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{"type": "text", "value": "The Gini coefficient is a measure of inequality that ranges between 0 and 1, where higher values indicate higher inequality.", "parseErrors": []}

{"type": "text", "value": "Depending on the country and year, the data relates to either disposable income or consumption per capita.", "parseErrors": []}

{"type": "text", "value": "Non-market sources of income, including food grown by subsistence farmers for their own consumption, are taken into account", "parseErrors": []}

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{"text": "faqs", "type": "heading", "level": 1, "parseErrors": []}

{"text": "How comparable is the World Bank data on household incomes across time or between countries?", "type": "heading", "level": 3, "parseErrors": []}

{"type": "text", "value": "Because there is no global survey of incomes, researchers need to rely on available national surveys. Such surveys are designed with cross-country comparability in mind, but because the surveys reflect the circumstances and priorities of individual countries at the time of the survey, there are some important differences. In collating this survey data the World Bank takes steps to harmonize it where possible, but comparability issues remain.", "parseErrors": []}

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{"type": "text", "value": "One important issue is that, whilst in most high-income countries the surveys capture people’s incomes, in poorer countries these surveys tend to capture people’s consumption.", "parseErrors": []}

{"type": "text", "value": "Pooling the data available from different kinds of survey data is unavoidable if we want to get a global picture of poverty or inequality. But it’s important to bear in mind that, depending on the country or year, somewhat different things are being measured.", "parseErrors": []}

{"type": "text", "value": "The two concepts are nevertheless closely related: the income of a household equals their consumption plus any saving, or minus any borrowing or spending out of savings.", "parseErrors": []}

{"type": "text", "value": "One important difference is that, while zero consumption is not a feasible value – people must consume something to survive – a zero income is a feasible value. At the bottom end of the distribution, people’s consumption may be somewhat higher than their income. A common example here is retired people who are using their savings: they may have a very low, or even zero, income, but still have a high level of consumption.", "parseErrors": []}

{"type": "text", "value": "Conversely, at the top end of the distribution, consumption is typically lower than income. The gap rises with income, with households generally saving a higher share of their income the richer they are. For both these reasons, the distribution of consumption is generally more equal than the distribution of income.", "parseErrors": []}

{"type": "text", "value": "There are a number of other ways in which comparability across surveys can be limited. In collating this survey data the World Bank takes a range of steps to harmonize it where possible, but comparability issues remain. The PIP Methodology Handbook provides a good summary of the comparability and data quality issues affecting this data and how it tries to address them.", "parseErrors": []}

{"type": "text", "value": "To help communicate this limitation of the data, the World Bank produces a companion indicator that groups data points within each individual 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. The breaks between these comparable spells are shown in the chart below for the share of population living in extreme poverty. You can select to see these breaks for any indicator in our Data Explorer of the World Bank data. These spells are also indicated in our data download of the World Bank poverty and inequality data.", "parseErrors": []}

{"url": "https://ourworldindata.org/explorers/poverty-explorer?hideControls=true&Metric=Share+in+poverty&Poverty+line=%242.15+per+day%3A+International+Poverty+Line&Household+survey+data+type=Show+data+from+both+income+and+consumption+surveys&Show+breaks+between+less+comparable+surveys=true&country=BGD~BRA~CHN~KEN", "type": "chart", "parseErrors": []}

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{"type": "text", "value": "For a small number of country-year observations, the World Bank PIP data contains two estimates: one based on income data and one based on consumption data. In these cases we keep only the consumption estimate in order to obtain a single series for each country.", "parseErrors": []}

{"type": "text", "value": "You can find the data with all available income and consumption data points, including these overlapping estimates, in this dataset.", "parseErrors": []}

{"text": "sourceDescription1", "type": "heading", "level": 1, "parseErrors": []}

{"type": "text", "value": "The Poverty and Inequality Platform (PIP) is an interactive computational tool that offers users quick access to the World Bank’s estimates of poverty, inequality, and shared prosperity. PIP provides a comprehensive view of global, regional, and country-level trends for more than 160 economies around the world.", "parseErrors": []}

{"type": "text", "value": "[Text from PIP website]", "parseErrors": []}

{"type": "text", "value": "Version 20220909_2017_01_02_PROD", "parseErrors": []}

{"type": "topic-page-intro", "content": [{"type": "text", "value": "Disasters – from earthquakes and storms to floods and droughts – kill approximately 40,000 to 50,000 people per year. This is the average over the last few decades.", "parseErrors": []}, {"type": "text", "value": "While that’s a relatively small fraction of all deaths globally, disasters can have much larger impacts on specific populations. Single extreme events can kill tens to hundreds of thousands of people. In the 20th century, more than a million deaths per year were not uncommon.", "parseErrors": []}, {"type": "text", "value": "Disasters have other large impacts, too. Millions of people are displaced – some left homeless – by them each year. And the economic costs of extreme events can be severe, and hard to recover from. This is particularly true in lower-income countries.", "parseErrors": []}, {"type": "text", "value": "We are not defenceless against disasters: deaths from disasters have fallen significantly over the last century as a result of early warning systems, better infrastructure, more productive agriculture, and coordinated responses.", "parseErrors": []}, {"type": "text", "value": "As climate change increases the risks of more extreme events, making societies even more resilient will be crucial to prevent our recent progress from reversing. To do so, we need to understand how disaster events are changing, who is most vulnerable, and what can be done to protect them.", "parseErrors": []}, {"type": "text", "value": "On this page, you will find our complete collection of data, charts, and research on natural disasters and their costs.", "parseErrors": []}], "parseErrors": [], "relatedTopics": [{"url": "https://ourworldindata.org/wildfires", "text": "Wildfires", "type": "topic-page-intro-related-topic"}, {"url": "https://ourworldindata.org/climate-change", "text": "Climate Change", "type": "topic-page-intro-related-topic"}, {"url": "https://ourworldindata.org/famines", "text": "Famines", "type": "topic-page-intro-related-topic"}]}

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{"text": "Explore Data on Natural Disasters\n", "type": "heading", "level": 1, "parseErrors": []}

{"url": "https://ourworldindata.org/explorers/natural-disasters?facet=none&Disaster+Type=All+disasters&Impact=Deaths&Timespan=Decadal+average&Per+capita=false&country=~OWID_WRL&hideControls=false&tab=chart", "type": "chart", "parseErrors": []}

{"rows": [{"heading": "Articles on natural disasters", "articles": [{"value": {"url": "https://docs.google.com/document/d/12wKSUdc4v2Za_xoR8JW-9X8YRV6SQnUUtpcQ646E4uQ/edit"}}, {"value": {"url": "https://docs.google.com/document/d/1AFVSZhbg4d5pDjzyztnwG2CSBHi1UxNwVngEK6JghKo/edit"}}, {"value": {"url": "https://docs.google.com/document/d/1oFJ56FwOdBd2gs_HXPv_XsrAEgOAZDV1y4YvU1OkAMo/edit"}}, {"value": {"url": "https://docs.google.com/document/d/1hWvkhICngOD0kXBE-ERh4bUfnDD6fy5nUa_7bQs-t38/edit"}}]}], "type": "research-and-writing", "primary": [{"value": {"url": "https://docs.google.com/document/d/1zi0ZsRO1gP-IzBpCs0D6mTVcvo65Vohwyi7zZd8ZAOA/edit"}}], "secondary": [{"value": {"url": "https://docs.google.com/document/d/1xTrEatdbSxXE7TGB1wuH99PIj0fZNRSCNRiZ1-TmbNs/edit"}}, {"value": {"url": "https://docs.google.com/document/d/15sjB_Kv--BQpTknXV_Qn4G_AU2WyASscEuqNq392epU/edit"}}], "parseErrors": [], "hide-authors": false}

{"top": [], "type": "all-charts", "heading": "Interactive charts on Natural Disasters", "parseErrors": []}

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{"type": "text", "value": "Stomach cancer is one of the leading causes of cancer deaths globally. It is estimated that around one million people died from it in 2021.", "parseErrors": []}

{"type": "text", "value": "However, most are preventable, and as the chart shows, there has been a substantial decline in age-standardized death rates from stomach cancer over time.", "parseErrors": []}

{"type": "text", "value": "This can be seen in data from the World Health Organization’s Mortality Database, which has recorded causes of death in many countries since the 1950s.", "parseErrors": []}

{"type": "text", "value": "These large declines are the result of improvements in prevention and treatment. For example, screening and treatment have improved for stomach infections by H. pylori, a bacteria that causes stomach ulcers that can develop into cancer.", "parseErrors": []}

{"type": "text", "value": "Additionally, improvements in hygiene, cancer treatments, and surgery, as well as reductions in smoking, have each been important in reducing stomach cancer mortality.", "parseErrors": []}

{"type": "text", "value": "Explore this data for many more countries — we have just updated it →", "parseErrors": []}

{"type": "pill-row", "pills": [{"url": "https://ourworldindata.org/migration", "text": "Migration"}], "title": "Topics covered by Simon on Our World in Data", "parseErrors": []}

{"rows": [], "type": "research-and-writing", "latest": {}, "heading": "Featured work", "primary": [{"value": {"url": "https://docs.google.com/document/d/1v3fW6CxMgsgtDDQ0PfO-KTUfgIlkvFImFsYxAaSa-pc/edit"}}], "secondary": [], "parseErrors": [], "hide-authors": true}

{"size": "narrow", "type": "image", "filename": "daily-infant-mortality-usa-top.png", "hasOutline": false, "parseErrors": [], "smallFilename": "daily-infant-mortality-usa-top-small.png"}

{"type": "text", "value": "The world has made progress against the tragedy of infant mortality. But infants remain at risk, especially after birth.", "parseErrors": []}

{"type": "text", "value": "The experience of a baby leaving the womb and entering the world is a sudden change, and birth complications, suffocation, trauma, and sepsis can be fatal.", "parseErrors": []}

{"type": "text", "value": "The chart shows the risks over the first year of life. It uses national data from the United States Centers for Disease Control and Prevention to show the daily death rate (on the vertical axis), plotted against the baby’s age in days (on the horizontal axis).", "parseErrors": []}

{"type": "text", "value": "As you can see, the first day of life is the riskiest. Risks then decline sharply in the days following birth and continue to decline gradually over the rest of the year. This pattern is seen across countries.", "parseErrors": []}

{"type": "text", "value": "Read more about how mortality risks change over our lifetimes →", "parseErrors": []}

{"type": "text", "value": "Over their lifetimes, people are often exposed to various factors that make disease or death more likely to occur. These are known as health “risk factors”.", "parseErrors": []}

{"type": "text", "value": "To guide decisions in public health, such as how to prevent premature deaths or how to allocate public health resources, it’s crucial to know about the impacts of different risk factors.", "parseErrors": []}

{"type": "text", "value": "Risk factors that have a large effect, or are common in the population, can help us understand which interventions would make a big difference.", "parseErrors": []}

{"type": "text", "value": "But how can we measure the risk that a given factor has on the outcomes we care about?", "parseErrors": []}

{"type": "text", "value": "In this article, I’ll explain this with a simple example. I’ll focus on a hypothetical risk factor – a toxic pollutant – and show you how its impact can be calculated in different ways, and how this affects the interpretation of the underlying data.", "parseErrors": []}

{"text": "Ratios", "type": "heading", "level": 2, "parseErrors": []}

{"type": "text", "value": "One way to calculate the effect is by using ratios of the outcome between two groups.", "parseErrors": []}

{"type": "text", "value": "For example, the risk ratio is aimed at answering the question: how many times higher is the risk of the outcome among people who are exposed to the risk factor?", "parseErrors": []}

{"type": "text", "value": "In our example, the outcome is death, and the data show that people exposed to high levels of this hypothetical type of toxic pollutant were twice as likely to die within the next 20 years.", "parseErrors": []}

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{"type": "text", "value": "The opposite is the survival ratio. It answers the question: how many times higher is the chance of avoiding the outcome, among people not exposed to the risk factor?", "parseErrors": []}

{"type": "text", "value": "In our example, people only exposed to low levels of this toxic pollutant were 1.5 times as likely to survive the next 20 years.", "parseErrors": []}

{"size": "wide", "type": "image", "filename": "calculating-survival-ratios.png", "parseErrors": []}

{"type": "text", "value": "Researchers sometimes calculate the odds ratio instead of the risk ratio. The odds ratio answers the question: how many times higher were the odds of the outcome, in people exposed to the risk factor?", "parseErrors": []}

{"type": "text", "value": "As you can see, the denominator is different from the risk ratio. Rather than calculating the proportion of people who died, it compares the number of people died to those who didn’t.", "parseErrors": []}

{"type": "text", "value": "In the example, people who died had 3 times the odds of having been exposed to high levels of the toxic pollutant during the past 20 years.", "parseErrors": []}

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{"type": "text", "value": "The odds ratio is mathematically similar to the risk ratio when the outcome is rare, because A+B will be similar to B, and C+D will be similar to D. But when the outcome is common, the odds ratio and risk ratio can be very different.1", "parseErrors": []}

{"text": "Differences", "type": "heading", "level": 2, "parseErrors": []}

{"type": "text", "value": "Another way to express the increase in risk is to measure how much of a difference the risk factor makes to the outcome.", "parseErrors": []}

{"type": "text", "value": "The risk difference answers the question: how much higher is the risk of the outcome among people who are exposed to the risk factor?", "parseErrors": []}

{"type": "text", "value": "In the example, people exposed to high levels of the toxic pollutant had a 25 percentage point higher chance of dying within the next 20 years.", "parseErrors": []}

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{"type": "text", "value": "Another metric is the number needed to harm. It aims to answer the question: how many people would need to be exposed to the risk factor, to see the outcome in one of them?", "parseErrors": []}

{"type": "text", "value": "It is the inverse of the risk difference.", "parseErrors": []}

{"type": "text", "value": "In our example, four people would need to be exposed to high levels of the toxic pollutant for one to die within the next 20 years, on average.", "parseErrors": []}

{"size": "wide", "type": "image", "filename": "calculating-number-needed-to-harm.png", "parseErrors": []}

{"type": "text", "value": "By clicking ‘show more’, you can see how they compare in a full visualization.", "parseErrors": []}

{"type": "expandable-paragraph", "parseErrors": []}

{"size": "wide", "type": "image", "filename": "calculating-risk-ratios-differences-odds.png", "parseErrors": []}

{"text": "Case study: How much does smoking increase the risk of death?", "type": "heading", "level": 2, "parseErrors": []}

{"type": "text", "value": "Let’s take smoking as a real-life example.", "parseErrors": []}

{"type": "text", "value": "Researchers know from many studies that smoking increases the risk of death from cancers, heart disease, diabetes, tuberculosis, and other causes of death. But how much does it increase these risks?", "parseErrors": []}

{"type": "text", "value": "In the chart, you can see an example using data from the United States.2", "parseErrors": []}

{"type": "text", "value": "It shows how smoking increases the risk of death from various causes, using the risk ratio. It shows, for example, that male smokers have 21 times the risk of dying from lung cancer as those who have never smoked.", "parseErrors": []}

{"size": "wide", "type": "image", "filename": "smoking_risk_ratios_death-v3.png", "parseErrors": []}

{"text": "What to consider when calculating risks", "type": "heading", "level": 2, "parseErrors": []}

{"type": "text", "value": "There are several important things to remember when calculating risks from risk factors.", "parseErrors": []}

{"type": "text", "value": "First, it’s important to have good data on the causes of death from death certificates, and epidemiological research to estimate the impact of risk factors on death.", "parseErrors": []}

{"type": "text", "value": "In the example of smoking, data on smoking prevalence comes from national surveys – but people tend to underreport their smoking behaviors. This means that many smokers would be labeled as non-smokers, leading to underestimating the risk of death from smoking.", "parseErrors": []}

{"type": "text", "value": "A related point to consider is that the risks can vary between people. For example, the category ‘current smoker’ is very broad, as people have smoked for different lengths of time and amounts.", "parseErrors": []}

{"type": "text", "value": "A third point is that other confounding factors can be present. For example, smokers may also have other risk factors or behaviors that increase their risk of death. In the example above, the researchers had adjusted for other known confounding factors, but this is not always simple.", "parseErrors": []}

{"text": "Conclusion", "type": "heading", "level": 2, "parseErrors": []}

{"type": "text", "value": "Researchers can calculate the impact of a risk factor using different metrics – but they are interpreted differently.", "parseErrors": []}

{"type": "text", "value": "In this article, we calculated these metrics using a simple hypothetical example – where there were only two possible levels of exposure and two possible outcomes. Researchers can use extended statistical methods for more complex questions.3", "parseErrors": []}

{"type": "text", "value": "These estimates also have important limitations. They depend on good underlying data and study designs to assess the causal effects of risk factors.", "parseErrors": []}

{"type": "text", "value": "By estimating the effects of different risk factors, and how many deaths they cause, we can identify better ways to save lives.", "parseErrors": []}

{"type": "callout", "title": "Acknowledgements", "parseErrors": []}

{"type": "text", "value": "Edouard Mathieu, Hannah Ritchie, and Max Roser provided valuable feedback on this article.", "parseErrors": []}

{"type": "text", "value": "How old are people when they died?", "parseErrors": []}

{"type": "text", "value": "The answer can be seen in the bar chart. It shows all deaths in the world by age in five-year age groups, starting with the oldest at the top and the youngest at the bottom.", "parseErrors": []}

{"url": "https://ourworldindata.org/grapher/deaths-globally-by-age?country=~OWID_WRL", "type": "chart", "parseErrors": []}

{"type": "text", "value": "What stands out is the death toll for the very youngest age-group. 5 million children died before they had their fifth birthday. On any average day, almost 14,000 young children.", "parseErrors": []}

{"type": "text", "value": "The suffering and dying of children remains immense, yet these daily tragedies continue without receiving the attention this injustice deserves. A comparison of the tragedy of child deaths with those tragedies that do receive public attention puts it in perspective. A large jumbo jet can carry up to 620 passengers.1", "parseErrors": []}