vaccine versus polysaccharide pneumococcal vaccine for prevention of pneumonia and invasive pneumococcal disease in immunocompetent and immunocompromised adults and children.](https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD012306/full)_ Cochrane Database of Systematic Reviews_, (8). Wahl, B., O'Brien, K. L., Greenbaum, A., Majumder, A., Liu, L., Chu, Y., ... & Rudan, I. (2018). [Burden of Streptococcus pneumoniae and Haemophilus influenzae type b disease in children in the era of conjugate vaccines: global, regional, and national estimates for 2000–15.](https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(18)30247-X/fulltext)_ The Lancet Global Health_, _6_(7), e744-e757. Chen, C., Liceras, F. C., Flasche, S., Sidharta, S., Yoong, J., Sundaram, N., & Jit, M. (2019). [Effect and cost-effectiveness of pneumococcal conjugate vaccination: a global modelling analysis.](https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(18)30422-4/fulltext)_ The Lancet Global Health_, _7_(1), e58-e67. GAVI (Global Alliance for Vaccines and Immunisation) is a non-profit organisation that provides access to vaccination programs for low-income countries by providing financial support and individual expertise. O'Brien, K. L. (2018). [When less is more: how many doses of PCV are enough?.](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(17)30684-9/fulltext?elsca1=etoc)_ The Lancet Infectious Diseases_, _18_(2), 127-128. For example Kenya has recently entered a transition phase during which it will pay a larger and larger portion of the PCV vaccine cost. By 2027 Kenya will have to pay the full $9 price for a three-dose course child vaccination. The 2016 [per capita healthcare expenditure in Kenya](https://databank.worldbank.org/Kenya-healthcare-per-capita-/id/58f0a890) was around $66 (5% of the GDP), clearly $9 per child is not a trivial cost. Simonsen, L., van Wijhe, M., & Taylor, R. (2019). [Are expensive vaccines the best investment in low-income and middle-income countries?](https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(18)30562-X/fulltext). _The Lancet Global Health_, _7_(5), e548-e549. Ojal, J., Griffiths, U., Hammitt, L. L., Adetifa, I., Akech, D., Tabu, C., ... & Flasche, S. (2019). [Sustaining pneumococcal vaccination after transitioning from Gavi support: a modelling and cost-effectiveness study in Kenya.](https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(18)30562-X/fulltext)_ The Lancet Global Health_, _7_(5), e644-e654. The return of investment was estimated for a projected coverage for individual countries for the decade between 2011 and 2020. It means that the economic benefits (as measured by the costs of vaccination program subtracted from the reduced costs of treatment and productivity loss) of using the vaccine are 3 times higher than no vaccine use. To reduce costs, some countries may also consider switching to a two rather than three dose immunization schedule, but more research on the effectiveness of this schedule in different countries is needed. See O'Brien et al. (2018) reference. Nakamura, M. M., Tasslimi, A., Lieu, T. A., Levine, O., Knoll, M. D., Russell, L. B., & Sinha, A. (2011). [Cost effectiveness of child pneumococcal conjugate vaccination in middle-income countries.](https://academic.oup.com/inthealth/article-lookup/doi/10.1016/j.inhe.2011.08.004)_International health_, _3_(4), 270-281. Ozawa, S., Clark, S., Portnoy, A., Grewal, S., Brenzel, L., & Walker, D. G. (2016). [Return on investment from childhood immunization in low-and middle-income countries, 2011–20.](https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2015.1086?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dpubmed)_Health Affairs_, _35_(2), 199-207. Center, I. V. A. (2017). [The evidence base for pneumococcal conjugate vaccines (PCVs): data for decision-making around PCV use in childhood.](https://www.jhsph.edu/ivac/wp-content/uploads/2018/05/PCVEvidenceBase-Jan2017.pdf)_Baltimore (MD): Johns Hopkins University_. Goldblatt, D., Southern, J., Andrews, N. J., Burbidge, P., Partington, J., Roalfe, L., ... & Snape, M. D. (2018). [Pneumococcal conjugate vaccine 13 delivered as one primary and one booster dose (1+ 1) compared with two primary doses and a booster (2+ 1) in UK infants: a multicentre, parallel group randomised controlled trial.](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(17)30654-0/fulltext)_The Lancet Infectious Diseases_, _18_(2), 171-179. O'Brien, K. L. (2018). [When less is more: how many doses of PCV are enough?.](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(17)30684-9/fulltext?elsca1=etoc)_ The Lancet Infectious Diseases_, _18_(2), 127-128. Adegbola, R. A., DeAntonio, R., Hill, P. C., Roca, A., Usuf, E., Hoet, B., & Greenwood, B. M. (2014). [Carriage of Streptococcus pneumoniae and other respiratory bacterial pathogens in low and lower-middle income countries: a systematic review and meta-analysis](https://www.ncbi.nlm.nih.gov/pubmed/25084351). _PloS one_, _9_(8), e103293. Megiddo, I., Klein, E., & Laxminarayan, R. (2018). [Potential impact of introducing the pneumococcal conjugate vaccine into national immunisation programmes: an economic-epidemiological analysis using data from India.](https://gh.bmj.com/content/3/3/e000636)_ BMJ global health_, _3_(3), e000636. Johnson, H. L., Deloria-Knoll, M., Levine, O. S., Stoszek, S. K., Hance, L. F., Reithinger, R., ... & O'Brien, K. L. (2010). [Systematic evaluation of serotypes causing invasive pneumococcal disease among children under five: the pneumococcal global serotype project.](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1000348)_PLoS medicine_, _7_(10), e1000348. World Health Organization. (2010). [Changing epidemiology of pneumococcal serotypes after introduction of conjugate vaccine: July 2010 report.](https://www.who.int/wer/2010/wer8543.pdf?ua=1)_ Weekly Epidemiological Record [Relevé épidémiologique hebdomadaire_], _85_(43), 434-436. Pichichero, M. E. (2017). [Pneumococcal whole-cell and protein-based vaccines: changing the paradigm](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6277969/). _Expert review of vaccines_, _16_(12), 1181-1190. Ginsburg, A. S., Nahm, M. H., Khambaty, F. M., & Alderson, M. R. (2012). Issues and challenges in the development of pneumococcal protein vaccines. Expert review of vaccines, 11(3), 279-285 Troeger, C., Blacker, B., Khalil, I. A., Rao, P. C., Cao, J., Zimsen, S. R., ... & Adetifa, I. M. O. (2018). [Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(18)30310-4/fulltext)_The Lancet Infectious Diseases_, _18_(11), 1191-1210. WHO, U. (2006). [Air quality guidelines: global update 2005.](http://www.euro.who.int/__data/assets/pdf_file/0005/78638/E90038.pdf) p123-124. _World Health Organization_. Ferdous, F., Ahmed, S., Das, S. K., Chisti, M. J., Nasrin, D., Kotloff, K. L., ... & Wagatsuma, Y. (2018). [Pneumonia mortality and healthcare utilization in young children in rural Bangladesh: a prospective verbal autopsy study.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5970515/)_Tropical medicine and health_, _46_(1), 17. UNICEF DATA. (2018). _[Pneumonia in Children](https://data.unicef.org/topic/child-health/pneumonia/)_[.](https://data.unicef.org/topic/child-health/pneumonia/) [online] [Accessed 5 Sep. 2019] World Health Organization. (2014). _[Revised WHO classification and treatment of pneumonia in children at health facilities: quick reference guide](https://apps.who.int/iris/bitstream/handle/10665/137319/9789241507813_eng.pdf?sequence=1)_ (No. WHO/FWC/MCA/14.9). World Health Organization. Unicef.org. (2018). _[Amoxicillin Dispersible Tablets: Market and Supply Update](https://www.unicef.org/supply/files/Amoxicillin_DT_Supply_Update.pdf)_[.](https://www.unicef.org/supply/files/Amoxicillin_DT_Supply_Update.pdf) [online] [Accessed 26 Sep. 2019]. Unicef. (2016). [The State of the World's Children 2016](https://www.unicef.org/publications/files/UNICEF_SOWC_2016.pdf). _New York: United Nations Children’s Fund_. Lamberti, L. M., Zakarija-Grković, I., Walker, C. L. F., Theodoratou, E., Nair, H., Campbell, H., & Black, R. E. (2013). [Breastfeeding for reducing the risk of pneumonia morbidity and mortality in children under two: a systematic literature review and meta-analysis.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3847465/)_ BMC public health_, _13_(3), S18. 41% number is estimated by the UNICEF based on the most recent data available for the countries from surveys between 2013-2018. UNICEF DATA. (2019). _[Infant and young child feeding](https://data.unicef.org/topic/nutrition/infant-and-young-child-feeding/)_[.](https://data.unicef.org/topic/nutrition/infant-and-young-child-feeding/) [online][Accessed 4 Sep. 2019].",Pneumonia 1pB2dwxPSw-i1TWhuEJqx6n3jBTwxB9bMJQNyinB6NOg,covid-jhu-who,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""Johns Hopkins University has been at the forefront of collecting and reporting data on confirmed COVID-19 cases and deaths since the outbreak began in 2020. 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Their efforts have been invaluable in helping us understand the impact of this virus across the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""article"", ""title"": ""Our World in Data will rely on data from the WHO to track confirmed COVID-19 cases and deaths"", ""authors"": [""Edouard Mathieu"", ""Lucas Rodés-Guirao""], ""excerpt"": ""Johns Hopkins University will stop publishing data on confirmed COVID-19 cases and deaths. Our team will replace our entire time series with WHO's data on 8 March 2023."", ""subtitle"": ""Johns Hopkins University will stop publishing data on confirmed COVID-19 cases and deaths. Our team will replace our entire time series with WHO's weekly-updated data on 8 March. This change will not affect users of our charts and dataset."", ""sidebar-toc"": false, ""featured-image"": ""coronavirus.png""}",1,2024-03-10 13:18:58,2023-02-28 12:49:05,2024-03-10 13:23:50,listed,ALBJ4LtFCpK9R6zuQNrmer6lvlJ7wUagIpYKlBpX53kflQtvaFkZ1X7VhnDn9Kvb-HXUjcKB39SkQbDCCi4J2Q,,"Johns Hopkins University has been at the forefront of collecting and reporting data on confirmed COVID-19 cases and deaths since the outbreak began in 2020. However, it [recently announced](https://github.com/CSSEGISandData/COVID-19/issues/6577) that it would stop updating its data on 10 March 2023. Our team at Our World in Data recognizes the importance of up-to-date data on the pandemic. We will switch our primary source to [the World Health Organization (WHO)](https://covid19.who.int/data), which updates its dataset weekly. To keep this data consistent over time, we will replace the entire time series with WHO data on 8 March 2023. Our goal is to continue providing the most complete and timely data on the pandemic, and relying on the WHO data is the best way to achieve this. This change will be seamless for our users. The URL of all our charts and the variable names in our dataset will remain the same. [All other COVID-19 datasets](https://github.com/owid/covid-19-data/tree/master/public/data) we maintain will remain unchanged. An archive of the last version (8 March 2023) of our dataset based on data from Johns Hopkins University is available for download [here](https://covid.ourworldindata.org/data/owid-covid-data-old.csv) (CSV file, 69 Mo). We want to take this opportunity to thank the team at Johns Hopkins for their crucial work since the beginning of the pandemic. Their efforts have been invaluable in helping us understand the impact of this virus across the world.",Our World in Data will rely on data from the WHO to track confirmed COVID-19 cases and deaths 1osrh8h4zehCwQV07KQrtqPg8BG3obcKUb2tBABs1PbY,winters-have-warmed-faster-than-summers-in-the-united-states,data-insight,"{""body"": [{""size"": ""narrow"", ""type"": ""image"", ""filename"": ""season-temp-us-desktop.png"", ""hasOutline"": false, ""parseErrors"": [], ""smallFilename"": ""season-temp-us-mobile.png""}, {""type"": ""text"", ""value"": [{""text"": ""The world is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/temperature-anomaly"", ""children"": [{""text"": ""getting hotter"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" as a result of climate change, with some countries "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/temperature-anomaly"", ""children"": [{""text"": ""warming faster"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" than others. But "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""within"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" countries, warming is not equal across the year."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the United States, winters have warmed faster than any other season. This is followed by spring, with summer and fall showing the slowest rates."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The chart below shows the temperature "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""anomaly"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" — the change in seasonal temperature compared to the average over the 20th century (1901 to 2000). This data is "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.epa.gov/climate-indicators/climate-change-indicators-seasonal-temperature"", ""children"": [{""text"": ""collected and published"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" by the National Oceanic and Atmospheric Administration."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""American winters have warmed by nearly 3 degrees Fahrenheit (°F), compared to 1.5°F to 2°F in other seasons."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Minimum temperatures "", ""spanType"": ""span-simple-text""}, {""url"": ""https://science2017.globalchange.gov/chapter/6/"", ""children"": [{""text"": ""have increased faster"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" than maximum temperatures. That means nighttime temperatures have increased more than daytime temperatures."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/grapher/seasonal-temp-anomaly-us"", ""children"": [{""text"": ""Explore the data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" →"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""data-insight"", ""title"": ""Winters have warmed faster than summers in the United States"", ""authors"": [""Hannah Ritchie""], ""approved-by"": ""Ed"", ""grapher-url"": ""https://ourworldindata.org/grapher/seasonal-temp-anomaly-us""}",1,2024-04-09 14:59:28,2024-05-30 06:49:52,2024-05-08 06:13:27,unlisted,ALBJ4LvEDZUQLWZiO8-wyhotmOGO031kfVh0azGHAEKxb7dCcERKo4oQtk-VzLO0NwJ-Tg9GStZKNRBk9aYx_g,," The world is [getting hotter](https://ourworldindata.org/grapher/temperature-anomaly) as a result of climate change, with some countries [warming faster](https://ourworldindata.org/temperature-anomaly) than others. But _within_ countries, warming is not equal across the year. In the United States, winters have warmed faster than any other season. This is followed by spring, with summer and fall showing the slowest rates. The chart below shows the temperature _anomaly_ — the change in seasonal temperature compared to the average over the 20th century (1901 to 2000). This data is [collected and published](https://www.epa.gov/climate-indicators/climate-change-indicators-seasonal-temperature) by the National Oceanic and Atmospheric Administration. American winters have warmed by nearly 3 degrees Fahrenheit (°F), compared to 1.5°F to 2°F in other seasons. Minimum temperatures [have increased faster](https://science2017.globalchange.gov/chapter/6/) than maximum temperatures. That means nighttime temperatures have increased more than daytime temperatures. [Explore the data](https://ourworldindata.org/grapher/seasonal-temp-anomaly-us) →",Winters have warmed faster than summers in the United States 1olX1_PBGZOQrLO9OcpCnlb04pUKp5GpeXntFGl3OxIc,new-board-members,article,"{""toc"": [{""slug"": ""rachel-glennerster-cmg-frsa"", ""text"": ""Rachel Glennerster CMG, FRSA"", ""title"": ""Rachel Glennerster CMG, FRSA"", ""supertitle"": """", ""isSubheading"": false}, {""slug"": ""sir-andrew-william-dilnot-cbe"", ""text"": ""Sir Andrew William Dilnot CBE"", ""title"": ""Sir Andrew William Dilnot CBE"", ""supertitle"": """", ""isSubheading"": false}], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""We are excited to introduce our two newest board members, Rachel Glennerster and Sir Andrew Dilnot."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The two of them are now part of the Board of Trustees at "", ""spanType"": ""span-simple-text""}, {""url"": ""https://global-change-data-lab.org/"", ""children"": [{""text"": ""Global Change Data Lab"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", the charity that publishes and maintains Our World in Data. They will join Wendy Carlin, Hetan Shah, and Stefano Caria."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""Rachel Glennerster CMG, FRSA"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""size"": ""narrow"", ""type"": ""image"", ""filename"": ""Glennerster4.jpeg"", ""hasOutline"": false, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Rachel Glennerster is a distinguished expert in policy, research, and international development. Formerly the Chief Economist of the UK's Department for International Development (DFID), she has made significant contributions to the field, including her pivotal role in the development economics RCT revolution. As the executive director of J-PAL and a co-founder of the Deworm the World Initiative, she brings a wealth of experience at the intersection of policy and data."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Rachel's international perspective and expertise will provide invaluable insights to our board discussions. Her addition will bolster our efforts in education and health topics."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""text"": [{""text"": ""Sir Andrew William Dilnot CBE"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 2, ""parseErrors"": []}, {""size"": ""narrow"", ""type"": ""image"", ""filename"": ""s465_SirAndrewDilnot.jpeg"", ""hasOutline"": false, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Andrew Dilnot is the Warden of Nuffield College Oxford. He was chair of the UK Statistics Authority from 2012 to 2017, the founding presenter of BBC Radio 4’s program ‘More or Less’, the Principal of St Hugh’s College Oxford from 2002-2012, and the Director of the Institute for Fiscal Studies from 1991-2002."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""horizontal-rule"", ""value"": {}, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We are truly grateful that the two of them have chosen to dedicate their time and expertise to help us achieve "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/problems-and-progress"", ""children"": [{""text"": ""our mission"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""article"", ""title"": ""Welcoming Rachel Glennerster and Andrew Dilnot"", ""authors"": [""Max Roser"", ""Esteban Ortiz-Ospina""], ""excerpt"": ""We are very excited to share that Rachel Glennerster and Andrew Dilnot have joined our Board of Trustees."", ""subtitle"": """", ""hide-citation"": true}",1,2023-09-22 10:07:06,2023-10-02 17:12:55,2023-12-28 16:31:10,listed,ALBJ4LvXKf2DINnbVl0yMFMPsf1UK8u9Vx4W7Dfuk5uigaQjPxiPCSAbWxepvb6NeqURmp2hIYE9Dpgj4TLaIw,,"We are excited to introduce our two newest board members, Rachel Glennerster and Sir Andrew Dilnot. The two of them are now part of the Board of Trustees at [Global Change Data Lab](https://global-change-data-lab.org/), the charity that publishes and maintains Our World in Data. They will join Wendy Carlin, Hetan Shah, and Stefano Caria. --- ## Rachel Glennerster CMG, FRSA Rachel Glennerster is a distinguished expert in policy, research, and international development. Formerly the Chief Economist of the UK's Department for International Development (DFID), she has made significant contributions to the field, including her pivotal role in the development economics RCT revolution. As the executive director of J-PAL and a co-founder of the Deworm the World Initiative, she brings a wealth of experience at the intersection of policy and data. Rachel's international perspective and expertise will provide invaluable insights to our board discussions. Her addition will bolster our efforts in education and health topics. --- ## Sir Andrew William Dilnot CBE Andrew Dilnot is the Warden of Nuffield College Oxford. He was chair of the UK Statistics Authority from 2012 to 2017, the founding presenter of BBC Radio 4’s program ‘More or Less’, the Principal of St Hugh’s College Oxford from 2002-2012, and the Director of the Institute for Fiscal Studies from 1991-2002. --- We are truly grateful that the two of them have chosen to dedicate their time and expertise to help us achieve [our mission](https://ourworldindata.org/problems-and-progress).",Welcoming Rachel Glennerster and Andrew Dilnot 1olAVEq3u9TMJ08jiWVPp9TP-9TBiGD0zdGhDncVVzgw,life-expectancy-globally,article,"{""toc"": [], ""body"": [{""type"": ""text"", ""value"": [{""text"": ""The three maps show the global history of life expectancy over the last two centuries."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""size"": ""wide"", ""type"": ""image"", ""caption"": [{""text"": ""Life expectancy in 1800, 1950, and 2015"", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""filename"": ""3-World-maps-of-Life-expectancy-e1538651530288.png"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Demographic research suggests that at the beginning of the 19"", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""th"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}, {""text"": "" century no country in the world had a life expectancy longer than 40 years."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" Every country is shown in red. Almost everyone in the world "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/extreme-poverty"", ""children"": [{""text"": ""lived in extreme poverty"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", we had very little medical knowledge, and in all countries our ancestors had to prepare for an early death."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Over the next 150 years "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""some"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" parts of the world achieved substantial health improvements. A global divide opened. In 1950 the life expectancy for newborns was already over 60 years in Europe, North America, Oceania, Japan and parts of South America. But elsewhere a newborn could only expect to live around 30 years. The global inequality in health was enormous in 1950: People in Norway had a life expectancy of 72 years, whilst in Mali this was 26 years. Africa as a whole had an average life expectancy of only 36 years, while people in other world regions could expect to live more than twice as long."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The decline of "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/child-mortality"", ""children"": [{""text"": ""child mortality"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" was important for the increase of life expectancy, but as we explain in our "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/entries/life-expectancy"", ""children"": [{""text"": ""entry on life expectancy"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" increasing life expectancy was certainly not only about falling child mortality – "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/life-expectancy#it-is-not-only-about-child-mortality-life-expectancy-by-age"", ""children"": [{""text"": ""life expectancy increased at all ages"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Such improvements in life expectancy — despite being exclusive to particular countries — was a landmark sign of progress. It was the first time in human history that we achieved sustained improvements in health for entire populations."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" After millennia of stagnation in terrible health conditions the seal was finally broken."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Now, let’s look at the change since 1950. Many of us have not updated our world view. We still "", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.gapminder.org/factfulness-book/"", ""children"": [{""text"": ""tend to think"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" of the world as divided as it was in 1950. But in health — and "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/a-history-of-global-living-conditions-in-5-charts"", ""children"": [{""text"": ""many other aspects"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" — the world has made rapid progress. Today most people in the world can expect to live as long as those in the very richest countries in 1950. The United Nations estimate a "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/life-expectancy?time=1770..2019&country=OWID_WRL"", ""children"": [{""text"": ""global average life expectancy of 72.6 years"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" for 2019 – the global average today is higher than in any country back in 1950. According to the UN estimates the country with the best health in 1950 was Norway with a life expectancy of "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/life-expectancy?time=1770..2019&country=OWID_WRL+NOR"", ""children"": [{""text"": ""72.3 years"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The three maps summarize the global history of life expectancy over the last two centuries: Back in 1800 a newborn baby could only expect a short life, no matter where in the world it was born. In 1950 newborns had the chance of a longer life if they were lucky enough to be born in the right place. In recent decades all regions of the world made very substantial progress, and it were those regions that were worst-off in 1950 that achieved the biggest progress since then. The divided world of 1950 has been narrowing."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Globally the life expectancy increased from less than 30 years to over 72 years; after two centuries of progress we can expect to live much more than twice as long as our ancestors. And this progress was not achieved in a few places. In every world region people today can expect to live "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/grapher/life-expectancy?tab=chart&country=Africa~Americas~Asia~Europe~Oceania~OWID_WRL"", ""children"": [{""text"": ""more than twice as long"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The global inequalities in health that we see today also show that we can do much better. The almost unbelievable progress the entire world has achieved over the last two centuries should be encouragement enough for us to realize what is possible."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {""394cef035e9d0e0a1621bf9e8306f7a8e15287c8"": {""id"": ""394cef035e9d0e0a1621bf9e8306f7a8e15287c8"", ""index"": 2, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""The country-by-country estimates for 1800 come with a considerable uncertainty and to not give a false sense of certainty I have not added these estimates into the map, but the estimates for life expectancies are considerably lower than 40 years – as is also shown for the regional and global estimates so that it is safe to assume that showing a life expectancy of less than 40 years on the map is correct. The few European countries with a life expectancy close to 40 years have more accurate data for this time period so that it seems unlikely that the life expectancy there was over 40 years, or at best it was only barely over 40 years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""4e89aabfdf726a6fe6a8a27da1d4452379d71f72"": {""id"": ""4e89aabfdf726a6fe6a8a27da1d4452379d71f72"", ""index"": 1, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""Data sources. I have relied on data from our entry on life expectancy and the combination of data we produced based on Riley (2005) for regional and global averages in 1800, Gapminder for country estimates in 1800, and the United Nations Population Division for country estimates in 1950 and 2015. James Riley (2005) – Estimates of Regional and Global Life Expectancy, 1800–2001. Population and Development Review. Volume31, Issue3 September 2005 Pages 537-543. First published: 21 October 2005 "", ""spanType"": ""span-simple-text""}, {""url"": ""https://doi.org/10.1111/j.1728-4457.2005.00083.x"", ""children"": [{""text"": ""https://doi.org/10.1111/j.1728-4457.2005.00083.x"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""parseErrors"": []}], ""parseErrors"": []}, ""96910d7a4d4159dac818fa14bd2c23b615fb4953"": {""id"": ""96910d7a4d4159dac818fa14bd2c23b615fb4953"", ""index"": 0, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""To make comparisons possible they all use the same legend and the researchers that reconstructed the historical data have applied today's country borders when reporting the health of the past populations around the world."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, ""f17c99cb328e39715129cda69f45110ccfb3c4fb"": {""id"": ""f17c99cb328e39715129cda69f45110ccfb3c4fb"", ""index"": 3, ""content"": [{""type"": ""text"", ""value"": [{""text"": ""For a discussion of pre-health transition estimates of life expectancy see James Riley (2005) – Estimates of Regional and Global Life Expectancy, 1800–2001. Population and Development Review. Volume31, Issue3 September 2005 Pages 537-543. First published: 21 October 2005 https://doi.org/10.1111/j.1728-4457.2005.00083.x"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""Twice as long – life expectancy around the world"", ""authors"": [""Max Roser""], ""excerpt"": ""Life expectancy has doubled over the last two centuries around the world. How has this happened?"", ""dateline"": ""October 8, 2018"", ""subtitle"": ""Life expectancy has doubled over the last two centuries around the world. How has this happened?""}",1,2023-10-12 11:03:59,2018-10-08 11:59:30,2024-03-18 15:41:59,listed,ALBJ4LtUxYBv1b31tiilXqdnTLhLVkbVXmW-Yk8AslSVnuLXQ5Loejm8rC0GyM4GpS-161-r80YKdeEGJxuw9Q,,"The three maps show the global history of life expectancy over the last two centuries.1 Demographic research suggests that at the beginning of the 19th century no country in the world had a life expectancy longer than 40 years.3 Every country is shown in red. Almost everyone in the world [lived in extreme poverty](https://ourworldindata.org/extreme-poverty), we had very little medical knowledge, and in all countries our ancestors had to prepare for an early death. Over the next 150 years _some_ parts of the world achieved substantial health improvements. A global divide opened. In 1950 the life expectancy for newborns was already over 60 years in Europe, North America, Oceania, Japan and parts of South America. But elsewhere a newborn could only expect to live around 30 years. The global inequality in health was enormous in 1950: People in Norway had a life expectancy of 72 years, whilst in Mali this was 26 years. Africa as a whole had an average life expectancy of only 36 years, while people in other world regions could expect to live more than twice as long. The decline of [child mortality](https://ourworldindata.org/child-mortality) was important for the increase of life expectancy, but as we explain in our [entry on life expectancy](https://ourworldindata.org/entries/life-expectancy) increasing life expectancy was certainly not only about falling child mortality – [life expectancy increased at all ages](https://ourworldindata.org/life-expectancy#it-is-not-only-about-child-mortality-life-expectancy-by-age). Such improvements in life expectancy — despite being exclusive to particular countries — was a landmark sign of progress. It was the first time in human history that we achieved sustained improvements in health for entire populations.4 After millennia of stagnation in terrible health conditions the seal was finally broken. Now, let’s look at the change since 1950. Many of us have not updated our world view. We still [tend to think](https://www.gapminder.org/factfulness-book/) of the world as divided as it was in 1950. But in health — and [many other aspects](https://ourworldindata.org/a-history-of-global-living-conditions-in-5-charts) — the world has made rapid progress. Today most people in the world can expect to live as long as those in the very richest countries in 1950. The United Nations estimate a [global average life expectancy of 72.6 years](https://ourworldindata.org/grapher/life-expectancy?time=1770..2019&country=OWID_WRL) for 2019 – the global average today is higher than in any country back in 1950. According to the UN estimates the country with the best health in 1950 was Norway with a life expectancy of [72.3 years](https://ourworldindata.org/grapher/life-expectancy?time=1770..2019&country=OWID_WRL+NOR). The three maps summarize the global history of life expectancy over the last two centuries: Back in 1800 a newborn baby could only expect a short life, no matter where in the world it was born. In 1950 newborns had the chance of a longer life if they were lucky enough to be born in the right place. In recent decades all regions of the world made very substantial progress, and it were those regions that were worst-off in 1950 that achieved the biggest progress since then. The divided world of 1950 has been narrowing. Globally the life expectancy increased from less than 30 years to over 72 years; after two centuries of progress we can expect to live much more than twice as long as our ancestors. And this progress was not achieved in a few places. In every world region people today can expect to live [more than twice as long](https://ourworldindata.org/grapher/life-expectancy?tab=chart&country=Africa~Americas~Asia~Europe~Oceania~OWID_WRL). The global inequalities in health that we see today also show that we can do much better. The almost unbelievable progress the entire world has achieved over the last two centuries should be encouragement enough for us to realize what is possible. The country-by-country estimates for 1800 come with a considerable uncertainty and to not give a false sense of certainty I have not added these estimates into the map, but the estimates for life expectancies are considerably lower than 40 years – as is also shown for the regional and global estimates so that it is safe to assume that showing a life expectancy of less than 40 years on the map is correct. The few European countries with a life expectancy close to 40 years have more accurate data for this time period so that it seems unlikely that the life expectancy there was over 40 years, or at best it was only barely over 40 years. Data sources. I have relied on data from our entry on life expectancy and the combination of data we produced based on Riley (2005) for regional and global averages in 1800, Gapminder for country estimates in 1800, and the United Nations Population Division for country estimates in 1950 and 2015. James Riley (2005) – Estimates of Regional and Global Life Expectancy, 1800–2001. Population and Development Review. Volume31, Issue3 September 2005 Pages 537-543. First published: 21 October 2005 [https://doi.org/10.1111/j.1728-4457.2005.00083.x](https://doi.org/10.1111/j.1728-4457.2005.00083.x) To make comparisons possible they all use the same legend and the researchers that reconstructed the historical data have applied today's country borders when reporting the health of the past populations around the world. For a discussion of pre-health transition estimates of life expectancy see James Riley (2005) – Estimates of Regional and Global Life Expectancy, 1800–2001. Population and Development Review. Volume31, Issue3 September 2005 Pages 537-543. First published: 21 October 2005 https://doi.org/10.1111/j.1728-4457.2005.00083.x",Twice as long – life expectancy around the world 1oiiJ1mUXrkXXGcbtTlcJcw1LxBzJ-tc2YDRBmbSnP4g,differences-in-life-expectancy-across-the-world-are-extremely-large,data-insight,"{""body"": [{""size"": ""narrow"", ""type"": ""image"", ""filename"": ""life-expectancy-desktop2.png"", ""parseErrors"": [], ""smallFilename"": ""life-expectancy-mobile2.png""}, {""type"": ""text"", ""value"": [{""text"": ""People in richer countries tend to live much longer than those in poorer countries."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We can see this in the cross-country life expectancy statistics shown on the chart. In Japan, life expectancy at birth is about 85 years, while in Chad and Nigeria, life expectancy is about 52 years — a gap of over three decades."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""url"": ""https://ourworldindata.org/grapher/life-expectancy?time=latest&country=JPN~CHN~USA~OWID_WRL~IND~ETH~NGA~TCD"", ""children"": [{""text"": ""Explore this data"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" →"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""refs"": {""errors"": [], ""definitions"": {}}, ""type"": ""data-insight"", ""title"": ""Differences in life expectancy across the world are extremely large"", ""authors"": [""Esteban Ortiz-Ospina""], ""approved-by"": ""Ed"", ""grapher-url"": ""https://ourworldindata.org/grapher/life-expectancy?tab=map""}",1,2023-12-28 18:04:29,2024-03-04 07:55:33,2024-03-06 09:09:16,unlisted,ALBJ4Lsf08gsBDU3Z47Ph937LPhJd3L4vMSFqjQX-NEoMJDHihfBSXoKIfw5eaMDKBXSbW3dGKI6RxwlKKPByA,," People in richer countries tend to live much longer than those in poorer countries. We can see this in the cross-country life expectancy statistics shown on the chart. In Japan, life expectancy at birth is about 85 years, while in Chad and Nigeria, life expectancy is about 52 years — a gap of over three decades. [Explore this data](https://ourworldindata.org/grapher/life-expectancy?time=latest&country=JPN~CHN~USA~OWID_WRL~IND~ETH~NGA~TCD) →",Differences in life expectancy across the world are extremely large 1oh0rTB9nZPq00zIJVaHzGC87AiJcjsV8DmYI3eDXFr8,battery-price-decline,article,"{""toc"": [], ""body"": [{""text"": [{""type"": ""text"", ""value"": [{""text"": ""To reduce global greenhouse gas emissions we need to shift towards a low-carbon energy system. Large reductions in the cost of renewable technologies such as solar and wind have made them cost-competitive with fossil fuels. But to balance these intermittent sources and electrify our transport systems, we also need "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""low-cost"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" energy storage. Lithium-ion batteries are the most commonly used."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Lithium-ion battery cells have also seen an impressive price reduction. Since 1991, prices have fallen by around 97%. Prices fall by an average of 19% for every doubling of capacity. Even more promising is that this rate of reduction does not yet appear to be slowing down."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""callout"", ""title"": ""Summary"", ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""To reduce emissions, the world needs to rapidly transition towards a low-carbon energy system. Around three-quarters of "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/ghg-emissions-by-sector"", ""children"": [{""text"": ""global greenhouse gas emissions"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" come from energy and industry."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""One of the barriers to this energy transition has been the relative cost of different energy sources. Fossil fuels were cheaper than renewables and therefore became the dominating sources of energy."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Thankfully this is changing quickly. As my colleague Max Roser showed in "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/cheap-renewables-growth"", ""children"": [{""text"": ""this article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", the cost of renewable technologies has plummeted. They’re now cost-competitive or cheaper than new fossil fuels. In 2009, it was more than three times as expensive as coal. Now the script has flipped, and a new solar plant is almost three times cheaper than a new coal one. The price of electricity from solar declined by 89% between 2009 and 2019."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""But the cost of electricity technologies themselves is only part of what matters for this transition. One of the challenges that renewables face is that they produce energy intermittently. The sun doesn’t always shine, and the wind doesn’t always blow, so we don’t get a steady flow of generation throughout the day."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-1"", ""children"": [{""children"": [{""text"": ""1"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" An obvious solution is to store excess energy and then release it later. But to do so, we need lots of energy storage and this adds large costs to our energy system."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-2"", ""children"": [{""children"": [{""text"": ""2"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The promising news is that these technologies have seen similarly impressive price declines as solar panels have."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""The price of lithium-ion batteries has declined by 97% since 1991"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""There are several ways to store excess energy. Most of us think of batteries. Here we’re going to look at lithium-ion batteries: the most common type. Lithium-ion batteries are used in everything, ranging from your mobile phone and laptop to electric vehicles and grid storage."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-3"", ""children"": [{""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The price of lithium-ion battery cells declined by 97% in the last three decades. A battery with a capacity of one kilowatt-hour that cost $7500 in 1991 was just $181 in 2018. That’s 41 times less. What’s promising is that prices are still falling steeply: the cost halved between 2014 and 2018. A halving in only four years."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We see this decline in the chart, which shows the average price trend of lithium-ion cells from 1991 through to 2018."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-4"", ""children"": [{""children"": [{""text"": ""4"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This is shown on a logarithmic axis and measured in 2018 US dollars per kilowatt-hour."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-5"", ""children"": [{""children"": [{""text"": ""5"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This data comes from the work of Micah Ziegler and Jessika Trancik, who constructed a global database tracking lithium-ion cell prices, installed capacity, and other metrics such as energy density over time."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-6"", ""children"": [{""children"": [{""text"": ""6"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" This database combines data from 90 series that describe how lithium-ion technologies have changed from 1990 onwards. "", ""spanType"": ""span-simple-text""}, {""url"": ""https://pubs.rsc.org/en/content/articlelanding/2021/ee/d0ee02681f"", ""children"": [{""text"": ""The full article"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" includes many more results and the authors’ discussion of their relevance, as well as the methodology behind this work. Additionally, you can also access "", ""spanType"": ""span-simple-text""}, {""url"": ""https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/9FEJ7C"", ""children"": [{""text"": ""the associated data series"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": ""."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Let’s put this price decline in perspective:"", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""The popular "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ev-database.uk/car/1106/Nissan-Leaf"", ""children"": [{""text"": ""Nissan Leaf"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" electric car – which is also one of the most affordable models – has a 40 kWh battery. At our 2018 price, the battery costs around $7,300. Imagine trying to buy the same model in 1991: the battery alone would cost $300,000."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""list"", ""items"": [{""type"": ""text"", ""value"": [{""text"": ""Or take the "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ev-database.uk/car/1070/Tesla-Model-S-75D"", ""children"": [{""text"": ""Tesla Model S 75D"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "", which has a 75 kWh battery. In 2018 the battery costs around $13,600; in 1991, it would have been $564,000. More than half a million dollars for a car battery."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""This shows how important these price reductions are for decarbonizing not only our electricity grids but our transport systems too."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""["", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""If you’re interested in the prices of other vehicle models, the "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""children"": [{""url"": ""https://www.carboncounter.com/#!/details"", ""children"": [{""text"": ""Carbon Counter tool"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}], ""spanType"": ""span-italic""}, {""children"": [{""text"": "" lets you see the cost and greenhouse gas breakdowns for others"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""]."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""type"": ""sticky-right"", ""right"": [{""alt"": """", ""size"": ""wide"", ""type"": ""image"", ""filename"": ""Battery-cost-decline.png"", ""parseErrors"": []}, {""url"": ""https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/9FEJ7C"", ""type"": ""prominent-link"", ""title"": ""Access the underlying database on battery costs and performance indicators"", ""description"": """", ""parseErrors"": []}, {""url"": ""https://pubs.rsc.org/en/content/articlelanding/2021/ee/d0ee02681f#!divAbstract"", ""type"": ""prominent-link"", ""title"": ""Re-examining rates of lithium-ion battery technology improvement and cost decline"", ""description"": ""Read the peer-reviewed article behind this work (open-access)."", ""parseErrors"": []}], ""parseErrors"": []}, {""left"": [{""text"": [{""text"": ""Smaller and lighter: deployment and technological improvements are making batteries cheaper and cheaper"", ""spanType"": ""span-simple-text""}], ""type"": ""heading"", ""level"": 1, ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""We often look at these price reductions relative to time. But, of course, it’s not time itself that drives these reductions. Innovations in the production of these batteries make it possible to produce them at lower and lower costs. As production increases, there are more opportunities and incentives to achieve such innovations: that’s why prices often fall when technologies begin to scale [Max’s post "", ""spanType"": ""span-simple-text""}, {""url"": ""https://ourworldindata.org/cheap-renewables-growth"", ""children"": [{""text"": ""looks at this mechanism"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "" – called "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Wright’s Law"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "" – in detail]."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In the chart, we see the relationship between prices and cumulative installed capacity of batteries. Both are shown on logarithmic axes."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""In 1991 the market size of lithium-ion cells was tiny: there were just 0.13 megawatts (MWh) installed. That’s just 130 kWh – less than two 75 kWh battery packs that you’d find in a Tesla car. Since then, deployed capacity has increased rapidly. By 2016, this had grown to 78,000 MWh. That’s six orders of magnitude higher."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""The relationship between price and cumulative installed capacity is called the ‘learning curve’. This is a concept that is often used to understand cost improvements in scaling technologies. The learning rate tells us, on average, how much the price of something falls for every doubling of cumulative capacity. We find that for lithium-ion cells, this learning rate was 20.1%. 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That’s a 3.4-fold increase."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""What this means is that batteries have been getting smaller and lighter for any given electrical capacity. You might have noticed this yourself as your mobile phones were getting lighter and slimmer. This is a crucial technological improvement as one of the major drawbacks of some battery technologies is that they are heavy, and this limits their use in a number of technologies that are still fossil fuel powered. Imagine trying to fly an electric plane full of heavy batteries. In fact, the size and weight of batteries that you’d need to power large aircraft is one the biggest barriers to a transition to electrified aviation."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-7"", ""children"": [{""children"": [{""text"": ""7"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" The same is true for shipping or trucks: bigger and heavier batteries just make everything more costly in energy terms."", ""spanType"": ""span-simple-text""}, {""url"": ""#note-8"", ""children"": [{""children"": [{""text"": ""8"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-superscript""}], ""spanType"": ""span-ref""}, {""text"": "" You need lots of large batteries, which take up space and add weight to carry around."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}, {""type"": ""text"", ""value"": [{""text"": ""Our batteries are now only a fraction of the cost "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""and "", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""are smaller and lighter. These technological improvements are just as essential to making low-carbon electricity the default affordable option as reductions in the cost of solar panels or wind turbines. 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"", ""spanType"": ""span-simple-text""}, {""url"": ""https://www.cell.com/joule/fulltext/S2542-4351(19)30300-9"", ""children"": [{""text"": ""Storage requirements and costs of shaping renewable energy toward grid decarbonization"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-link""}, {""text"": "". "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""Joule"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": "", "", ""spanType"": ""span-simple-text""}, {""children"": [{""text"": ""3"", ""spanType"": ""span-simple-text""}], ""spanType"": ""span-italic""}, {""text"": ""(9), 2134-2153."", ""spanType"": ""span-simple-text""}], ""parseErrors"": []}], ""parseErrors"": []}}}, ""type"": ""article"", ""title"": ""The price of batteries has declined by 97% in the last three decades"", ""authors"": [""Hannah Ritchie""], ""excerpt"": ""To transition towards low-carbon energy systems, we need low-cost energy storage. Battery costs have been falling quickly."", ""dateline"": ""June 4, 2021"", ""subtitle"": ""To transition towards low-carbon energy systems, we need low-cost energy storage. Battery costs have been falling quickly."", ""sidebar-toc"": false, ""featured-image"": ""battery-thumbnail-01.png""}",1,2024-02-27 18:50:04,2021-06-04 10:00:00,2024-02-27 19:18:38,listed,ALBJ4LtvIPqCoxRgjAWa8WhYR65_YQtCYCNvEJGRyo-Z-A9mQfzsroPE5RExcBE1uvvIsizVN2TtxDvt1jyqpQ,," To reduce emissions, the world needs to rapidly transition towards a low-carbon energy system. Around three-quarters of [global greenhouse gas emissions](https://ourworldindata.org/ghg-emissions-by-sector) come from energy and industry. One of the barriers to this energy transition has been the relative cost of different energy sources. Fossil fuels were cheaper than renewables and therefore became the dominating sources of energy. Thankfully this is changing quickly. As my colleague Max Roser showed in [this article](https://ourworldindata.org/cheap-renewables-growth), the cost of renewable technologies has plummeted. They’re now cost-competitive or cheaper than new fossil fuels. In 2009, it was more than three times as expensive as coal. Now the script has flipped, and a new solar plant is almost three times cheaper than a new coal one. The price of electricity from solar declined by 89% between 2009 and 2019. But the cost of electricity technologies themselves is only part of what matters for this transition. One of the challenges that renewables face is that they produce energy intermittently. The sun doesn’t always shine, and the wind doesn’t always blow, so we don’t get a steady flow of generation throughout the day.1 An obvious solution is to store excess energy and then release it later. But to do so, we need lots of energy storage and this adds large costs to our energy system.2 The promising news is that these technologies have seen similarly impressive price declines as solar panels have. # The price of lithium-ion batteries has declined by 97% since 1991 There are several ways to store excess energy. Most of us think of batteries. Here we’re going to look at lithium-ion batteries: the most common type. Lithium-ion batteries are used in everything, ranging from your mobile phone and laptop to electric vehicles and grid storage.3 The price of lithium-ion battery cells declined by 97% in the last three decades. A battery with a capacity of one kilowatt-hour that cost $7500 in 1991 was just $181 in 2018. That’s 41 times less. What’s promising is that prices are still falling steeply: the cost halved between 2014 and 2018. A halving in only four years. We see this decline in the chart, which shows the average price trend of lithium-ion cells from 1991 through to 2018.4 This is shown on a logarithmic axis and measured in 2018 US dollars per kilowatt-hour.5 This data comes from the work of Micah Ziegler and Jessika Trancik, who constructed a global database tracking lithium-ion cell prices, installed capacity, and other metrics such as energy density over time.6 This database combines data from 90 series that describe how lithium-ion technologies have changed from 1990 onwards. [The full article](https://pubs.rsc.org/en/content/articlelanding/2021/ee/d0ee02681f) includes many more results and the authors’ discussion of their relevance, as well as the methodology behind this work. Additionally, you can also access [the associated data series](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/9FEJ7C). Let’s put this price decline in perspective: * The popular [Nissan Leaf](https://ev-database.uk/car/1106/Nissan-Leaf) electric car – which is also one of the most affordable models – has a 40 kWh battery. At our 2018 price, the battery costs around $7,300. Imagine trying to buy the same model in 1991: the battery alone would cost $300,000. * Or take the [Tesla Model S 75D](https://ev-database.uk/car/1070/Tesla-Model-S-75D), which has a 75 kWh battery. In 2018 the battery costs around $13,600; in 1991, it would have been $564,000. More than half a million dollars for a car battery. This shows how important these price reductions are for decarbonizing not only our electricity grids but our transport systems too. [_If you’re interested in the prices of other vehicle models, the __[Carbon Counter tool](https://www.carboncounter.com/#!/details)__ lets you see the cost and greenhouse gas breakdowns for others_]. ### Access the underlying database on battery costs and performance indicators https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/9FEJ7C ### Re-examining rates of lithium-ion battery technology improvement and cost decline Read the peer-reviewed article behind this work (open-access). https://pubs.rsc.org/en/content/articlelanding/2021/ee/d0ee02681f#!divAbstract # Smaller and lighter: deployment and technological improvements are making batteries cheaper and cheaper We often look at these price reductions relative to time. But, of course, it’s not time itself that drives these reductions. Innovations in the production of these batteries make it possible to produce them at lower and lower costs. As production increases, there are more opportunities and incentives to achieve such innovations: that’s why prices often fall when technologies begin to scale [Max’s post [looks at this mechanism](https://ourworldindata.org/cheap-renewables-growth) – called _Wright’s Law_ – in detail]. In the chart, we see the relationship between prices and cumulative installed capacity of batteries. Both are shown on logarithmic axes. In 1991 the market size of lithium-ion cells was tiny: there were just 0.13 megawatts (MWh) installed. That’s just 130 kWh – less than two 75 kWh battery packs that you’d find in a Tesla car. Since then, deployed capacity has increased rapidly. By 2016, this had grown to 78,000 MWh. That’s six orders of magnitude higher. The relationship between price and cumulative installed capacity is called the ‘learning curve’. This is a concept that is often used to understand cost improvements in scaling technologies. The learning rate tells us, on average, how much the price of something falls for every doubling of cumulative capacity. We find that for lithium-ion cells, this learning rate was 20.1%. This means prices fell an average of 18.9% every time the installed capacity doubled. As it happens, this is similar to the [learning rate of solar modules](https://ourworldindata.org/cheap-renewables-growth); with every doubling of installed solar capacity, the price of solar modules dropped by an average of 20.2%. The improvements we’ve seen in battery technologies are not limited to lower costs. As Ziegler and Trancik show, the energy density of cells has also been increasing. Energy density measures the amount of electrical energy you can store in a liter (or unit) of battery. In 1991 you could only get 200 watt-hours (Wh) of capacity per liter of battery. You can now get over 700 Wh. That’s a 3.4-fold increase. What this means is that batteries have been getting smaller and lighter for any given electrical capacity. You might have noticed this yourself as your mobile phones were getting lighter and slimmer. This is a crucial technological improvement as one of the major drawbacks of some battery technologies is that they are heavy, and this limits their use in a number of technologies that are still fossil fuel powered. Imagine trying to fly an electric plane full of heavy batteries. In fact, the size and weight of batteries that you’d need to power large aircraft is one the biggest barriers to a transition to electrified aviation.7 The same is true for shipping or trucks: bigger and heavier batteries just make everything more costly in energy terms.8 You need lots of large batteries, which take up space and add weight to carry around. Our batteries are now only a fraction of the cost _and _are smaller and lighter. These technological improvements are just as essential to making low-carbon electricity the default affordable option as reductions in the cost of solar panels or wind turbines. But there is still a lot to do if we want to fly in electric airliners or have our goods transported across the oceans in electric ships any time soon. ### Access the underlying database on battery costs and performance indicators https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/9FEJ7C --- # Explore more of our work at Our World in Data ### The argument for a carbon price https://ourworldindata.org/carbon-price Braff, W. A., Mueller, J. M., & Trancik, J. E. (2016). [Value of storage technologies for wind and solar energy](https://www.nature.com/articles/NCLIMATE3045). _Nature Climate Change_, 6(10), 964-969. Ziegler, M. S., Mueller, J. M., Pereira, G. D., Song, J., Ferrara, M., Chiang, Y. M., & Trancik, J. E. (2019). [Storage requirements and costs of shaping renewable energy toward grid decarbonization](https://www.cell.com/joule/fulltext/S2542-4351(19)30300-9). _Joule_, _3_(9), 2134-2153. Schmidt, O., Hawkes, A., Gambhir, A., & Staffell, I. (2017). [The future cost of electrical energy storage based on experience rates](https://www.nature.com/articles/nenergy2017110). _Nature Energy_, 2(8), 1-8. IRENA (2019), [Innovation landscape brief: Utility-scale batteries](https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Sep/IRENA_Utility-scale-batteries_2019.pdf), _International Renewable Energy Agency_, Abu Dhabi. Lithium-ion cells can be manufactured in different shapes, such as cylindrical, prismatic, or pouch. Cylindrical cells were the earliest to become commercially available. Other cells were introduced later; the average across all lithium-ion cells is shown in the chart. This means it is adjusted for inflation. Ziegler, M. S., & Trancik, J. E. (2021). [Re-examining rates of lithium-ion battery technology improvement and cost decline](https://pubs.rsc.org/en/content/articlelanding/2021/ee/d0ee02681f). _Energy & Environmental Science_, 14(4), 1635-1651. Davis, S. J., Lewis, N. S., Shaner, M., Aggarwal, S., Arent, D., Azevedo, I. L., ... & Caldeira, K. (2018). [Net-zero emissions energy systems](https://science.sciencemag.org/content/360/6396/eaas9793). _Science_, 360(6396). Finger, D. F., Braun, C., & Bil, C. (2020). [Impact of battery performance on the initial sizing of hybrid-electric general aviation aircraft](https://ascelibrary.org/doi/10.1061/%28ASCE%29AS.1943-5525.0001113). _Journal of Aerospace Engineering_, 33(3), 04020007. Gray, N., McDonagh, S., O'Shea, R., Smyth, B., & Murphy, J. D. (2021). 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Read more about what’s new."", ""subtitle"": ""Our new search tools unlock thousands of indicators. Read more about what’s new."", ""hide-citation"": true, ""featured-image"": ""thumbnail-search-improvements.png""}",1,2024-04-16 09:17:07,2024-04-24 05:55:27,2024-04-23 12:14:24,unlisted,ALBJ4LuacHLmdguUvsaXtP-Jpt4lF8_oMtal6dwb3FkurW6JJmvuWLjINbek8-BlZsgH5S7L4xB9STRwKGmHMQ,,"On _Our World in Data_, you can find thousands of indicators and hundreds of articles covering more than [100 topics](https://ourworldindata.org/#all-topics) on the pressing problems the world faces. This range of content makes the ability to _search_ across our site particularly important. We recently released some improvements to our search tools that will make it easier for people to explore the range of our work and quickly find what they are looking for. There are two main changes. # 1) Finding data for particular countries When you include one or more country names in a query, our search now looks for data that includes those countries and shows you an instant preview within our charts. For example, searching for “[Life expectancy Nigeria India Australia](https://ourworldindata.org/search?q=life%20expectancy%20Nigeria%20India%20Australia)” shows you a preview of our top chart results for “Life expectancy” with those countries already displayed. Clicking “Show more” takes you to the Charts tab, where you can find all relevant chart results. Clicking on any result takes you to an interactive visualization with those countries selected.