posts: 48512
Data license: CC-BY
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| 48512 | What are the sources for Our World in Data's population estimates? | population-sources | post | publish | <!-- wp:columns --> <div class="wp-block-columns"><!-- wp:column --> <div class="wp-block-column"><!-- wp:paragraph --> <p>Population size is our most commonly used metric throughout Our World in Data. It is used directly to understand population growth over time or indirectly to calculate per-capita adjustments of the many other metrics we care about: from <a href="https://ourworldindata.org/extreme-poverty">extreme poverty</a> to <a href="https://ourworldindata.org/energy">electricity access</a>, <a href="https://ourworldindata.org/explorers/co2">CO₂ emissions</a>, and <a href="https://ourworldindata.org/coronavirus">vaccination rates</a>.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>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.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>Our team, therefore, builds and maintains a long-run dataset on population by country, region, and for the world, based on three key sources:</p> <!-- /wp:paragraph --> <!-- wp:list --> <ul><li>10,000 BCE to 1799: <a href="https://www.pbl.nl/en/image/links/hyde">HYDE version 3.2</a>.</li><li>1800 to 1949: Gapminder's <a href="https://www.gapminder.org/data/documentation/gd003/">Population version 7</a></li><li>1950 onwards: <a href="https://population.un.org/wpp/Download/Standard/Population/">UN World Population Prospects (2022)</a></li><li>For former countries: Gapminder's <a href="https://github.com/open-numbers/ddf--gapminder--systema_globalis">Systema Globalis</a></li></ul> <!-- /wp:list --> <!-- wp:paragraph --> <p>The scripts that produce this long-run dataset can be accessed <a href="https://github.com/owid/etl/tree/master/etl/steps/data/garden/demography/2023-03-31/population">in our GitHub repository</a>.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>In all sources we rely on, historical population estimates are based on today’s geographical borders.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>We provide a full citation for each of the sources below. If you cite population data for a specific period, please cite the source. For example, for the period 1950 onwards, please cite the UN World Population Prospects. You can add “via Our World in Data” if you downloaded the data from us. </p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>You can find the complete list of the sources used for each country and year <a href="https://ourworldindata.org/grapher/sources-population-dataset">here</a>.</p> <!-- /wp:paragraph --></div> <!-- /wp:column --> <!-- wp:column --> <div class="wp-block-column"><!-- wp:html --> <iframe src="https://ourworldindata.org/grapher/projected-population-by-country?tab=chart&time=earliest..2100&country=~OWID_WRL" loading="lazy" style="width: 100%; height: 600px; border: 0px none;"></iframe> <!-- /wp:html --></div> <!-- /wp:column --></div> <!-- /wp:columns --> <!-- wp:heading {"level":3} --> <h3>HYDE version 3.2</h3> <!-- /wp:heading --> <!-- wp:paragraph --> <p>The very ambitious <a href="https://www.pbl.nl/en/image/links/hyde">HYDE database</a> (History Database of the Global Environment) is maintained by researchers at the Netherlands Environmental Assessment Agency.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p><strong>Full citation</strong>: Klein Goldewijk, K., A. Beusen, J.Doelman and E. Stehfest (2017), <a href="https://www.pbl.nl/en/publications/new-anthropogenic-land-use-estimates-for-the-holocene-hyde-32">Anthropogenic land use estimates for the Holocene; HYDE 3.2</a>, Earth System Science Data, 9, 927-953.</p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>The HYDE estimates go up to 2020, but they are only available once per decade for the period 1800–2020. Therefore, from 1800 onwards, when data is available from both HYDE and Gapminder, we favor the Gapminder version 7 dataset, as it provides annual estimates.</p> <!-- /wp:paragraph --> <!-- wp:heading {"level":3} --> <h3>Gapminder</h3> <!-- /wp:heading --> <!-- wp:heading {"level":4} --> <h4>Gapminder version 7</h4> <!-- /wp:heading --> <!-- wp:paragraph --> <p><a href="https://www.gapminder.org/about/">Gapminder</a> maintains a <a href="https://www.gapminder.org/data/documentation/gd003/">population dataset</a> based on Angus Maddison and Clio Infra data. Their documentation provides the following details on their sources:</p> <!-- /wp:paragraph --> <!-- wp:quote --> <blockquote class="wp-block-quote"><p>We use Maddison population data improved by Clio Infra in April 2015 and Gapminder v3 documented in greater detail by Mattias Lindgren. The main source of v3 was <a href="https://clio-infra.eu/Indicators/TotalPopulation.html">Angus Maddison’s data</a> which is maintained and improved by the Clio Infra Project. The updated Maddison data by Clio Infra were based on the following improvements:</p><p>i. Whenever estimates by Maddison were available, his figures were followed in favor of estimates by Gapminder;</p><p>ii. For Africa, estimates by Frankema and Jerven (2014) for 1850-1960 have been added to the existing database;</p><p>iii. For Latin America, estimates by Abad & Van Zanden (2014) for 1500-1940 have been added.</p></blockquote> <!-- /wp:quote --> <!-- wp:paragraph --> <p><strong>Full citation</strong>: Gapminder doesn’t provide a preferred citation. We cite their work as: Gapminder population dataset version 7, based on data by Angus Maddison improved by Clio Infra. <a href="https://www.gapminder.org/data/documentation/gd003/">https://www.gapminder.org/data/documentation/gd003/</a></p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>We use this dataset as our source from 1800 to 1949. In addition, we use their population estimates for the Vatican until 2100 since these are missing in UN WPP’s dataset.</p> <!-- /wp:paragraph --> <!-- wp:heading {"level":4} --> <h4>Systema Globalis</h4> <!-- /wp:heading --> <!-- wp:paragraph --> <p><a href="https://github.com/open-numbers/ddf--gapminder--systema_globalis">Systema Globalis</a> is <a href="https://www.gapminder.org/about/">Gapminder</a>’s primary dataset, used in tools on their official website. </p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p><strong>Full citation</strong>: Gapminder doesn’t provide a preferred citation. We cite their work as: Gapminder Systema Globalis. <a href="https://github.com/open-numbers/ddf--gapminder--systema_globalis">https://github.com/open-numbers/ddf--gapminder--systema_globalis</a></p> <!-- /wp:paragraph --> <!-- wp:paragraph --> <p>Data from this source covers the period 1555-2008. We use it to complement our population dataset with data from former countries (e.g., the Soviet Union, Yugoslavia, etc.) and other data not present in other sources.</p> <!-- /wp:paragraph --> <!-- wp:heading {"level":3} --> <h3>UN World Population Prospects</h3> <!-- /wp:heading --> <!-- wp:paragraph --> <p>We rely on the latest <a href="https://population.un.org/wpp/Download/Standard/Population/">United Nations World Population Prospects</a> revision as our primary source for recent historical data and future projections. We use this data for its reliability, its consistent methods, and because it includes population estimates for almost all territories in the world. The UN updates its dataset every 2 years with the following:</p> <!-- /wp:paragraph --> <!-- wp:list --> <ul><li>Annual historical estimates running from 1950 to the year before the most recent dataset publication;</li><li>Annual projections running from the year of the most recent dataset publication to 2100. The UN publishes multiple projections based on different scenarios of global fertility rates: a low, medium, and high scenario. In our dataset, we use the <a href="https://population.un.org/wpp/DefinitionOfProjectionVariants/">medium-variant scenario</a>.</li></ul> <!-- /wp:list --> <!-- wp:paragraph --> <p>The United Nations estimates may not always reflect the latest censuses or national figures. However, there are several reasons why we use this data over country-by-country national population estimates:</p> <!-- /wp:paragraph --> <!-- wp:list --> <ul><li>The UNWPP dataset is the standard in research. The main reason is that it uses a reliable and standardized methodology for all countries. For example, if we used individual country data, some may include overseas workers, ex-pats, undocumented immigrants, etc., but others wouldn’t. The UNWPP dataset tries to maintain a consistent methodology across all countries.</li><li>Using data from the UN allows us to get accurate population estimates for all territories worldwide. Finding and maintaining estimates based on national censuses would be time-consuming and more prone to errors.</li><li>Other reasons include the availability of yearly data (national censuses are only conducted every few years) and avoiding double-counting in cases of border disputes.</li></ul> <!-- /wp:list --> <!-- wp:paragraph --> <p><strong>Full citation:</strong> United Nations, Department of Economic and Social Affairs, Population Division (2022). <em>World Population Prospects 2022, Online Edition. Rev. 1</em>.</p> <!-- /wp:paragraph --> <!-- wp:separator --> <hr class="wp-block-separator"/> <!-- /wp:separator --> <!-- wp:heading {"level":5} --> <h5>Keep reading at <em>Our World in Data</em></h5> <!-- /wp:heading --> <!-- wp:owid/prominent-link {"title":"","linkUrl":"https://ourworldindata.org/world-population-growth","className":"is-style-thin"} /--> | {
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2022-02-08 13:15:30 | 2024-02-16 14:22:53 | 1XrOGw_v7K1af0WVvjBIsQb9C4VesIGrjwEg6IEj-plU | [
"Edouard Mathieu",
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2022-02-08 13:15:30 | 2023-07-14 15:57:57 | {} |
Population size is our most commonly used metric throughout Our World in Data. It is used directly to understand population growth over time or indirectly to calculate per-capita adjustments of the many other metrics we care about: from [extreme poverty](https://ourworldindata.org/extreme-poverty) to [electricity access](https://ourworldindata.org/energy), [CO₂ emissions](https://ourworldindata.org/explorers/co2), and [vaccination rates](https://ourworldindata.org/coronavirus). 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, therefore, 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) 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). In all sources we rely on, historical population estimates are based on today’s geographical borders. We provide a full citation for each of the sources below. If you cite population data for a specific period, please cite the source. For example, for the period 1950 onwards, please cite the UN World Population Prospects. You can add “via Our World in Data” if you downloaded the data from us. You can find the complete list of the sources used for each country and year [here](https://ourworldindata.org/grapher/sources-population-dataset). <Chart url="https://ourworldindata.org/grapher/projected-population-by-country?tab=chart&time=earliest..2100&country=~OWID_WRL"/> ## HYDE version 3.2 The very ambitious [HYDE database](https://www.pbl.nl/en/image/links/hyde) (History Database of the Global Environment) is maintained by researchers at the Netherlands Environmental Assessment Agency. **Full citation**: Klein Goldewijk, K., A. Beusen, J.Doelman and E. Stehfest (2017), [Anthropogenic land use estimates for the Holocene; HYDE 3.2](https://www.pbl.nl/en/publications/new-anthropogenic-land-use-estimates-for-the-holocene-hyde-32), Earth System Science Data, 9, 927-953. The HYDE estimates go up to 2020, but they are only available once per decade for the period 1800–2020. Therefore, from 1800 onwards, when data is available from both HYDE and Gapminder, we favor the Gapminder version 7 dataset, as it provides annual estimates. ## Gapminder ### Gapminder version 7 [Gapminder](https://www.gapminder.org/about/) maintains a [population dataset](https://www.gapminder.org/data/documentation/gd003/) based on Angus Maddison and Clio Infra data. Their documentation provides the following details on their sources: -- undefined **Full citation**: Gapminder doesn’t provide a preferred citation. We cite their work as: Gapminder population dataset version 7, based on data by Angus Maddison improved by Clio Infra. [https://www.gapminder.org/data/documentation/gd003/](https://www.gapminder.org/data/documentation/gd003/) We use this dataset as our source from 1800 to 1949. In addition, we use their population estimates for the Vatican until 2100 since these are missing in UN WPP’s dataset. ### Systema Globalis [Systema Globalis](https://github.com/open-numbers/ddf--gapminder--systema_globalis) is [Gapminder](https://www.gapminder.org/about/)’s primary dataset, used in tools on their official website. **Full citation**: Gapminder doesn’t provide a preferred citation. We cite their work as: Gapminder Systema Globalis. [https://github.com/open-numbers/ddf--gapminder--systema_globalis](https://github.com/open-numbers/ddf--gapminder--systema_globalis) Data from this source covers the period 1555-2008. We use it to complement our population dataset with data from former countries (e.g., the Soviet Union, Yugoslavia, etc.) and other data not present in other sources. ## UN World Population Prospects We rely on the latest [United Nations World Population Prospects](https://population.un.org/wpp/Download/Standard/Population/) revision as our primary source for recent historical data and future projections. We use this data for its reliability, its consistent methods, and because it includes population estimates for almost all territories in the world. The UN updates its dataset every 2 years with the following: * Annual historical estimates running from 1950 to the year before the most recent dataset publication; * Annual projections running from the year of the most recent dataset publication to 2100. The UN publishes multiple projections based on different scenarios of global fertility rates: a low, medium, and high scenario. In our dataset, we use the [medium-variant scenario](https://population.un.org/wpp/DefinitionOfProjectionVariants/). The United Nations estimates may not always reflect the latest censuses or national figures. However, there are several reasons why we use this data over country-by-country national population estimates: * The UNWPP dataset is the standard in research. The main reason is that it uses a reliable and standardized methodology for all countries. For example, if we used individual country data, some may include overseas workers, ex-pats, undocumented immigrants, etc., but others wouldn’t. The UNWPP dataset tries to maintain a consistent methodology across all countries. * Using data from the UN allows us to get accurate population estimates for all territories worldwide. Finding and maintaining estimates based on national censuses would be time-consuming and more prone to errors. * Other reasons include the availability of yearly data (national censuses are only conducted every few years) and avoiding double-counting in cases of border disputes. **Full citation:** United Nations, Department of Economic and Social Affairs, Population Division (2022). _World Population Prospects 2022, Online Edition. Rev. 1_. #### Keep reading at _Our World in Data_ ### https://ourworldindata.org/world-population-growth | {
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"rendered": "\n<div class=\"wp-block-columns\">\n<div class=\"wp-block-column\">\n<p>Population size is our most commonly used metric throughout Our World in Data. It is used directly to understand population growth over time or indirectly to calculate per-capita adjustments of the many other metrics we care about: from <a href=\"https://ourworldindata.org/extreme-poverty\">extreme poverty</a> to <a href=\"https://ourworldindata.org/energy\">electricity access</a>, <a href=\"https://ourworldindata.org/explorers/co2\">CO\u2082 emissions</a>, and <a href=\"https://ourworldindata.org/coronavirus\">vaccination rates</a>.</p>\n\n\n\n<p>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.</p>\n\n\n\n<p>Our team, therefore, builds and maintains a long-run dataset on population by country, region, and for the world, based on three key sources:</p>\n\n\n\n<ul><li>10,000 BCE to 1799: <a href=\"https://www.pbl.nl/en/image/links/hyde\">HYDE version 3.2</a>.</li><li>1800 to 1949: Gapminder’s <a href=\"https://www.gapminder.org/data/documentation/gd003/\">Population version 7</a></li><li>1950 onwards: <a href=\"https://population.un.org/wpp/Download/Standard/Population/\">UN World Population Prospects (2022)</a></li><li>For former countries: Gapminder’s <a href=\"https://github.com/open-numbers/ddf--gapminder--systema_globalis\">Systema Globalis</a></li></ul>\n\n\n\n<p>The scripts that produce this long-run dataset can be accessed <a href=\"https://github.com/owid/etl/tree/master/etl/steps/data/garden/demography/2023-03-31/population\">in our GitHub repository</a>.</p>\n\n\n\n<p>In all sources we rely on, historical population estimates are based on today\u2019s geographical borders.</p>\n\n\n\n<p>We provide a full citation for each of the sources below. If you cite population data for a specific period, please cite the source. For example, for the period 1950 onwards, please cite the UN World Population Prospects. You can add \u201cvia Our World in Data\u201d if you downloaded the data from us. </p>\n\n\n\n<p>You can find the complete list of the sources used for each country and year <a href=\"https://ourworldindata.org/grapher/sources-population-dataset\">here</a>.</p>\n</div>\n\n\n\n<div class=\"wp-block-column\">\n<iframe src=\"https://ourworldindata.org/grapher/projected-population-by-country?tab=chart&time=earliest..2100&country=~OWID_WRL\" loading=\"lazy\" style=\"width: 100%; height: 600px; border: 0px none;\"></iframe>\n</div>\n</div>\n\n\n\n<h3>HYDE version 3.2</h3>\n\n\n\n<p>The very ambitious <a href=\"https://www.pbl.nl/en/image/links/hyde\">HYDE database</a> (History Database of the Global Environment) is maintained by researchers at the Netherlands Environmental Assessment Agency.</p>\n\n\n\n<p><strong>Full citation</strong>: Klein Goldewijk, K., A. Beusen, J.Doelman and E. Stehfest (2017), <a href=\"https://www.pbl.nl/en/publications/new-anthropogenic-land-use-estimates-for-the-holocene-hyde-32\">Anthropogenic land use estimates for the Holocene; HYDE 3.2</a>, Earth System Science Data, 9, 927-953.</p>\n\n\n\n<p>The HYDE estimates go up to 2020, but they are only available once per decade for the period 1800\u20132020. Therefore, from 1800 onwards, when data is available from both HYDE and Gapminder, we favor the Gapminder version 7 dataset, as it provides annual estimates.</p>\n\n\n\n<h3>Gapminder</h3>\n\n\n\n<h4>Gapminder version 7</h4>\n\n\n\n<p><a href=\"https://www.gapminder.org/about/\">Gapminder</a> maintains a <a href=\"https://www.gapminder.org/data/documentation/gd003/\">population dataset</a> based on Angus Maddison and Clio Infra data. Their documentation provides the following details on their sources:</p>\n\n\n\n<blockquote class=\"wp-block-quote\"><p>We use Maddison population data improved by Clio Infra in April 2015 and Gapminder v3 documented in greater detail by Mattias Lindgren. The main source of v3 was <a href=\"https://clio-infra.eu/Indicators/TotalPopulation.html\">Angus Maddison\u2019s data</a> which is maintained and improved by the Clio Infra Project. The updated Maddison data by Clio Infra were based on the following improvements:</p><p>i. Whenever estimates by Maddison were available, his figures were followed in favor of estimates by Gapminder;</p><p>ii. For Africa, estimates by Frankema and Jerven (2014) for 1850-1960 have been added to the existing database;</p><p>iii. For Latin America, estimates by Abad & Van Zanden (2014) for 1500-1940 have been added.</p></blockquote>\n\n\n\n<p><strong>Full citation</strong>: Gapminder doesn\u2019t provide a preferred citation. We cite their work as: Gapminder population dataset version 7, based on data by Angus Maddison improved by Clio Infra. <a href=\"https://www.gapminder.org/data/documentation/gd003/\">https://www.gapminder.org/data/documentation/gd003/</a></p>\n\n\n\n<p>We use this dataset as our source from 1800 to 1949. In addition, we use their population estimates for the Vatican until 2100 since these are missing in UN WPP\u2019s dataset.</p>\n\n\n\n<h4>Systema Globalis</h4>\n\n\n\n<p><a href=\"https://github.com/open-numbers/ddf--gapminder--systema_globalis\">Systema Globalis</a> is <a href=\"https://www.gapminder.org/about/\">Gapminder</a>\u2019s primary dataset, used in tools on their official website. </p>\n\n\n\n<p><strong>Full citation</strong>: Gapminder doesn\u2019t provide a preferred citation. We cite their work as: Gapminder Systema Globalis. <a href=\"https://github.com/open-numbers/ddf--gapminder--systema_globalis\">https://github.com/open-numbers/ddf–gapminder–systema_globalis</a></p>\n\n\n\n<p>Data from this source covers the period 1555-2008. We use it to complement our population dataset with data from former countries (e.g., the Soviet Union, Yugoslavia, etc.) and other data not present in other sources.</p>\n\n\n\n<h3>UN World Population Prospects</h3>\n\n\n\n<p>We rely on the latest <a href=\"https://population.un.org/wpp/Download/Standard/Population/\">United Nations World Population Prospects</a> revision as our primary source for recent historical data and future projections. We use this data for its reliability, its consistent methods, and because it includes population estimates for almost all territories in the world. The UN updates its dataset every 2 years with the following:</p>\n\n\n\n<ul><li>Annual historical estimates running from 1950 to the year before the most recent dataset publication;</li><li>Annual projections running from the year of the most recent dataset publication to 2100. The UN publishes multiple projections based on different scenarios of global fertility rates: a low, medium, and high scenario. In our dataset, we use the <a href=\"https://population.un.org/wpp/DefinitionOfProjectionVariants/\">medium-variant scenario</a>.</li></ul>\n\n\n\n<p>The United Nations estimates may not always reflect the latest censuses or national figures. However, there are several reasons why we use this data over country-by-country national population estimates:</p>\n\n\n\n<ul><li>The UNWPP dataset is the standard in research. The main reason is that it uses a reliable and standardized methodology for all countries. For example, if we used individual country data, some may include overseas workers, ex-pats, undocumented immigrants, etc., but others wouldn\u2019t. The UNWPP dataset tries to maintain a consistent methodology across all countries.</li><li>Using data from the UN allows us to get accurate population estimates for all territories worldwide. Finding and maintaining estimates based on national censuses would be time-consuming and more prone to errors.</li><li>Other reasons include the availability of yearly data (national censuses are only conducted every few years) and avoiding double-counting in cases of border disputes.</li></ul>\n\n\n\n<p><strong>Full citation:</strong> United Nations, Department of Economic and Social Affairs, Population Division (2022). <em>World Population Prospects 2022, Online Edition. Rev. 1</em>.</p>\n\n\n\n<hr class=\"wp-block-separator\"/>\n\n\n\n<h5>Keep reading at <em>Our World in Data</em></h5>\n\n\n <block type=\"prominent-link\" style=\"is-style-thin\">\n <link-url>https://ourworldindata.org/world-population-growth</link-url>\n <title></title>\n <content></content>\n <figure></figure>\n </block>",
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